The Satb1 genome organizer regulates multiple cellular and developmental processes. It is not yet clear how Satb1 selects different sets of targets throughout the genome. Here we have used live-cell single molecule imaging and deep sequencing to assess determinants of Satb1 binding-site selectivity. We have found that Satb1 preferentially targets nucleosome-dense regions and can directly bind consensus motifs within nucleosomes. Some genomic regions harbor multiple, regularly spaced Satb1 binding motifs  characterized by highly cooperative binding. The Satb1 homeodomain is dispensable for high affinity binding but is essential for specificity. Finally, we find that Satb1-DNA interactions are mechanosensitive. Increasing negative torsional stress in DNA enhances Satb1 binding and Satb1 stabilizes base unpairing regions against melting by molecular machines. The ability of Satb1 to control diverse biological programs may reflect its ability to combinatorially use multiple site selection criteria. Satb1 is a master regulator of multiple cellular processes. Here the authors find that Satb1 preferentially targets nucleosome dense regions and combinatorially uses multiple selection criteria including DNA torsion, flanking DNA shape, motif density and periodicity to streamline binding choices. The DNA backbone and major and minor groove shape constitute a second evolutionarily conserved constraint3 recognized through indirect readout4 or shape readout5 mechanisms. Recently it has been suggested that dinucleotide features are sufficient to reliably predict DNA shape parameters6. Transcription factors have also been shown to streamline their binding choices based on the shape of regions flanking the core motif7. Beyond sequence and shape, geometric constraints such as motif-to-motif spacing, orientation, and local density of binding sites influence cooperative TF binding9. Finally, TF binding is further modulated by chromatin accessibility and nucleosome occupancy11 which in turn are affected by DNA torsional stress and deformability14. Our goal was to explore how the interplay of these parameters defines the genome-wide distribution of the chromatin loopscape regulator15 Special AT-Rich Sequence Binding Protein 1 (Satb1).At the core of cellular information processing is the ability of transcription factors (TFs) to bind subsets of genomic targets selectively. Proteins with multiple DNA Binding Domains (DBD) can combinatorially engage multiple distinct core DNA consensus motifs16 transcription factor with multiple DNA binding domains, namely CUT1, CUT2 and a C-terminal homeodomain (HD). Satb1 has been implicated in diverse cellular processes including epidermal differentiation17, breast cancer metastasis18, thymocyte development19, Th2 cell activation and cytokine production20, cortical development21, X-chromosome inactivation22, and embryonic stem cell differentiation23. Satb1 has also been deemed to be a genome organizer responsible for rapid phenotypic transitions15. For instance, 3C techniques applied to T cells suggest that Satb1 mediates transcriptional and epigenetic changes at target loci20. In the same vein, Satb1 is instrumental in establishing a Foxp3+ regulatory T cell (Treg)-specific lineage by defining a Treg cell-specific super enhancer landscape24.Satb1 is a dimeric/tetramericBase Unpairing Regions: BUR)26, which have a propensity to unpair under torsional stress28. Chemical interference assays suggest that Satb1 binds along the minor groove of DNA with virtually no contact with the bases29. SELEX experiments suggest that the Satb1 homodimer binds an inverted AT-rich palindromic repeat along the minor groove29. Unlike other HD proteins, Satb1's HD does not independently bind an HD consensus sequence but appears to increase binding specificity towards BURs in vitro26. It has also been postulated that the spacing of the half-sites is critical for binding as a dimer. Finally, solution biophysical assays suggest that the sequence specificity is due to binding by CUT1 along the major groove31 but does not require an AT-rich inverted palindromic repeat31. The disparate nature of these findings could reflect over-simplified in vitro biochemical settings  or the use of a narrow subset of DNA substrates with selection bias.Despite an abundance of in vitro biochemical and biophysical data, there is no consensus on Satb1's binding mechanism(s). In vitro and affinity-based pull-down experiments indicate that Satb1 binds A/T rich motifs with high base-unpairing potential , genomics  and in vitro nucleosome binding assays. Our work shows that Satb1 binds transposase-inaccessible, nucleosome-dense regions in chromatin and contacts motifs embedded in nucleosomal core sequences, both in vitro and in vivo. Satb1 binding shows repeated binding and unbinding to a small number of spatially proximal chromatin interaction hotspots. Using deep sequencing we show that of Satb1 binding sites exist in clusters in the genome, characterized by highly cooperative binding. Satb1 prefers regions of the DNA with multiple consensus motifs spaced ~10 bp apart. The HD is crucial for site-specificity, as it helps to home in on motifs with flanks having enhanced negative propeller twist (PT) and higher AT content than the genome average. Finally, Satb1 binds more efficiently to target sites under highly negative torsional stress and stabilizes BURs against helix destabilization, as evidenced by the abolition of psoralen crosslinking from binding sites in the presence of Satb1.To elucidate how Satb1 binds its targets, we have combined live cell imaging  and to a lesser extent at the EC/HC boundary (1.7 ± 1.1 μm2 s−1). Therefore, the majority of the Satb1 molecules are highly dynamic albeit with varying ability to access different regions of the nucleus, raising the possibility that Satb1 tendrils are not static structures in their own right, but reflect cell-type specific features of the thymocyte heterochromatin.We used image correlation spectroscopy (ICS) to measure the dynamics of Satb1-eGFP in the euchromatin, as well as at the euchromatin/heterochromatin (EC/HC) boundary. Figure xel Fig. . The dif38 to localize single, chromatin-bound Satb1 molecules. To set a baseline and assess how Satb1 binds to chromatin, we ectopically expressed a C terminal eGFP fusion of Satb1 (inducible via cumate control) in the MCF10A breast epithelial cell line. MCF10A lacks native Satb1. In this experimental geometry, the Satb1-eGFP will bind a genome that is unaffected by architectural changes and epigenetic modifications resulting from chronic Satb1 exposure. Unlike thymocytes which exhibit a specialized nuclear architecture, the Satb1 distribution in the MCF10A did not show marked depletion in heterochromatin  in VL3-3M2 and MCF10A cells. Although absolute level of Satb1 expression was ~7 fold less in VL3-3M2 cells compared to MCF10A cells, concentrations of Satb1 per unit nuclear volume were nearly identical owing to much smaller nuclear volume of VL3-3M2  of these potential targets. We asked whether nucleosomes act as barriers to Satb1 binding since the ENCODE consortium has shown that most transcription factors bind to highly accessible (nucleosome-free) DNA regions41. Satb1 has been shown to have pioneer activity in Treg cells24. Satb1’s preference for inaccessible chromatin made it inherently difficult to determine nucleosome positions at target sites due to low ATAC signal  were modified to generate three Satb1 consensus sites (601 A). Recombinant Satb1 robustly bound 601 A mono-nucleosomes  and accessible or inaccessible chromatin (based on ATAC seq). Although nucleosome-free regions represent the least abundant class of Satb1 binding sites, this none the less was best suited for assessing the role of DNA shape on site selectivity, since these sites are free of structural distortions imposed by the nucleosomal histone scaffold45. The minor groove width (MGW), helical twist (HelT), and Roll varied only slightly with Satb1 occupancy, but bound motifs had higher negative propeller twist (PT) in their flanking sequences  than the unbound ones (67%)  Fig. , locatedtes Fig.  raises t28. Torsion can be generated by replicating and transcribing polymerases, nucleosome assembly, and remodeling, as well as chromatin condensation by ATP dependent Condensins47. Topoisomerases maintain a torsionally relaxed genome by relieving torsional strain in chromatin that can build up during transcription and chromatin remodeling12. To trap the genome in a torsionally strained state we inhibited topoisomerase I (Topo I) and topoisomerase II (Topo II) in MCF10A cells for a short time with Camptothecin (CPT) and ICRF-193, respectively, and we then carried out ChIP-seq of Satb1 bound chromatin.Classic Satb1 target sites such as the immunoglobulin heavy chain (IgH) enhancer region have been shown to melt over extended regions under torsional stress13 on WT MCF10A cells and MCF10A cells stably expressing Satb1-eGFP. We also collected TMP-seq data from both of these cell types upon treatment with the topoisomerase inhibitors CPT and ICRF-193. Psoralen-crosslinking efficiency at Satb1 binding sites in native MCF10A cells diminished markedly upon CPT treatment and to a much greater extent upon ICRF-193 treatment  of 10 different chromatin binding proteins revealed distinct patterns  such as CTCF, SMC1 and Cohesin-SA151 bound to sites characterized by a broader range of torsional states compared to canonical transcription factors but showed clear bias against highly under-wound DNA. Satb1 showed a visibly sharper choice for torsional states. Unlike the other CAPs, Satb1 demonstrated almost no binding to sites with TMP signal lower than the genome average and preferred slightly under-wound DNA, with binding strength decaying sharply with increase in TMP crosslinking.Since torsional stress regulates chromatin fine structurerns Fig. . Fos, Ju52, it is plausible that the Satb1 HD might play a role in conferring specificity through the recognition of specific DNA shape parameters. To address these questions, we compared full-length Satb1 (FL) to three domain truncation mutants, one with the N terminal dimerization domain but no DNA interaction domains (N), one with the N terminus and CUT1 (N-C1), and one lacking only the HD (∆HD)  DNA interaction domains  all contribute to affinity and specificity; alternatively, affinity and specificity could arise out of the modular organization of Satb1 where some domains could provide affinity and others would provide specificity. Since several HD family transcription factors have been shown to bind to sequence environments with enhanced negative propeller twistHD) Fig. . We used53 was used to determine the protein's affinities to chromatin . For each construct, we determined the distribution of dwell times  than for N  frequently occur periodically at helix/histone interaction sites along the length of a nucleosome58. Interestingly, Satb1 binding sites also show ~10 bp periodic spacing and Satb1 preferentially targets closed chromatin by directly accessing nucleosome embedded motifs in vivo and in vitro. Indeed, enhancement in Satb1 binding efficiency upon treatment with CPT and ICRF-193 is consistent with a nucleosome mediated cooperativity model59, as treatment with topoisomerase inhibitors would destabilize nucleosomes, facilitating partial unwrapping. Torsional stress sensing and preferential binding