CCND1 in healthy and malignant B cells. After confirming that the model gives good predictions of Hi-C data for the nonmalignant human B cell–derived cell line GM12878, we generated predictions for two cancer cell lines, U266 and Z-138. These possess genome rearrangements involving CCND1 and the immunoglobulin heavy locus (IGH), which we mapped using targeted genome sequencing. Our simulations showed that a rearrangement in U266 cells where a single IGH super-enhancer is inserted next to CCND1 leaves the local topologically associated domain (TAD) structure intact. We also observed extensive changes in enhancer-promoter interactions within the TAD, suggesting that it is the downstream chromatin remodeling which gives rise to the oncogene activation, rather than the presence of the inserted super-enhancer DNA sequence per se. Simulations of the IGH-CCND1 reciprocal translocation in Z-138 cells revealed that an oncogenic fusion TAD is created, encompassing CCND1 and the IGH super-enhancers. We predicted how the structure and expression of CCND1 changes in these different cell lines, validating this using qPCR and fluorescence in situ hybridization microscopy. Our work demonstrates the power of polymer simulations to predict differences in chromatin interactions and gene expression for different translocation breakpoints.Genomic rearrangements are known to result in proto-oncogene deregulation in many cancers, but the link to 3D genome structure remains poorly understood. Here, we used the highly predictive heteromorphic polymer (HiP-HoP) model to predict chromatin conformations at the proto-oncogene  Chromatin structure, nuclear organization, and the epigenome are intimately linked to gene regulation and cell function and are tightly controlled during differentiation. Genome rearrangements leading to structural variants  can disturb these, leading to gene misregulation and cancer . An impocis-regulatory mechanisms; they are often bounded by binding sites for the CCCTC-binding factor CTCF  . These ator CTCF . ChIP-setor CTCF . Super-etor CTCF  and havetor CTCF .A full epigenetic and three-dimensional (3D) structural characterization of genome rearrangements is crucial for our understanding of proto-oncogene activation. However, the small size of patient samples and their inherent variability means that routinely applying methods like Hi-C to primary cancer samples remains challenging, despite recent progress in reducing the amount of material required . This isIGH) (CCND1 proto-oncogene encoding the cell cycle protein cyclin D1. IGH-CCND1 is a common translocation observed in mantle cell lymphoma (MCL) and multiple myeloma (MM); when translocated together, the IGH super-enhancers drive CCND1 overexpression. The goal was to use the model to shed light on how such rearrangements lead to changes in the 3D structure of gene loci which in turn leads to deregulation. If these simulations allow us to infer the pathway through which the CCND1 proto-oncogene becomes activated, they could be used to generate testable hypotheses to direct new experiments suggesting targets for therapeutic intervention. In the longer term, this points to a future role for the method in a clinical setting, for example, in personalized medicine, where structural predictions based on available or easily obtainable data could be extremely valuable.In this work, our aim was to show how computer simulations can help us understand the effects of common genome rearrangements in B cell malignancies. Specifically, we used our “highly predictive heteromorphic polymer” (HiP-HoP) model  to studyIGH)  and the IGH and CCND1 loci in healthy cells, as well as two different rearrangements which are studied here. Specifically, we consider an MM cell line, U266, where a single IGH super-enhancer is inserted into the CCND1-TAD, and an MCL cell line, Z-138, possessing a translocation which repositions CCND1 next to the IGH joining region  and single-cell measurements  . CrucialCCND1 lead to local changes in histone modifications  diffusing spheres representing complexes of chromatin binding proteins which can bind to multiple points at the same time to form molecular bridges   . The reghancers) ; (2) a hhancers) . Loop exhancers) , which ahancers) .Supplemental Table S1). In the model, we had two different types of chromatin structure (the heteromorphic polymer): more open regions  and more compact regions  . We used the chromatin states to identify these regions: H3K27ac associated states have the more open structure . Repressive protein binding sites were identified using the chromatin states, with one species binding to H3K9me3 states, and one species representing polycomb repressive complexes and binding to H3K27me3 states. Finally, ChIP-seq for CTCF was used to identify “anchor” sites where loop extrusion is halted. Full details of the simulations are given in Supplemental Methods, and a schematic is shown in Supplemental Figure S1.A way to identify different types of chromatin is via high-throughput analysis of histone modification data and hidden Markov modeling (HMM) ; this aptructure . The modCCND1, we generated simulated Hi-C interaction maps and compared them with publicly available data . The simulations gave a good prediction of the TAD pattern across the region . CCND1 sits at the far right of a TAD which is bounded by convergent CTCF sites, starts just to the left of TPCN2, and ends between LTO1 and FGF19. We shall refer to the three neighboring domains as the TPCN2-, CCND1-, and FGF19-TADs. The gene MYEOV sits just left of the center of the CCND1-TAD. More broadly, the dependence of interactions with genomic separation was correctly captured by the model , and we found good