to nucleosome embedded sites may constitute the effector and response modules of Satb1’s binding mechanism. It has been suggested that recognition of partial motifs on one face of nucleosomal DNA is the discerning feature of a pioneer factor41. Further studies are needed to delineate the Satb1-nucleosme binding interface at high resolution and to temporally resolve the changes in chromatin architecture upon binding of Satb1 to silent chromatin.Our data provide the first comprehensive quantitative picture of how Satb1 uses multiple identifiers to selectively target a small cohort of potential binding sites in the genome. SMT and spatiotemporal FRAP revealed that the CUT1 domain is enough to ensure prolonged interactions with the genome. By complementing imaging with deep sequencing, we have found that while HD is dispensable for prolonged interactions in SMT experiments, it plays a crucial role in assigning specificity by selecting targets surrounded by sequences with more negative propeller twist and higher A/T percentage. Further, Satb1 shows cooperative binding to genomic regions with high binding site density, a feature that is also visible in dynamic super-resolution images as chromatin interaction hotspots. While long tracts of poly(dA:dT) are unfavorable to nucleosome formation60. The donor plasmid was built based on pmCherry-C1 vector where the DNA of two homologous arms flanking the sequence of interest was inserted. Here mCherry was used a negative selection marker as it would be removed if proper homologous recombination and integration took place. The HR cassette consisted of a 5’ end and 3’ end homologous arms of 800–1000 bp (PCRed directly from Vl3 3M2 genomic DNA) flanking the loci of interest . Surveyor assay was performed for each guide sequence according to Sanjana et al.61. HR donor plasmid, guide DNA plasmid and Cas9 cDNA plasmid were electropolated into Vl3-3M2 cells using Neon system (invitrogen). Double nickase version of cas9 was used to enhance the editing specificity62. The cells were then selected with puromycin after 3 days of culture and sorted into 96-well for clonal selection. Cells per well was immuno-stained to obtain the final clonal Vl3 3M2 cells with double-allele knock-out. To tag the native Satb1 with eGFP in Vl3 3M2 cells, we used the same technique to insert an eGFP-P2A-puromycin sequence cassette replacing the stop codon by homologous recombination.We knocked out native Satb1 in Vl3 3M2 with CRISPR-mediated homogeneous recombination technique. Source vectors of guide-tracer DNA and Cas9 was obtained from Doudna lab at Berkeley and Zhang lab at MIT, through Addgene. Briefly, we first designed a CRISPR guide vector with double BsaI sites that is able to insert target oligo efficiently using golden gate method. This vector was constructed using SLICSatb1 knock-out CRISPR sgRNA (antisense strand): AGTTGCCTCGTTCAAATGASatb1 eGFP knock-in CRISPR sgRNA (antisense strand): GTTCTCTCAGTCTTTCAAGT−/− or MCF-10A (ATCC® CRL-10317™) cell lines. Stable cell lines were generated by lentiviral transduction. Cells were then selected for appropriate resistance marker to achieve stable integration and sorted by FACS.FL Satb1 and the different domain deletion mutants were generated as C terminal eGFP fusions in a pLenti based vector with cumate switch promoter and stably integrated into VL3 3M2  Satb1E. coli . E. coli were grown in M9 minimal media (according to manufacturer's protocol) at 16C, and protein expression was induced by IPTG. Unless otherwise stated, all subsequent steps were done at 4 C. Cells were spun down and lysed prior to protein isolation by standard His Nickel-NTA  column chromatography. The Nickel-NTA isolated fraction was further purified using Superose 6 Increase column 10/300 GL  size-exclusion chromatography. The choice of column provided the appropriate fractionation range and resolution such that the expected protein tetramer could be purified from smaller incomplete or degraded products. Subsequent analysis of column fractions was performed by visual inspection of SDS-Page gel electrophoresis  and fractions meeting expected size and purity were pooled and concentrated .Recombinant pETDuet plasmids were transformed into SoluBL21 64. All buffers used for purifying core histones were supplemented with 0.5 mM PMSF, 1000× final dilution of protease inhibitor cocktail , 20 µg/ml TPCK , 0.5 mM Benzamidine . Chicken erythrocyte nuclei were first washed with RSB buffer  and then resuspended in RSB supplemented with 1 mM CaCl2 and then treated with 3 µ/ml Micrococcal Nuclease for 20 min at 37 C. The nuclease digestion was stopped by adding 5 mM EDTA followed by incubation on ice. Digested chromatin was spun down at 8000 RPM for 10 min and the supernatant was discarded. The pellet was washed twice in 20 ml TE buffer . The final pellet was resuspended in TE buffer supplemented with 0.3 M NaCl and centrifuged at 3500 rpm for 10 min The pellet was resuspended in TE plus 0.65 M NaCl, homogenized in Dounce Homogenizer and then spun for 16 h at 46,000 rpm over a sucrose cushion (15% sucrose in TE plus 0.65 M NaCl). H5 histones were removed by repeating the wash in 0.675 M NaCl. The pellet was homogenized in TE buffer containing 2.0 M NaCl and centrifuged again for 16 h at 46,000 rpm. The supernatant contained equimolar mixture of 4 core histones.Core histone octamers were isolated from chicken erythrocytes43. The nucleotides at superhelical locations SHL3, SHL4, and SHL6 (where the minor groove is facing histones/histone core) were modified (shown below in bold) to generate Satb1 consensus sites.Nucleosomes were assembled using 5’ Alexa 488 labeled 601 A which is derivative of the nucleosome positioning 601TAATAATCCTTAATAACCAGGCACGTGTCATTAATAAACATCCTGTGCATGTGGATCCGCACTC3’5’CTATACGCGGCCGCCCTGGAGAATCCCGGTCTGCAGGCCGCTCAATTGGTCGTAGACAGCTCTAGCACCGCTTAAACGCACGTACGCGCTGTCCCCCGCGTTTTAACCGCCAAGGT64.Reconstitution was performed following established protocolsBriefly, core histones and DNA template were mixed at equimolar ratios in low binding tubes  in TE buffer (plus 2 M NaCl) and incubated on ice for 1 h. The mixture was then injected into a Slyde-A-Lyzer unit  and dialyzed in a reconstitution buffer  with a stepwise drop in NaCl concentration  to generate final reconstitutes.For Electrophoretic Mobility Shift Assays 6 nM nucleosomes were incubated with increasing loading of Satb1in total volume of 25 µl in STE buffer . Resulting complexes were separated on 1% type IV (Sigma cat. #A3643) agarose gel electrophoresis in 0.5xTBE buffer at constant 100 V and visualized on Typhoon fluoroimager.Uncropped versions of the EMSA gels shown in Fig. 33. Zeiss LSM 700 laser scanning microscope was used for image acquisition using ×63 oil immersion NA 1.40 objective. A 3.2 µm-line was scanned repeatedly with a pixel dwell time of 6.3 µs (32 pixels) and a line time of 0.47 ms. Data were processed by the SimFCS software developed at the Laboratory for Fluorescence Dynamics.Image correlation spectroscopy was conducted according to Cardarelli et al. 201038, on an Olympus CellTIRF system. TIRF angles for each fiber-coupled illumination laser were controlled independently with a motor. Imaging was done through a 1.49 NA ×100 objective and ×1.6 Optovar magnifier and recorded on an Andor iXon Plus EMCCD camera at 20 Hz. All dichroics and filters were purchased from Semrock. All experiments were performed within a heated, CO2 controlled incubation chamber set to 37 °C and 5%, respectively with additional temperature control provided using a collar-type objective heater, also set to 37 °C.All live cell single molecule imaging was done using HiLo Total internal reflection microscopy (HiLo-TIRFM)x/y position and signal amplitude. The single particle positions in each frame were then linked into single particle trajectories based on nearest-neighbor algorithm with a defined maximum per-frame jump distance. The maximum frame to frame displacement was set to 0.25 μm as this was the maximum step size that we observed for the chromatin fiducial histone H2B. Finally, a quality control step was performed where a large subset of particles and trajectories were validated by eye. Trajectories that lasted ≥ 10 frames (0.5 s) were selected for further analysis. Individual Satb1 molecules showed considerable variability in their motion. We classified displacements into fast, slow, and multistate categories based on the initial MSD values of each trace. The first 10 Log[δt] points (corresponding to δt = 500 ms) were fit to a line (Log[MSD] = a + b*Log[δt]).Single molecule tracking was preformed using Mathematica and python. Detectable particles in each frame were first identified by performing a Laplacian of Gaussian filter and then thresholding the filtered image based on intensity. Then the raw image data of each identified particle was fitted with a 2D Gaussian function to obtain particle For super-resolution imaging, after localizing the fluorophores, we combined all the localization centroids for the full length Satb1 in one computed image. For each detected molecule, a normalized symmetric 2D Gaussian  was drawn, and all the Gaussians from all the localizations (collected over 35 s at 20 Hz) were summed to yield a super-resolution image.2 for 20 min before imaging. For different cell lines, we scanned a 50 × 2-pixel rectangle (pixel size = 0.0893 μm) inside the thymocyte nucleus in euchromatic regions with homogeneous fluorescence distribution using 488 nm laser with 0.6% power and PMT gain 825. The cells were imaged at a frequency of 0.0076 s/frame. A circular spot with radius = 15 pix (1.34 μm) was bleached so that the center of the circle aligned with the center of the scanned rectangle. Experiments were repeated in the same cell nucleus one more time without the bleaching step to correct for photo-bleaching54.For FRAP experiments VL3 3M2 cells expressing different domain constructs were grown in RPMI media and then embedded in CyGEL Sustain (#CS20500) Biostatus for live imaging. FRAP experiments were performed on a Zeiss LSM 700 confocal microscope with a ×63/1.40 oil immersion objective. Cells were equilibrated at 37 °C with 5% CO54. Three parameters: Deff, kon* and koff were extracted from the model. Data processing and model fitting were designed and implemented in Mathematica.The FRAP images were corrected for photo-bleaching and then normalized. Spatiotemporal fluorescence data for fitting were generated for each time point by averaging over the two pixels that constituted the height of the rectangular scan region. This resulted in a 50 × 1 pixel-wide line (VL3-3M2) or a 80 × 1 or alternatively a 160 × 1 pixel-wide line (MCF10A) that represented the fluorescence along a line bisecting the bleach spot. This line was then averaged about the bleach center to produce a line that represented the radial distribution of the bleach spot fluorescence. These data were exponentially sampled in time and fit with a reaction diffusion model32. Briefly, FRAP recoveries of Satb1 variants were first fit to a pure diffusion model, which yielded the diffusion constant D. Of the five Satb1 truncation mutants, only ∆N was well fit by the diffusion model. The other four Satb1 mutants yielded diffusion coefficients that were too slow to be explained by pure diffusion. Therefore, their FRAP recoveries were fit with a reaction-diffusion model and yielded estimates of Deff (effective diffusion