correlation with experiments using several different metrics  .To verify that HiP-HoP gives good predictions for Supplemental Fig. S2G) . In the simulated Hi-C, several smaller ‘subTADs’ are also visible; for example, one subTAD is generated by looping interactions between a CTCF site just upstream of MYEOV and a CTCF within the CCND1 gene body [green dashed lines in Simulated Hi-C maps tend to show more structure within the domains than experiments . Typically, these features arise in the model due to protein-mediated enhancer-promoter interactions. It could be that these interactions are overestimated in the model, for example, because parameters such as the number of bridging proteins are not correct, or because such interactions are disrupted by a mechanism which is not present in the HiP-HoP framework. Another possibility is that the experimental data lacks sufficient resolution to reveal these features. Many of the predicted ‘within-TAD’ interactions are present in publicly available HiChIP and ChIA-PET data which have higher resolution . A quanCCND1 promoter often interacts with polycomb-associated regions (arrow ‘i’ in the figure); this is because there is enrichment of H3K27me3 at the promoter. There are also ‘active’ H3K4me1/3 modifications, which could indicate variation between alleles or across the population, with the gene sometimes expressed and sometimes repressed. Consistent with this, publicly available RNA-seq data .A more focused view of chromatin interactions at gene promoters is shown in simulated 4C interaction profiles . We found that, in these cells, TPCN2 and LTO1 are expressed at slightly lower levels than CCND1; in simulations their promoters interact with several nearby DHSs. MYEOV shows much lower expression than the other three genes  and is covered by enhancer-associated H3K4me1 and H3K27ac states. Although there is no DHS at the MYEOV promoter to drive strong interactions, there is a general enrichment of interactions within a broad region downstream from the gene (arrow ‘ii’ in MYEOV (arrow ‘iv’) which interacts strongly with DHSs across the locus  . We obtained U266 DNA accessibility and chromatin states from the BLUEPRINT project  and usedCCND1 and within the IGH insert itself. Many of these are between DHSs, between regions with heterochromatin states, or between regions with polycomb states. DHSs often coincide with regulatory elements such as promoters and enhancers; particularly in U266 there is a cluster of DHSs near and within CCND1 which is not present in GM12878. Together, these observations suggest that loop extruders and CTCF sites drive TAD formation, whereas bridging proteins drive interactions within TADs. Extrusion is unchanged by the presence of the insert , whereas significant remodeling of chromatin in U266 compared to GM12878 leads to changes within the TAD. To validate this new TAD layout, we compared our simulated interaction map with Hi-C data for U266 cells obtained from Supplemental Fig. S4); however, there is a region with repetitive DNA sequence within the insert region to which short Hi-C reads cannot be mapped, and this leads to an incorrectly called TAD boundary. Nevertheless, the rest of the TADs in the data match those called from the simulated map, and there is good visual agreement . It is clear that CCND1 is indeed within the domain which encompasses the IGH insert, and its promoter shows enriched upstream interactions, validating the simulation predictions. Broader comparison measures also showed significant correlations between the simulation and experimental Hi-C, though the agreement was reduced compared to the GM12878 case (likely due to the lower resolution of the data) .The overall TAD structure is unchanged by the presence of the insert; however, interactions within the domains show significant differences between the two cell lines. Particularly, in U266 there are several strongly interacting regions , including close to CCND1 promoter and several DHSs across the TAD, including three prominent peaks within the insert , and within and around the nearby genes . Reciprocal interactions were observed as expected (CCND1 gains DHSs and active promoter and enhancer chromatin states at the promoter and across its body (while losing H3K4me1 and H3K27me3 at the promoter). These features are consistent with H3K4me3 broad domains, which have previously been associated with super-enhancers (CCND1) (nhancers . Their a (CCND1) .MYEOV also gains a DHS in U266 compared to GM12878, and the chromatin state changes such that H3K27ac and H3K4me3 now cover the gene, extending several kbp downstream (another broad domain). It also shows a number of new interaction peaks, particularly in a region downstream where there is a cluster of new DHSs with active enhancer and promoter chromatin state . Taken together, these observations suggest that this new MYEOV broad domain is active as an enhancer .The promoter of TPCN2 and CCND1 tends to be larger in U266 where the insert is present. This is expected because not only is the genomic separation  of the probes larger, but in the model, more of the region has the open (H3K27ac-associated) structure. The latter effect is highlighted by examining the 3D size of a region of fixed genomic length around CCND1 in the two cell lines: the 3D size is larger in U266 cells  and provides full three-dimensional details. We can also therefore measure, for example, separations between specific points or the overall 3D size of a given region. We find that the mean separation of CCND1: it relocates to Chromosome 14 where it becomes juxtaposed with the joining region of the IGH locus. Although the t rearrangement is common, the breakpoint has not previously been mapped