constant). The remaining parameters bf (bound fraction in the fast diffusing state), bs (bound fraction in the slow diffusing state), kon* (association rate), koff (dissociation rate), and Kd (dissociation constant) reported in Table 32.The FRAP parameters extracted from the fit have been elaborated in Stasevich et al.65 with modifications to adapt to chromatin shearing using Covaris AFA. Briefly, the cells were fixed with 1% PFA for 5 min, washed twice with PBS and then quenched with glycine (125 mM) for 5 min Cells are lysed with cell lysis buffer  for 15 min, and sheared in TE 10 mM + 0.1% SDS buffer with Covaris (Covaris Inc) S2 according to manufactures protocol .ChIP-seq libraries were prepared according to Myers lab protocolAfter centrifuging the lysates to remove debris, 2–5 µg antibody  was added to 4 ml of cell lysate and incubated overnight at 4 °C. Protein G magnetic beads (Invitrogen) were then added and the mixture was shaken for 2 h before collecting using a magnetic rack. The samples were washed 6 times  according to Myers lab protocol. The library was generated with Ultra II DNA kit (NEB) Briefly, DNA fragment ends were generated enzymatically and adaptors were ligated to immunoprecipitated DNA fragments for downstream PCR amplification using Illumina index primers.13 with some modifications: Cells were incubated with Trimethylpsoralen  at a final concentration of 2 μg/mL for 10 min in the dark. Plates of cells were then exposed to 3 kJ/m2 of 365-nm light (Fotodyne UV Transilluminator 3–3000 with 15-W bulbs). Cells were washed with PBS 1×, scraped from the culture dish and then spun down. The pellet was then resuspended  and incubated at 37 °C for 30 min Cells were then spun down and resuspended in TE  with 0.5% SDS and proteinase K and incubated at 50 °C for 1 h. Chromatin was then sheared with Covaris to get 200–500 bp fragments and subsequently cleaned with columns (Fisher GeneJET kit).TMP-seq libraries were prepared according to Teves and Henikoff (2014)13. In summary three μg of sonicated DNA, diluted to 250 μL, was boiled in a water bath for 10 min and incubated in ice water for 2 min To each sample, 30 μL of 10 × Exo I buffer and 10 μL of Exo I were added, and digestion was allowed to proceed for 2 h at 37 °C. Then samples were boiled and cooled as before, and 10 μL of Exo I was added for a second round of 1-h digestion. After cleanup, Exo I–digested DNA samples were subjected to enzymatic reactions for end polishing and ‘A’ tailing. Illumina adaptors were ligated using NEBNext Ultra library prep kit. After that, the 5′ strand was digested with 20 U of λ exonuclease (NEB) for 30 min at 37 °C. The DNA was purified with Ampure beads. The resulting 3′ strand was used as a template for ten rounds of primer extension in 1× pfu Ultra II HS buffer, 0.8 mM dNTP, 1U of pfu UltraII HS DNA polymerase (Agilent) and 40 nM of P7 extension primer. The resulting single-stranded products were purified with Ampure beads and eluted.Cross-linked fragments were enriched by repeated rounds of denaturation and Exonuclease I (Exo I) digestion following protocol from Teves and Henikoff (2014)The purified products were then appended with ribo-G in 1× terminal deoxynucleotidyl transferase (TdT) buffer, 10 U TdT (NEB) and 0.1 mM of rGTP in 37 °C for 30 min The products were purified with Ampure beads. The single-stranded ribotailed products were ligated to a double-stranded adaptor with CCC-overhangs AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT) and final library was generated by PCR amplification (using NEB Q5 High-Fidelity 2× master mix) for 5–10 cycles.2, pH 7.4) with 0.1% NP40, and tagmentated with 2.5 µl Tn5 enzyme. The libraries were prepared as per Buenrostro et al. (2013)37. Briefly DNA was cleaned with MiniElute columns (Qiagen), and PCR amplified for 8–12 cycles using Illumina NextEra primers (Illumina), where the cycle number was determined by qPCR.ATAC-seq.: 50k cells were collected and washed with ice cold PBS and then further washed with RSB buffer . Final library was prepared the same way as ATAC-seq library.Adapter sequences in sequencing reads were trimmed with cutadapt. Quality of reads were assessed by fastqc and aligned to human genome version hg19 and mouse version mm10 with bowtie2. Typically, alignment rate was between 90–95%.66, using Poisson statistical model. Differential binding was analyzed with DESeq2 package in R67, using binomial statistic model. The widely used MEME suite tools were employed for motif analysis39. Specifically, de novo motif discovery was performed with MEME and FIMO was used to search for occurrence of known motifs with default threshold.ChIP-seq binding site peak calls were made with MACS2Briefly, Motif analysis was performed using FIMO tools and custom python script. A 400 bp window centered at each binding site was selected and fasta sequence was retrieved with bedtools. FIMO was used to search for motif occurrence and location. Then the spacing between nearest motifs for each binding site and number of motif occurrences per 400 bp was calculated to correlate with ChIP-seq signals.Other ChIP-seq data were collected from ENCODE project, with the following access numbers:ENCFF000WJE (RelA), ENCFF002CIA (TCF3), ENCFF002CJF (Oct4), ENCFF002CHF (NFATc1), ENCFF002CJA (nanog), ENCFF231TGQ (IRF2), ENCFF388AJH (IRF1), ENCFF856FVT (FoxA1), ENCFF002DCM (CTCF). The PWM (power weight matrix) for other transcription factors and chromatin regulators were retrieved from MEME motif database.68 was used for genome feature annotation, and generation of read heatmap. ChIP-seq signals were presented as either raw reads or fold enrichment over background derived from MACS2 peak calling, as noted in the text and figures. When using raw reads, signal strength was calculated by summing the ChIP-seq reads within a defined genomic window as noted in the main text and normalized against a total count of 10 M reads.Homer software13 the first 4 bases were trimmed in each read before alignment to remove the CCC of ligated adapter. After alignment, cross-linked sites (starting base of each sequencing read) were detected and expanded on either side by 20 bps to smooth the distribution and converted to bigwig files for calculation. A window of 50 bp was used to average the TMP-seq signals.For TMP-seq analysisTMP-seq reads from purified MCF10A genomic DNA were used to correct for any sequence bias in psoralen crosslinking efficiency. Briefly, the TMP-seq reads for each experiment were first normalized to the total read count (10 M reads); then those reads were divided by background reads over a 50 bp sliding genomic window to calculate the true background corrected TMP-seq signals.For correlative analysis of TMP-signal enrichment versus CHIP-seq strength, MCF10A CHIP-seq data for various chromatin binding proteins including Ring1B (GSE107176), cohesin complex (GSE101921), Jun, JunB and Fos (GSE115597), BRD4 (GSE72931), and BRCA1 (GSE40591) were downloaded from SRA deposits. The fastq files were trimmed of adaptors and low quality reads (Q-score < 25), and then aligned to human genome hg19 using bowtie. We use the fold of enrichment score from the peak-calling algorithm MACS2 as the TF's binding strength. The corresponding TMP-seq signal per binding site was calculated as the Logarithm of mean TMP-seq reads over a 400 bp genomic window centered on the binding sites, divided by the background TMP-seq signal . The line plot for CHIP-seq versus TMP-seq relations for each TF was generated by calculating the average CHIP-seq signals for 20 evenly distributed groups of TMP-seq signal. The violin plots were used to show the distribution of TMP-seq signals for the top 20% strongly represented CHIP-seq peaks.37 and Denny et al.69. In summary, the reads were trimmed off adapter sequences by Cutadapt. Reads that were shorter than 28 bp after trimming were discarded. Remaining reads were aligned to either hg19 (human) or mm10 (mouse) genome to generate BAM files. For every library, we calculated the genome wide ratio of reads at TSS sites and reads at regions 2 kb away from TSS as the TSS-score. ATAC-seq libraries with TSS-score < 7 were discarded. MACS2 was used to call the peaks (using parameter –nomodel –keep-dup --broad). Nucleosome positions at accessible regions were called with nucleoatac tools42 with default parameters.ATAC-seq data were analyzed according to Buenrostro et al.44. All the sequences of interest and their shape parameters were categorized into 3 groups for comparison: nucleosome free region (NFR) derived from nucleosome position analysis as described above, accessible chromatin and inaccessible chromatin. When there were more than 10,000 motifs per group, motifs were down sampled randomly to 5000–7000 motifs for further analysis.The PWM (position weight matrix) for Satb1 motif was constructed from the motif discovery as described above. Genome wide Satb1 motif locations were obtained using Homer package . The DNA sequences flanking 300 bp on either side of motif were retrieved and saved in a fasta file for DNA shape analysis using the web serverU test5 was used to assign p-value per DNA base with typical sample size from 300–7000 sequence elements depending on group size.When comparing the shape parameters for weak and strong binding motifs, the shape data were mapped to ChIP-seq results to obtain the corresponding ChIP-seq signals per binding site. Typically, unless otherwise mentioned, we took 600 top and bottom motifs ranked according to ChIP-seq signals to compare the shape parameters. A nonparametric Mann–Whitney Further information on research design is available in the Peer Review FileDescription of Additional Supplementary FilesSupplementary InformationSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3Supplementary Movie 4Supplementary Code 1Reporting SummarySource Data


High-speed single-molecule fluorescence microscopy in vivo shows that transcription factors in eukaryotes can act in oligomeric clusters mediated by molecular crowding and intrinsically disordered protein. This finding impacts on the longstanding puzzle of how transcription factors find their gene targets so efficiently in the complex, heterogeneous environment of the cell.Abbreviations CDF - cumulative distribution function; FRAP - fluorescence recovery after photobleaching; GFP - Green fluorescent protein; STORM - stochastic optical reconstruction microscopy; TF - Transcription factor; YFP - Yellow fluorescent protein Cells regulate gene expression through binding of transcription factors (TFs) to promoters to turn gene expression on or off ,2. SimulSaccharomyces cerevisiae (budding yeast). In particular, its glucose sensing pathway presents an experimentally tractable system to study gene regulation. Here, control of gene expression is achieved by TFs which include the Zn finger DNA binding protein Mig1 [GAL genes involved in glucose metabolism [Many of the important features of gene expression control in eukaryotes are exemplified in the model unicellular microorganism ein Mig1  that acttabolism . Mig1 lotabolism , correlatabolism ,22.S. cerevisiae cells, using its glucose sensing pathway as a model for signal transduction, was explored using physics methods which enable the understanding of the processes of life one molecule at a time [In recent investigations from my own group  the spatt a time ,25, emplt a time . The comt a time  in both t a time . These st a time ,30, sucht a time –35, cellt a time –38 and pt a time , along wt a time –43. The t a time –46, and t a time –49.In this Points of View article I discuss further the findings from my team from single-molecule fluorescence microscopy to track functional TFs with very high speed to match typical rates of protein diffusion in live cells and thereby enable “blur-free” observations. We were able to quantify the composition and dynamics of Mig1 under normal and perturbed conditions which affected its state of phosphorylation, and also performed experiments on a protein called Msn2 which functions antagonistically, i.e. instead as an enhancer/activator, for many of the same Mig1 target genes  through S. cerevisiae cells ) using a green fluorescent protein (GFP) reporter for Mig1 integrated into the genome, including mCherry reporter on the RNA polymerase subunit protein Nrd1 to indicate the position of the cell nucleus. Slimfield was optimized for single-molecule detection sensitivity by using an in vitro imaging assay [We used millisecond Slimfield single-molecule fluorescence imaging ,45,51 onae cells ) using ang assay . We alsong assay  and esting assay , but is ng assay . AFM canng assay ), latticng assay ), multi-ng assay .), remotng assay .), and dng assay .).10.108glucose (-/+) we measured cytoplasmic and nuclear Mig1 localization bias respectively, visible in individual cells by our generating rapid microfluidic exchange (a few seconds) of extracellular fluid (Under depleted/elevated extracellular ar fluid ), and rear fluid ). The foar fluid ), with mD as a function of its location in either the cytoplasm, nucleus or translocating across the nuclear envelope, as well as the copy number of Mig1 molecules associated with each subcellular region and in each cell as a whole, indicating ~850–1,300 Mig1 molecules per cell dependent on extracellular glucose. It should be noted that confinement may affect the apparent diffusion coefficient in the small volume of a yeast nucleus if the length the mean square displacement (MSD) of tracked particles is comparable to the diameter of the nucleus, however, if our case only the short length scale MSD regions are considered to determine D.Nanoscale tracking determined the position of tracked Mig1 foci to a lateral precision of 40 nm ,62 couplGSY1 showed colocalization between PP7-GFP expressed off a plasmid and Mig1-mCherry expressed genomically under high glucose conditions. We also observed similar clustering and co-localization to PP7 for the antagonistic TF Msn2. These PP7 co-localization results suggest that clusters both of Mig1 and Msn2 are functionally active in regulating target gene expression of the test target gene GSY1.In control experiments, a modified strain  generateglucose (±), and nuclear foci at glucose (-), were consistent with just a single mobile population whose D of 1–2 μm2/s consistent with earlier observations. However, nuclear foci at glucose (+) indicated a mixture of mobile and immobile components (glucose (+) Mig1 diffusion being impacted by interactions with nuclear structures, similar to that reported for other TFs [lac repressor. Control experiments with Zn finger deletion strains of Mig1 indicated that Mig1 clusters bind to the DNA via their Zn finger motif with direct glucose dependence. At the high laser exceition intensities used for Slimfiled imaging photobleaching is rapid, and so typically a single GFP molecule will photobleach on average after 5–10 consecutive image frame. To account for this we interpolate observed foci brightness values back to the start of each photobleach using an exponential photobleach function. We observed no direct evidence for irreversible photobleaching (i.e. “photoblinking”) with GFP at these intensities, though other fluorescent proteins such as YFP have been known to exhibit such blinking behavior, which if so would need to be further characterized, for example using surface imnmobilized purified YFP in vitro samples. A general compromise here, however, is to confine tracking analysis to typically less than 100 ms of laser exposure so that irreversible photoblinking is more dominant than reversible blinking.Cytoplasmic Mig1 fluorescent foci at mponents ). These ther TFs . Here, tglucose (-) does not exhibit immobility in the nucleus and that Mig1 lacking the Zn finger still accumulates in the nucleus at glucose (+) this suggests that Mig1 localization is driven by changes in Mig1 binding affinity to other proteins, e.g. the general co-repressor complex at the genome [We compared the spatiotemporal dynamics of different Mig1 clusters during translocation by converting trans-nuclear tracks into coordinates parallel and perpendicular to the measured nuclear envelope location, and synchronizing coordinate origins to be at the nuclear envelope crossing point for a given foci track. A heat map of spatial locations of translocating clusters indicated a hotspot of comparable volume to the nuclear pore complexes and accessory structures ,66 Figu). The dwe genome , or outsglucose (-) and (+) with a time constant of just a few seconds but a larger time constant at glucose (+) for nuclear foci of at least ~100s (By modifying the microscope we were able to implement fluorescence recovery after photobleaching (FRAP) to probe nuclear turnover of Mig1, by focusing a separate laser onto just the nucleus, photobleaching this region with a rapid 200 ms pulse, and quantifying any subsequent fluorescence intensity recovery into that region ). We coust ~100s ), with rIn vitro Slimfield imaging of purified Mig1-GFP under identical imaging conditions for live cells similarly indicated monomeric Mig1-GFP foci in addition to a small fraction of brighter foci which were consistent with chance overlap of monomer GFP images. However, addition of a molecular crowding reagent in the form of low molecular weight polyethylene glycol (PEG) at a concentration known to correspond to small molecule “depletion” forces in cells [Native, denaturing gel electrophoresis and western blots on purified extracts from Mig1-GFP cells ) indicatin cells  resultedSecondary structure predictions suggested significant regions of disorder away from the Zn finger binding motif. We measured changes in circular dichroism of the Mig1 fusion construct upon addition of PEG ) in a waOur findings address aspects of functional gene regulation in live cells which have hitherto been unexplored, using biophysical technology that has not been available until recently. The results strongly support a functional link between Mig1 and Msn2 TF clusters and target gene expression; a biological role of multivalent TFs for enhancing intersegmental transfer had been elucidated previously in simulations  but unobS. cerevisiae genome from the 3C model indicates 20–30% are <50 nm apart, small enough to enable different DNA segments to be linked though intersegment transfer by a single cluster [Quantifying nearest-neighbor distances between Mig1 promoter sites in the  cluster ,9, whichOur discovery is, to our knowledge, the first to make a link between predicted disorder and the ability to form oligomeric clusters in TFs. Our findings may potentially offer some insights into addressing the longstanding question of why in general there is so much predicted disorder in eukaryote transcription factors; ~90% of eukaryotic TFs indicate significant proportions of sequences with disordered content . Our fin


CDK8 and its paralog, CDK19, collectively termed ‘Mediator Kinase,’ are cyclin-dependent kinases that have been implicated as key rheostats in cellular homeostasis and developmental programming. CDK8 and CDK19 are incorporated, in a mutually exclusive manner, as part of a 4-protein complex called the Mediator kinase module. This module reversibly associates with the Mediator, a 26 subunit protein complex that regulates RNA Polymerase II mediated gene expression. As part of this complex, the Mediator kinases have been implicated in diverse process such as developmental signaling, metabolic homeostasis and in innate immunity. In recent years, dysregulation of Mediator kinase module proteins, including CDK8/19, has been implicated in the development of different human diseases, and in particular cancer. This has led to intense efforts to understand how CDK8/19 regulate diverse biological outputs and develop Mediator kinase inhibitors that can be exploited therapeutically. Herein, we review both context and function of the Mediator kinases at a molecular, cellular and animal level. In so doing, we illuminate emerging concepts underpinning Mediator kinase biology and highlight certain aspects that remain unsolved. Transcription, the first stage of gene expression, is a fundamental cellular process by which genetic information (DNA) is converted to a molecule (RNA) that can exert a molecular phenotype. Aberrant transcription has been implicated as a causal event in diverse human diseases, underscoring the need to better understand and control this critical process. In recent years a slew of small molecule inhibitors have been developed and utilized to target proteins that control transcription at the either the epigenetic  or genetAt the heart of the basal transcriptional machinery is the holoenzyme RNA polymerase II (RNPII), composed of 12 subunits that mediate the transcription of DNA to messenger RNA (mRNA). RNPII, in turn, is regulated by a macromolecular complex called the pre-initiation complex (PIC), which consists of TFIIA, TFIIB, TFIIE, TFIIF, TFIID, TFIIH, SAGA, P-TEFb, and Mediator . A centrCyclin-dependent kinases (CDKs) are serine/threonine protein kinases that serve as key regulators of both cell cycle and transcription. CDKs require the binding of a cyclin subunit to exert their enzymatic activity and are broadly divided into two functional subgroups, cell cycle CDKs, and transcriptional CDKs . Among tSaccharomyces cerevisiae (yeast) as transcriptional regulators of the RNPII holoenzyme , which has been associated with impaired gene expression  was delayed or completely skipped in more than half of all cells, resulting in formation of two diploid spores (dyads). These results suggest an important role for CCNC during meiosis in yeast . InducerThe same growth conditions (nitrogen limitation and the presence of a fermentable carbon source) can induce pseudohyphal growth, a differentiation pathway in yeast which phenotypic occurrence correlates with the expression of FLO11 . During Although the exact mechanism of how CDK8-CCNC represses H3 Lys4 trimethylation at the FLO11 locus is unknown, it has been suggested that CDK8/CCNC might interact with the transcriptional repressor Sfl1p to inhibit gene expression at the target locus as it has been described for SUC2 . FurtherDrosophila melanogaster, all four kinase module subunits CDK8, CCNC, MED12, and MED13 are essential for development  which forms a complex with cofactors CBF1 and MAML to activate the response genes  reported no significant abnormalities. These mice are born at expected Mendelian ratios and have a body weight as well as a normal life-span. Analysis performed by the IMPC did not detect behavioral differences and no phenotypes in analyzed compartments such as the neurological-, reproductive- or cardiovascular-system. Thus, CDK19 is dispensable for both embryonic development and adult tissue homeostasis . CDK19-d-/-Cdk8 and -/-Cdk19 mice demonstrate clearly distinct functions of CDK8 and its homolog CDK19. In some specific instances, CKM subunit members might have a certain degree of functional redundancy. For instance, MED12L can bind Sox10 during myelination in a similar way to MED12 and both, CDK8 and CDK19 can interact with the histone arginine methyl transferase PRMT5 to repress transcription  homeostasis, as induced deletion of MED12 in HSCs and progenitor cells (HSPCs) causes bone marrow aplasia resulting in lethality within 4 days . AnalysiMinApc model, a murine model of colorectal cancer (CRC), CDK8 deletion shortens the survival of the mice and increasing tumor burden. While tumor initiation is not affected, tumor growth rate and tumor size are increased upon ablation of CDK8, accompanied by reduced histone H3K27 tri-methylation affecting the promoters of PcG-regulated genes and increasing oncogenic signaling  to adapt to hypoxic conditions. CDK8 but not CDK19 is required for HIF1A induced gene expression upon hypoxia in CRC cells . Mechaniin vitro and in vivo in the presence of ATP analog 3MB-PP1. In these cells, CDK8 kinase activity was required for maximum expression of ∼7.5% of all genes during normoxia, and the maximum expression of ∼17% of all genes during hypoxia and 23.3% of hypoxia inducible genes is CDK8-kinase activity dependent  pathways, activated upon sensing of single- and double strand breaks by ATR and ATM, prevent accumulation of damaged DNA and thus, maintain genomic stability of cells . Recent Trp53) is a well know tumor suppressor that regulates various cellular processes such as DNA repair, cell cycle and apoptosis upon DNA damage to prevent accumulation of mutations in the genome. In osteosarcoma cells that do not express CDK8, knockdown of CDK19 resulted in reduced proliferation, accompanied by reduced mitotic gene expression. In contrast, genes involved in the p53 pathway and cholesterol metabolism were upregulated (Cdkn1a (p21) and Bbc3 (PUMA) in response to Nutlin-3 was reduced. As PUMA is a pro-apoptotic protein, reduced levels of apoptosis were observed (in vivo to prevent the formation of tumors in tissues exposed to DNA damaging agents (such as skin UV radiation).p53  family of transcription factors are master-regulators of inflammation and have been implicated in cancer . Recentled genes . During se genes . These sInterferons (IFNs) are signaling proteins that provide a first unspecific line of defense against invading pathogens . CDK8 waex vivo. Tissue co-culture models using different primary cells revealed a modulated expression of various cytokines such as IL-8 and IL-17A, IL-17F upon CDK8/19 inhibition, suggesting that CDK8/19 may have a function in inflammatory gene expression in vivo (Interestingly, there is evidence that CDK8/19 might regulate cytokine expression  in vivo . While CCDK8 and CDK19 are among a unique class of cyclin dependent kinases with non-cell cycle roles. In this review, we have summarized the molecular interaction of these kinases with Mediator, their pleiotropic effects on multiple signaling pathways and their important roles in both embryonic development and adult tissue homoestasis. An overarching theme from these studies is the exemplary ability of these kinases to impinge on different pathways in a context specific manner. Future studies focusing on the epigenetic and molecular contexts that underpin CKM regulatory effects will be critical to advance our understanding of how gene expression can be fine tuned in a temporal and spatial manner.RF designed the topic of the review. MD, DS, and JL wrote the manuscript. MD and RF edited the manuscript. RF approved the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Polycomb repressive complex 2 (PRC2) is a chromatin binding complex that represses gene expression by methylating histone H3 at K27 to establish repressed chromatin domains. PRC2 can either regulate genes directly through the methyltransferase activity of its component EZH2 or indirectly by regulating other gene regulators. Gene expression analysis of glioblastoma (GBM) cells lacking EZH2 showed that PRC2 regulates hundreds of interferon-stimulated genes (ISGs). We found that PRC2 directly represses several ISGs and also indirectly activates a distinct set of ISGs. Assessment of EZH2 binding proximal to miRNAs showed that PRC2 directly represses miRNAs encoded in the chromosome 14 imprinted DLK1-DIO3 locus. We found that repression of this locus by PRC2 occurs in immortalized GBM-derived cell lines as well as in primary bulk tumors from GBM and anaplastic astrocytoma patients. Through repression of these miRNAs and several other miRNAs, PRC2 activates a set of ISGs that are targeted by these miRNAs. This PRC2-miRNA-ISG network is likely to be important in regulating gene expression programs in GBM. PRC2 (polycomb repressive complex 2) is an epigenetic modifier complex that binds near transcription start sites and elsewhere in the genome and silences genes by methylating histone H3 at K27 through its histone methyltransferase component EZH2. . PRC2 caSeveral cancers including GBM, are characterized by upregulation of multiple components of the PRC2 complex including EZH2 . PRC2 coThe regulatory interplay between miRNAs and PRC2 components has been shown to play important roles in several disease and developmental pathways. In several contexts, miRNAs exert their regulatory impact by repressing PRC2 components , 8. SimiPreviously, we identified a regulatory network between PRC2 and miRNAs where PRC2 represses genes by activating miRNAs . Here, wT98G cells (ATCC-CRL-1690) were grown in EMEM with 10% FBS. Cells were maintained at 37°C and 5% CO2. For type I interferon treatment, cells were treated with universal type I IFN-α  at 100 U/ml final concentration for 6 hr. For type II interferon treatment, cells were treated with IFN-γ (Millipore Sigma SRP3058) at 30 ng/ml final concentration for 48 hr.RNA from interferon treated T98G cells and primary tumor samples was extracted with TRIzol. Total RNA was then enriched for mRNA using NEXTFLEX Poly(A) Beads Bioo 512980) as previously described 980 as pr. Reads wThe 3' UTR of target mRNAs spanning at least 500 bp around the predicted miRNA binding sites were cloned into the psi-CHECK2 vector  downstream of the Renilla luciferase gene . Mutagen2 fold induction in response to either IFN-α or IFN-γ as ISGs for further analysis. We found that 40% of the genes regulated by EZH2 were ISGs (P = 9.6e-89). 737 ISGs showed higher expression levels in response to loss of EZH2, suggesting that they were normally repressed by EZH2  of the EZH2-repressed genes also showed induction on interferon treatment . This suPolycomb proteins and miRNAs have independently been implicated in the regulation of interferon genes , 23, 24.S1 Table(PDF)Click here for additional data file.S2 TableExpression values for significantly affected genes  are shown for WT and EZH2 -/- cells in two replicate experiments. Columns on the right indicate whether a gene was repressed by EZH2 and if it showed binding by EZH2 in ChIP-seq data.(TXT)Click here for additional data file.


Multiplexed cell-based screening of promoters for three TetR-like aTFs generated with this approach gave rich diversity of gene expression levels, dynamic ranges and ligand sensitivities and were 50- to 100-fold more active over their respective native promoters. Machine learning on our dataset revealed that relative position of the core motif and bases flanking the core motif play an important role in modulating induction response. Our generalized approach yields customizable and programmable aTF-regulated promoters for engineering cellular pathways and enables the discovery of new small molecule biosensors.Ligand-responsive allosteric transcription factors (aTF) play a vital role in genetic circuits and high-throughput screening because they transduce biochemical signals into gene expression changes. Programmable control of gene expression from aTF-regulated promoter is important because different downstream effector genes function optimally at different expression levels. However, tuning gene expression of native promoters is difficult due to complex layers of homeostatic regulation encoded within them. We engineered synthetic promoters  The ability to program gene expression is a fundamental requirement for engineering new cellular functions. Development of standardized parts has enabled the design of sophisticated, model-guided genetic circuits with minimal human intervention . This hede novo engineer aTF-regulated promoters based on a minimal constitutive promoter completely unrelated to the native promoter and devoid of regulation. Our goal was to engineer a suite of inducible promoters for an aTF displaying the full range of gene expression levels and ligand response characteristics. We chose to engineer promoters for TetR-like family of aTFs because they are commonly used in genetic circuits  followed by clonal screening  plates containing 100 μg/ml carbenicillin. After overnight growth at 37°C, we inoculated colonies into 3 ml LB medium (100 μg/ml carbenicillin) and grew the cells to an optical density of 0.6 at 600 nm (OD600) at 37°C. Then we induced cultures with 0.5 mM isopropyl β-d-1-thiogalactopyranoside (IPTG) for 3 h and confirmed protein expressions on a 4–12% Bis–Tris glycine gel. Cells were subsequently lysed by sonication in a lysis buffer .We synthesized codon optimized genes for all three aTFs  and cloned them using Gibson Assembly into overexpression vector pET-31b with a C-terminus His tag . We tranWe combined equimolar amounts of 16, 17, 18 and 19 base pairs (bp) hybridized promoter libraries into one mixed library. The mixed library (100 nM) was incubated with 2 μl protein lysate in 1× enrichment buffer  in 20 μl reactions at room temperature for 1 h. We mixed 2.5 μl magnetic His-tag pull-down beads  with protein and DNA by rotating at low speed for 30 min at 4°C. We pulled down protein–DNA complex attached to beads, removed unbound DNA and washed the complex three times with pre-wash buffer . After removing final wash buffer, we mixed the complex with Kapa HF PCR master mix and primers to amplify enriched DNA  and confirmed the amplicon size on a 2% agarose gel.in vitro enriched DNA for deep sequencing, we added standard Illumina P5 adaptor and custom 8 bp barcode to the 5′ end using PCR . Barcoded DNA amplicon size was checked using 2100 Bioanalyzer (Agilent) and sequenced using Illumina HiSeq 2500 sequencing system  after addition of 40% PhiX sequencing control (Illumina). The sequencing run gave around 289 million raw reads.To prepare k-mers’ - these are k-mers with spaces in the middle of the form ‘l-mer m-space n-mer’ (thus l + m + n = k), where l and n can take values 4 or 5 and spacer m can take values 0 to N – l – n, where N is the length of the random portion of the library. For example, ACTACxxxACGC is a ‘gapped 12-mer’ of ‘5-mer 3-spacer 4-mer’ type, where spacers x can be any nucleotide A, C, G or T. First occurrence of such gapped k-mers is counted in the random portion of the sequenced sample and those with occurrence <50 counts are removed from further analysis. Then the enrichment for each gapped k-mer is calculated by dividing these counts in the sequenced sample against the expected number of occurrences of the same gapped k-mer from the starting random library . For the starting library the sequencing depth is enough to have counts for all 8-mer (without spaces or gaps in the middle), but the depth  isn’t enough to capture all gapped k-mers or even non gapped k-mers with k ≥10. Thus, to normalize the counts of gapped k-mer in the sample against library, a fifth-order Markov model was created to get the expected number of occurrence of each sequence and capture the bias in the library  by first calculating enrichment for all gapped k-mer sequences having 1-mismatch to the seed sequence in the same manner and then weighting each position of PWM on the basis of the calculated enrichment values  and were ordered as single-stranded DNA oligos from Integrated DNA Technologies, Inc. To render the libraries double-stranded, we used a primer complimentary to the 3′ of the single-stranded libraries for DNA hybridization and DNA polymerization . Hybridization products were purified  and the amplicon sizes were verified on a 2% agarose gel. We used Golden Gate Assembly to clone the promoters into designed vector pXL-3  and recovered at 37°C for 1 h. After recovery, serial dilutions of transformants on LB (50 μg/ml kanamycin) plates yielded ∼500 000 CFUs per transformation.The promoter libraries were based on a strong constitutive in vitro enriched promoter library. We plated serial dilutions of each transformation and estimated ∼500 000 CFUs per transformation. We made two replicates of cells carrying libraries with aTF and induced one of them with appropriate small molecule inducer. We induced the PmeR library with 250 μM phloretin, TtgR library with 500 μM Naringenin and NalC library with 20 μM PCP. After growing with shaking at 37°C for 5 h, cells were prepared in PBS for flow cytometry .We cloned aTFs into a pSC101 backbone . We removed doublets and cell debris by setting forward scattering threshold to 2500 arbitrary units. We gated out doublets by plotting forward scattering area versus forward scattering height and removing events with roughly doubled time (time is a function of area and height in the forward scattering measurement). We sorted 500 000 events per bin and recovered sorted population in 4 ml LB shaking at 37°C for 1 h. Then, we added antibiotics (50 μg/ml kanamycin and 50 μg/ml spectinomycin) and allowed cells to grow to OD600 of 0.6. Then, we reinoculated cells in fresh LB at 1:15 dilution either with or without respective inducers. We allowed cells to grow for 5 h before measuring their OD600, GFP and mCherry fluorescence on a multi-plate reader (Biotek HTX). We calculated fold induction by dividing induced normalized fluorescence against non-induced normalized fluorescence. We ranked colonies by their normalized induced fluorescence and selected 96 colonies from each aTF initial colony pool with high fold inductions along the normalized induced fluorescence scale. We sequenced 96 colonies from each aTF pool to remove any duplications and sequences with imperfect constant regions on the promoter, RBS and reporter gene gfp. At the end, we had 30–60 unique sequences for each aTF . We inoculated colonies in LB (50 μg/ml Kanamycin and 50 μg/ml Spectinomycin) in 96-well plates and allowed cells to grow until ODF Figure . We measF Figure . We calcR Figure . Fitted R Figure .For each of the functional operator sequences corresponding to the three prokaryotic transcription factors—PmeR, TtgR and NalC, fold induction ratios were computed based on experimentally measured induced and repressed level responses. As the operator sequences were of varying length, multiple sequence alignment was performed using T-coffee as it yielded the most compact gap-filled sequence representation  Supplem. These gIn order to assess which nucleotides at a given position are important, feature importance scores were computed based on the number of times a particular feature was selected among the best set of features obtained at the end of simulated annealing across 100 bootstrap samples of the training dataset, as well as the observed distribution of the feature values among the inducible and non-inducible sets. These feature importance scores were internally normalized and custom position weight matrices were computed for inducible and non-inducible sets for the three different aTFs. Mathematically,ogo tool .E. coli promoter (apFab71) with canonical –35 (TTGACA) and –10 (TATAAT) sites as the starting promoter for design. We call this the ‘template’ promoter because all engineered promoter variants are derivatives of apFab71. We created a library of operators with theoretical diversity of approximately 1012 sequences by randomizing the spacer bases between –35 and –10 sites. Although spacers in natural promoters are typically 17 bp long, we created 16, 17, 18 and 19 bp long spacer libraries to provide greater adaptability for binding to an aTF. Prior to in vitro selection, we validated that all three aTFs indeed bind and release their native binding site in a ligand-dependent manner by gel shift measurements , and sog CD-HIT ,21  crossing angle between the DNA-binding domains of individual monomers could be greater with native binding site, but narrower with in vitro operators. Interestingly, we observe the effect in NalC where separation between half sites is longer in the in vitro operator library (6 bp) compared to the native binding site   gave high fluorescence confirming that promoters are still constitutively active  spanned 50–100-fold indicating that the operator affinity for aTF varies widely across the promoter library. Fluorescence distribution of repressed TtgR promoter library (aTF+/inducer-) is distinctly bimodal with a small fraction of high GFP cells which likely contain operators with low or no affinity for TtgR  below toxicity threshold to repressed cells . Fold inE. coli , we simplify aTF portability and achieve high activity.For each aTF, we clonally evaluated promoters by testing ∼100 randomly selected colonies from each sorted subpopulation with high fold induction to find 30–50 inducible promoters. Selected promoters for all three aTFs gave a broad range of gene expression properties in terms of maximum reporter expression and dynamic range of the reporter signal (ratio of induced to uninduced reporter expression) Figure . All proE. coli  but similar gene expression levels (Vmax). The widest gap in Km values for similar Vmax are the following promoter pairs: PmeR promoters 20 versus 17 (77 μM versus 149 μM), TtgR promoters 5 versus 3 (122 μM versus 339 μM), and NalC promoters 8 versus 11 (2.8 μM versus 12.2 μM). This result is non-intuitive because it shows ligand sensitivity can be altered by changing operators without mutating the protein itself. One may expect ligand sensitivity to be linked to ligand affinity or allosteric activity which are properties of the aTF, not the operator. Second, changing operators can change cooperativity of signal response  of all promoters and fitted the data to a standard Hill equation. The dose response curves revealed rich functional diversity of promoter variants with different ligand sensitivities and transcriptional activities compared to native promoter ported in gression . Vmax iss Figure . First, s Figure  are promresponse . Greaterresponse . Since cresponse ,27. Thirin vitro binding assay, to assess the contribution of different nucleotides at a given position. It is well known that sampling sequence space uniformly helps improve predictive power of empirical models for making predictions on unexplored regions. To retrospectively assess sequence and functional diversity in our dataset, we plotted edit (or Levenshtein) distance between sequence of interest and a reference sequence  vs. fold induction ratio   Figure . While P) Figure  (Supplem) Figure . Based o) Figure . We used) Figure  with rad) Figure . In orde) Figure  for opern Figure  which sun Figure . Differen Figure . They caE. coli promoter because it mimicked the location of natural operator sites for many aTFs and hence would likely be most effective region for blocking the RNA polymerase. This architecture imposes a limitation on length of the operator because transcriptional strength is highly sensitive to the distance between –35 and –10 sites . Our methodology can be easily modified to explore other promoter architectures to accommodate aTFs with longer binding footprint. Length is no longer a limitation if the operator is placed upstream of –35 site, flanking –35 on both sides, flanking –10 on both sides or downstream of –10 site. To improve the efficiency of in vitro selection, we can facilitate binding by a priori fixing the core motif from natural operators and randomizing the remaining positions. Our promoter engineering strategy should be applicable to transcription activators too because though they are mechanistically different from repressors, the underlying principles governing aTF-operator interactions remain the same. For instance, transcription activators of AraC-, LysR- and XylR-like families translocate across distal and proximal operator sites with differential affinities in a ligand-dependent manner to mask or unmask RNA polymerase binding sites within a promoter (Programmable ligand-inducible promoters are an invaluable tool for synthetic circuit design and biosensor-guided high-throughput screening. We designed inducible promoters by modularized assembly onto the simplest of bacterial promoters, devoid of complex regulation found in native promoters. By simply changing aTF’s affinity for its operators, we engineered promoter variants with different gene expression levels, dynamic ranges and ligand sensitivities. This generalizable method should be applicable to other TetR-family proteins and potentially thousands of other bacterial repressors expanding the suite of aTFs for synthetic biology applications. We inserted the operator between the –35 and –10 sites of an promoter . Operatode novo engineer inducible promoters for metagenomic aTFs by our method. Non-model organisms have attracted renewed interest, enabled by CRISPR-Cas9- genome editing, because their novel metabolic capabilities can be harnessed for biosynthetic engineering (E. coli, their use is virtually non-existent in non-model organisms due to poor compatibility of promoters across hosts. Our method should simplify aTF portability into industrially important non-model microbes including Corynebacterium, Pseudomonas and Zymomonas by using a constitutive promoter of the native host, and adjusting aTF-operator interactions to match the strength of the native promoter.Discovery of new metagenomic aTF biosensors for high-throughput screening would be bolstered by our method ,31. The ineering . AlthougOur result also challenges the passive role attributed to an operator as merely a docking or tethering site of the aTF. This view arises from perceiving the ligand as the allosteric effector and DNA-binding interface as the distal site. We show that changing the operator can profoundly affect transcriptional response such as ligand sensitivity and cooperativity which are generally thought of as properties of the protein, not DNA. Protein allostery could be potentially bidirectional where both allosteric and distal sites ‘talk’ to each other rather than the conventional unidirectional paradigm. Furthermore, this would expose a whole new layer of site-specific transcription regulation governed by aTF-operator interactions.gkz772_Supplemental_FileClick here for additional data file.


Instead, it appears that folding at a larger scale than the nucleosome involves a variety of random structures with fractal characteristics. Nevertheless, recent progress provides evidence for the existence of structural motifs in chromatin fibers, potentially localized to strategic sites in the genome. Here we review the current understanding of chromatin fiber folding and the emerging roles that oligonucleosomal motifs play in the regulation of genome function.Nucleosomes cover eukaryotic genomes like beads on a string and play a central role in regulating genome function. Isolated strings of nucleosomes have the potential to compact and form higher order chromatin structures, such as the well-characterized 30-nm fiber. However, despite tremendous advances in observing chromatin fibers  The physical form of eukaryotic genomes is a complex of DNA, RNA, and proteins referred to as ‘chromatin’. Chromatin compacts the long and fragile DNA molecules into the confines of the cell’s nucleus and protects them. At the same time it facilitates and regulates access to the underlying DNA sequence for vital processes such as transcription, replication, DNA repair, and chromosome segregation. It is therefore not surprising that chromatin is deeply involved in adaptation to environmental changes, in development, ageing, and in disease etiology ,2.The basic building block of chromatin is the nucleosome, which is constituted by an octameric histone core wrapped by 145–147 bp of DNA in approximately 1.7 helical turns ,4. The hThe primary role of histones is to neutralize the DNA charge and together with inorganic and small organic ions they facilitate the compaction of the genome . In contRecent advances have shown that at the kilo- to megabase scale, the chromatin fiber organizes into preferentially interacting regions, referred to as Chromosomally Interacting Domains (CIDs) or Topologically Associating Domains (TADs), which for example regulate enhancer–promoter interactions and replication units through the action of cohesin and CTCF ,13. TADsFor the last 50 years, the structure of the chromatin fiber has been analyzed intensively by biochemical approaches, by light and electron microscopy (EM), by small angle diffraction and X-ray crystallography.in vitro by cross-linking approaches [First groundbreaking work using EM on chromatin isolated from cells revealed the existence of filaments with a beads-on-a-string appearance  and furtproaches  and by Xproaches  , although on top of a stronger background signal, which indicates increased heterogeneity in linker lengths in the mESCs [When the relative frequencies of NRLs are compared, a 10-bp  quantization becomes apparent . Also wihrocytes ,48. Chemhe mESCs . Comparihe mESCs .Drosophila melanogaster have made significant advances in the question of nucleosome spacing on genomes and show that heterochromatic regions and non-transcribed genes are more regularly spaced than transcriptionally active regions [The genomics area has seen a large increase in nucleosome mapping studies that combine micrococcal nuclease digestion or chemical cleavage with next-generation sequencing (NGS) ,55. Thes regions ,56. Thes regions ,57,58.Given the primary distribution of nucleosomes on the DNA sequence, how do the strings of nucleosomes fold into secondary and tertiary structures? Seminal experiments using small angle X-ray scattering on nuclei revealed the presence of 40-30 nm periodicities, which were assigned to chromatin fibers ,59. Howein situ EM that relies on cross-linking, staining, and resin embedding has a deleterious impact on the fine structure of chromatin, and therefore structural analyses of chromatin fibers have been unreliable until the establishment of cryo-EM technology, which enabled imaging of frozen hydrated sections of cells in vitreous ice [in situ cryo-EM technologies to chromatin has made it clear that there is enormous complexity in the chromatin fibers structure, which depends on organism, cell type, cell cycle, and genome context. On one hand, there are cells with clear evidence for 30-nm fibers such as sperm from marine species like sea urchin or sea stars and from chicken erythrocytes [Traditional eous ice ,68  have recently been developed to study chromatin fiber structure in vivo ,82. Thesin vivo . Chromatin vivo ,85. It iDinucleosomes are the simplest oligonucleosomal substrates, but as all other chromatin fiber motifs they are nevertheless structurally quite diverse. This is due to DNA linker variation, which not only changes the distance between the two nucleosomes, but also has a huge impact on relative orientation, since addition of every base pair induces a change in relative rotation between the two nucleosomes of 36°. There are multiple lines of evidence that dinucleosomes are preferred substrates for chromatin-modifying complexes. For example the polycomb complex PRC2 prefers di- and oligonucleosomes as substrates over mononucleosomes ,87, and in cis as well as in trans (between fibers) to condense and segregate heterochromatin.The heterochromatin protein HP1 is a dimer with two H3K9me3 binding chromodomains, which has the potential to engage with two nucleosomes. Recently, HP1 bound to a dinucleosome has been visualized by cryo-EM  Figure B. Nevertin situ by cryo-EM [Trinucleosomes form V-shaped motifs that compact upon increasing salt levels , and tri cryo-EM  and are  cryo-EM ,96. Part cryo-EM .S. pombe has a short NRL with an average of 152 bp, and the tetranucleosome crystal structures with 157 bp NRL reveal a relatively open structure with minimal face-to-face contact in the chromatin fiber [in situ cryo-EM of S. pombe chromatin is consistent with the crystal structures since face-to-face nucleosome contacts were not observed [S. cerevisiae average NRL of 165 bp and shows a compact, dumbbell-shaped arrangement of two pairs of nucleosomes facing each other [S. cerevisiae chromatin. Tetranucleosomes have further been implicated in chromatin compaction through the PRC1 complex. In vitro, PRC1 binds and compacts preferentially tetranucleosomes [Tetranucleosomes have shown to be crystallizable biochemical entities at NRLs of 157 and 167 bp ,30 FiguA,B. Theyobserved . In contobserved . The NRLch other . Comparich other . Insteadleosomes . Howeverin vitro and in cells [Description of penta- and longer oligonucleosomes as preferred substrates of chromatin-associated complexes is rare and longer regular structures have not been detected in mammalian cells . One docin cells .In vitro there is clear evidence for the potential of the chromatin fiber to assume compact, regular structures. However, in the crowded environment of the nucleus these fibers appear to interdigitate and ‘melt’ into each other in a highly random fashion. At the same time, there is evidence supporting the existence of small oligonucleosomal structures in the nucleus as well as evidence supporting oligonucleosomes as the physiological substrates for large chromatin-associated complexes. Elucidating the role of oligonucleosomal structures in genome function therefore remains a key topic. The technologies most likely to provide breakthroughs are cryo-EM and micro-C derived NGS approaches. High-resolution cryo-EM combined with focused ion beam (FIB) milling to create undistorted, ultrathin sections of cells is already being employed [While the nucleosome is a structurally well-defined entity, the chain of nucleosomes appears to assume highly variable architectures. employed  and willemployed . We are in vitro, chromatin fibers in situ manifest a highly random organization.In contrast with regular structures of chromatin fibers observed Oligonucleosomes serve as preferred substrates for chromatin-associated complexes.We propose that defined oligonucleosomal structures play an important role in chromatin biology.


Cap Analysis of Gene Expression (CAGE) has emerged as a powerful experimental technique for assisting in the identification of transcription start sites (TSSs). There is strong evidence that CAGE also identifies capping sites along various other locations of transcribed loci such as splicing byproducts, alternative isoforms and capped molecules overlapping introns and exons. We present ADAPT-CAGE, a Machine Learning framework which is trained to distinguish between CAGE signal derived from TSSs and transcriptional noise. ADAPT-CAGE provides highly accurate experimentally derived TSSs on a genome-wide scale. It has been specifically designed for flexibility and ease-of-use by only requiring aligned CAGE data and the underlying genomic sequence. When compared to existing algorithms, ADAPT-CAGE exhibits improved performance on every benchmark that we designed based on both annotation- and experimentally-driven strategies. This performance boost brings ADAPT-CAGE in the spotlight as a computational framework that is able to assist in the refinement of gene regulatory networks, the incorporation of accurate information of gene expression regulators and alternative promoter usage in both physiological and pathological conditions. During the last decade, CAGE has been continuously refined and improved into its current mature form as a well-established protocol for the identification of transcription start sites (TSS) and promoter regions of transcribed loci. The FANTOM Consortium2 has extensively applied CAGE on hundreds of tissues and cell lines to produce a high-quality annotation of the human and mouse promoterome and characterize regulatory mechanisms of gene expression.Cap Analysis of Gene Expression (CAGE) was initially introduced in 20035 that besides promoter regions, CAGE also identifies capping sites along various locations of transcribed loci such as different splicing products, isoforms and capped molecules that can be summed up as transcriptional noise. As a result, only a portion of regions enriched in CAGE signal were found to overlap with the surrounding region of annotated TSSs.Despite its increasing popularity as an experimental promoter identification protocol, the specificity of CAGE regarding the identification of transcription initiation events in the genome has several limitations. There is strong evidencein silico methodologies have been developed to provide basic pipelines for analyzing CAGE datasets and to facilitate peak identification and annotation. PARACLU6 performs clustering of CAGE tags into wider regions based on a density parameter that reflects the trade-off between size and number of overlapping reads. RECLU7 is an adaptation of PARACLU algorithm that is able to handle replicated (maximum two replicates) experiments based on the irreproducible discovery rate (IDR) algorithm8 and utilizes slightly modified parameters to filter the final results when compared against the original implementation. CAGER is the most recent in silico framework for CAGE analysis and TSS identification9. CAGER applies quality filtering, and depending on the utilized protocol, removes the 5′ end G nucleotide bias. It subsequently clusters reads into groups and is able to handle multiple CAGE experiments, detect differential TSS usage while addressing the in-between tissue variability in TSS choice and promoter shifting. The common denominator between all the aforementioned implementations is that they can be applied directly on aligned CAGE reads and do not require any additional input such as DNA sequence or other in silico or experimentally derived features. TSS classifier2, that is part of TOMETOOLS suite, was trained to distinguish between TSS and non-TSS associated CAGE signal, based on the annotation of protein coding genes. The algorithm is based on Gaussian mixture models and a random decision tree ensemble to combine their output, trained to capture the relative distribution of 4-mer occurrences in TSS and non-TSS CAGE peaks.This poses a significant hindrance to research studies that aim to integrate regulatory regions into the framework of biological pathways. During the last decade, several 13. In a recent study10, the structural properties of DNA in promoter regions were investigated by analyzing 13 features including duplex disrupt energy, duplex free energy, bending stiffness, denaturation, stacking energy, bendability, propeller twist, z-DNA, A-philicity, nucleosome positioning, protein deformation, B-DNA twist and protein-DNA twist. Based on structural models derived from a wide array of biochemical experiments, sequences around transcription initiation events from DBTSS (positive set) were converted into numerical vectors and compared against their shuffled counterparts (negative set). It was shown that there are distinct patterns of structural DNA features that can distinguish between promoter and non-promoter regions. Based on these observations we hypothesized that the same features and their promoter patterns should be able to facilitate the classification of CAGE tag-clusters between real TSSs and transcriptional noise.Despite the important advances in the development of CAGE analysis pipelines, it is evident that existing implementations still include a high number of false positive rate on TSS identification in CAGE datasets. In various older studies, unique structural features were found to be associated with promoter regions10 associated with transcription initiation and Polymerase II promoter regions , thus belonging in the same promoter region, were merged into a unique event. The position with the highest coverage of reads was selected as a representative, resulting in 31,912 transcription initiation events. The occupancy of each histone mark was calculated in their surrounding region (+/−1 kb). The histone mark enrichment formed the basis for clustering these regions into groups with similar patterns of chromatin activity  for the enrichment of positively scored predictions in each chromatin state, in logarithmic scale Fig. . The OR To further explore the algorithms’ performance without using any third-party computational method, transcription factor binding sites (TFBS) were downloaded from the ENCODE Txn factor track that incorporates ChIP-Seq derived binding sites for more than 160 transcription factors. The overlap of each algorithm’s prediction with at least one TFBS in H9 cell lines was calculated Fig. . ADAPT-CThe last benchmark in the experimentally oriented group of comparisons was based on peaks derived from ChIP-Seq data against H3K4me3, a histone modification that is highly enriched at active promoters Fig. . CAGE enThe evaluation presented in the previous section was based on the output of ChromHMM, which utilizes histone mark ChIP-Seq data to annotate the genome with distinct cell-specific chromatin states. One limitation of this approach is related to the data quality and sequencing depth, parameters that might affect the accurate assignment of lowly expressed promoters to active chromatin. We sought to perform a more systematic evaluation of algorithms, one that relies on RefSeq protein coding gene annotation and H3K4me3 ChIP-Seq from H9 cells.2. In the second approach, we expanded the positive zone to +/−1 kb. CAGE peaks located in the negative zones that overlapped with H3K4me3 ChIP-Seq peaks were treated as positive instead of negative. This definition of positive and negative zones enabled the calculation of performance metrics such as specificity and sensitivity in H9 cell line. This hybrid strategy is able to remove certain obstacles that may arise in an annotation-only based evaluation. The main disadvantage is related to alternative promoter usage. Alternative promoters might frequently be located outside of the loci selected as positive zones, severely affecting the comparison results. The incorporation of H3K4me3 ChIP-Seq peaks in the process of generating the positives alleviates such effects.The region surrounding annotated TSSs (+/−50 kb) were selected to form the set of benchmark parts of the genome . Initially, all CAGE peaks located +/−500 bp around TSSs were considered as positive, while the ones located in the remaining parts of the benchmark regions were treated as negative Fig. . The samIn contrast to other algorithms, ADAPT-CAGE and TOMETOOLS are the only methods that provide a score to every processed CAGE enriched region. Therefore, by applying multiple score cutoffs, we evaluated ADAPT-CAGE and TOMETOOLS performance at each threshold. To perform a similar evaluation on the remaining algorithms, we applied them using different parameters at each run  as their first step and then apply filters of statistical nature among others. Besides TOMETOOLS, there is no Machine Learning involved in the pipeline of the aforementioned algorithms. However, the evidence in the literature shows that strong CAGE enrichment can also be found in non-promoter regions, therefore there is not a one-to-one association between high read-count CAGE peaks and transcription initiation events. That was exactly the type of evidence which lead to the development of ADAPT-CAGE and the application of Machine Learning for distinguishing between CAGE enriched regions that belong into the transcription initiation event class and other events or sequencing noise.This poses a significant obstacle to transcription-related research, signifying the need to develop computational methods for excluding events captured with CAGE that do not associate with TSSs. There are a few Evaluating and comparing the performance of algorithms with different properties and mechanisms is not a straightforward task. Our strategy was focused on generating benchmarking datasets that are unbiased against one algorithm or the other, as much as possible. To this end, the evaluation process involved, epigenetic experimental data indicative of active transcription, third-party Machine Learning software for annotating chromatin states, coverage in TF binding events and different combinations of protein coding gene related annotations.In truth, every evaluation approach offers certain advantages and disadvantages that, to some extent, might affect the comparison of algorithms. One of the disadvantages, in the annotated TSS based evaluation for example, is the definition of positive (+/−500 bp around the TSS) and negative zones . In this scenario, there is a possibility of alternative promoters being expressed in the selected negative zones, therefore affecting the comparison results. In reality, we will never be able to fully correct for such problems, and this is a fundamental issue that exists in every annotation-based evaluation approach. The pace of discovering new functional regions in the genome is set too high by Next Generation Sequencing, however the incorporation of de novo elements into the genome annotation is severely lagging behind. We attempted to alleviate the effects of this problem by following a hybrid strategy and combining protein coding gene annotation and experimental data derived from H3K4me3 ChIP-Seq.Despite all the obstacles described above, we believe that every type of evaluation is able to unveil properties of the tested algorithms that other approaches cannot. In fact, there are caveats in every evaluation strategy, and for this reason we decided to apply multiple approaches, attempting to complement the disadvantages of one strategy with the advantages of another.Several observations were made regarding existing algorithms, based on the evaluation results. Specifically, most algorithms involve stringent cutoffs in their pipeline resulting in the removal of valuable information from the CAGE tag-cluster set. The remaining tag-clusters exhibit high coverage in CAGE reads but that does not imply they are true positive transcription initiation events. Interestingly, PARACLU is performing better than the other existing implementations, despite its simplicity. On the other hand, ADAPT-CAGE is more sensitive and harnesses the power of Machine Learning on promoter-associated and structural DNA features to provide highly accurate predictions without removing the majority of candidate TSSs. It is able to perfectly complement research oriented around transcriptional mechanics by removing CAGE tag-clusters or genomic loci that are not associated with true TSSs.In light of Next Generation Sequencing, accurate computational methods such as ADAPT-CAGE can emerge as key components of studies that aim to identify the regulatory machinery and core constituents of biological pathways. This is a fundamental endeavor towards understanding and characterizing the underlying mechanisms that govern every aspect of physiological conditions in biological systems, as well as stimuli that cause the transition into pathological states.For the training and evaluation process we used CAGE samples already pre-processed and aligned on GRCh38 assembly by FANTOM. There are multiple ways to retrieve information from FANTOM repository, ranging from sam formatted alignments per sample to files consolidating all of the identified CAGE enriched loci with expression levels across the entire panel of profiled cell types. ADAPT-CAGE provides two modes for utilizing CAGE information in the algorithm (see Overview of ADAPT-CAGE section). Briefly, the first mode starts from CAGE sample bam files, creates peaks and identifies representative nucleotides for each peak. Subsequent steps of the algorithm are then based on these representatives. The second mode starts from bed formatted files of already identified peak representatives as specified by the user.2. The file named “hg38.fair + new_CAGE_peaks_phase1and2_tpm.osc.txt.gz”, which contains all characterized CAGE peaks  and their expression on all cell types, was downloaded from FANTOM repository alongside CAGE tag start site (CTSS) bed files related to these samples. The file including all identified CAGE peaks was filtered based on the expression level of peaks (tpm > 0) in H9 and K562 sample ids to create two sets of peaks (Supplementary Table The training of ADAPT-CAGE (see the relevant section for more details) was based on the aforementioned first mode of utilizing CAGE information that was applied on CAGE bam files from H1 cells with FANTOM ids CNhs14067, CNhs14068 and CNhs13964. For the evaluation process we used CAGE samples from H9 and K562 cells with FANTOM ids CNhs11917, CNhs12824, CNhs12824, CNhs12458, CNhs12684 and CNhs1278619 has been utilized as the reference annotation of coding and non-coding genes that were incorporated in the process of training the ADAPT-CAGE modules and evaluating all algorithms. For the TFBS-oriented evaluation, the genomic coordinates for TFBSs were downloaded from the ENCODE ‘Txn Factor’ track. This track incorporates a large collection of ChIP-Seq derived TFBSs from 161 TFs in 91 cell types.ChIP-Seq datasets in the aforementioned cell-lines were obtained from the ENCODE repository (specific ENCODE sample ids are shown in the following sections). NCBI RefSeq database16 repository. E008_15_coreMarks for H9 and E123_15_coreMarks for K562 were downloaded and converted to GRCh38 compatible coordinates. In case of predictions overlapping multiple chromatin states, we assigned the CAGE enriched region to the state with which exhibits the highest overlap. Additionally, H3K4me3 ChIP-Seq derived peaks with sample ids ENCSR716ZJH and ENCSR000EWA for H9 and K562 cells were used.The epigenomic data evaluation was based on the ChromHMM 15-states annotation . As expected, similar patterns were observed for the positive set of CAGE tag-clusters compared to the negative set. One characteristic example of A-philicity is shown in Supplementary Fig. 21. These motifs represent various promoter-related elements such as Inr, TATA-box, MTE, GC-Box, CCAAT-Box, DPE, BREu, BREd, DCE-S-I, DCE-S-II, DCE-S-III, XCPE1 and MED1. In this case, we also observed striking differences between the positive and negative set. Supplementary Fig. Following previous studies22 package in R . Details regarding the models’ training are presented in the following paragraphs.The performance of each structural feature fluctuated depending on the combination of the queried sequence size (window around CAGE tag-cluster representatives) and the smoothing parameter (sliding window size). The Polymerase II motif affinity was calculated using TRAP25, one for each structural DNA feature, as well as for training the SGB model for the Polymerase II associated motifs. Even though SVM models are theoretically resistant to the curse of dimensionality, the ratio of training samples versus sample dimension was approximately 6:1 to avoid any potential overfitting. Additionally, 10-fold cross validation (CV) was applied during the training process to optimize the generalisation properties of the final models. The second training set  formed the basis for training the SGB model that combines the results of the individual SVM models related to the structural DNA features. The third training set  was utilized to train the final SGB model that combines the output of the last tier of SGB models plus the normalized expression level in log10 scale. The final set that served as our benchmark consisted of 1,140 positives and 1,342 negatives. Supplementary Fig. CAGE reads, from H1 cells, with less than 10 mapping quality were removed. Reads were merged into peaks/tag-clusters if they were located closer than 50 bp. From the initial set of tag-clusters only those exhibiting a normalized expression level of more than 0.5 tpm were retained for downstream analysis .The assessment of the importance of the final structural and promoter feature models, as well as the normalized expression level for the classification task was performed with Boruta R package , H3K4me1 (ENCFF804YUX), H3K9me3 (ENCFF049TFL), H3K27me3 (ENCFF022SFF), H3K36me3 (ENCFF760NOJ) and H3K27ac (ENCFF271XBU) versus Input (ENCFF734TEC) in the +/−1 kb region surrounding CAGE tag-cluster representatives has been calculated with normR26. These enrichment results facilitated the k-means clustering of CAGE tag-clusters in 9 groups, following silhouette coefficient analysis (data not shown). The adjusted p-value was used as a proxy of the enrichment of each mark, to cluster CAGE peaks with similar chromatin states. For visualization purposes, the normalized histone mark ChIP-Seq as well as DNase-Seq (ENCFF110SPC) enrichment was also calculated for 10 bp non-overlapping bins spanning the +/−2 kb region centered on CAGE tag-cluster representatives. The same analysis was performed after applying ADAPT-CAGE with a score cutoff of 0.5 to the set of 31,912 CAGE enriched loci, separately to the positively  and negatively  scored regions  as well as promoter regions  and K562  CAGE peaks. This way, we generated TOMETOOLS predictions in H9 and K562 cells  from Size distributions of CAGE tag-clusters provided by each algorithm are presented in Supplementary Fig. The repeated application of existing implementations using different parameters, as described above, leads to the creation of overlapping sets of predictions based on a gradual increase in the certainty related to the quality (or robustness) of provided results. This approach, combined with the usage of multiple ADAPT-CAGE and TOMETOOLS score cutoffs, practically corresponds to the selection of different percentiles from the total amount of predictions of each algorithm . Additionally, since existing implementations are not based on Machine Learning, the approach of multiple runs based on different parameters, also corresponds to the strategy documented in FANTOM repository that selects CAGE peaks with respect to read count associated thresholding. This is also evident from the certain parameters that are supported by these algorithms that correspond to either raw or normalized read-count cutoffs.Supplementary Information.Supplementary table 1.Supplementary table 2.Supplementary table 3.Supplementary table 4.Supplementary table 5.Supplementary table 6.Supplementary table 7.