in this cell line. Using paired-end read targeted DNA sequencing, we precisely mapped the chromosomal changes . With this information, we could then perform simulations of the rearranged genome, which we denote hg19_z138.The MCL cell line Z-138 harbors a translocation that dramatically changes the local environment of CCND1, LTO1, and the IGH super-enhancers . The right-hand boundary of the TAD is formed by a pair of divergent CTCF sites and is conserved from the Chromosome 11 boundary observed in GM12878 and other cell types. The left-hand boundary is formed by three inward pointing CTCF sites; a boundary is also seen at this position on Chromosome 14 in some other cell types which do not have a translocation  . One can also see a region within the TAD which shows stronger enrichment of iterations (a ‘subTAD’ of length ∼407 kbp) (α2 and a CTCF within CCND1. The latter is the same CTCF site which forms a subTAD in GM12878.l cells) . In summ407 kbp) B; this aCCND1 and several positions within the translocated IGH super-enhancers. CCND1 shows prominent interaction peaks in both Eδ and Eµ . There are also smaller interaction peaks within Eα1 and Eα2 and at a number of other DHSs . Similar to U266, there are a number of DHSs at the CCND1 promoter and within the gene body which are not present in the GM12878 cell line. In the simulations, the interactions of the promoter are driven by binding (and molecular bridging) of active proteins; this bridging is likely further promoted by CTCF/cohesion-driven loop extrusion. Viewpoints positioned at DHSs within the enhancers show reciprocal interactions with CCND1.CCND1). It is also possible to simulate alternative rearrangements to better understand the effect of breakpoint variation between patients. Up to 50% of t MCL breakpoints are within the so-called ‘major translocation cluster’ about 120 kbp upstream of CCND1 . In both U266 and Z-138, the CCND1 gene body gains several DHSs and an H3K4me3 broad domain appears. From 10 distinct DHSs within CCND1, five are common to both cancer cell lines, three are specific to Z-138, and two are specific to U266. In other words, the different rearrangements lead to similar, but not identical, changes to the chromatin structure at CCND1. In U266 cells, two other regions within the TAD also show substantial changes: a region covering and extending downstream from MYEOV and a region between MYEOV and CCND1 .The changes to chromatin states and DNA accessibility at CCND1 have a striking effect on the 3D structure of the gene: In Rg) . In GM12878, the gene is on average more compact than in the other cell lines. The snapshots in Rg. The snapshot for the Z-138 case shows a configuration where a loop forms between a DHS within the gene body and a DHS in the upstream region. Because there are more DHSs across the gene in Z-138, there are more possibilities for loops to form: smaller Rg values are therefore more likely and there is less variation.The changes to the chromatin states at Supplemental Fig. S3B). Whereas all of the genes show an increase, that of CCND1 is by far the largest: more than 500-fold. The next largest change is for LTO1 which shows an ∼9.8-fold increase. We considered whether these changes could be predicted from the simulated structures. In our simulations, the active proteins represent complexes of transcription factors and polymerases, and one might therefore associate binding of these to a gene promoter site with transcription. Indeed, in previous work using a simpler model, we showed that the fraction of time a promoter is bound by an active protein during the simulation is correlated with gene expression measured experimentally via GRO-seq . An even better correlation between simulations and data is obtained if, rather than considering protein binding, we instead ask how often the promoter interacts with (forms loops to) chromatin which has an enhancer-associated state ; LTO1 and TPCN2 also show increased expression in Z-138 compared to GM12878 cells, but we did not detect MYEOV expression. Only CCND1 and LTO1 were present within the region simulated, and simulations correctly predicted the former would display the greater fold change compared to GM12878 cells .In Z-138 cells, Supplemental Methods). We note that the probes cover a region several times larger than the CCND1 gene body (42.8 kbp compared to the 13.4-kbp gene), so this is an indirect measure. CCND1) probe is alone, whereas the other red and green probes  are together. From the images, we obtain the 3D volumes of the probes; a similar measure is obtained from the simulations. The simulations predict that compared to the GM12878 cell line, the volume occupied by the CCND1 probe region will be, on average, 30% larger in U266 cells and on average, 16% larger in Z-138 cells. They also predict that the MYEOV probe volume will be, on average, 36% larger in U266 cells compared to GM12878, whereas the TPCN2 probe will be the same volume in both of these cell lines. Experimentally, we obtained measurements from at least 20 cells for each cell line  . The data showed trends which were generally consistent with the simulations: the CCND1 probe volume was, on average, 30% larger in U266 compared to GM12878, and the MYEOV probe was, on average, 23% larger. However, in the experiments the differences were not statistically significant; nevertheless, the simulations are still consistent with this, as taking a random subset of the simulation measurements such that there were the same number as in the experiments also did not show any significant differences between the probe volumes in the different cell lines . Plots showing the distributions of the CCND1 probe volumes for experiments and simulations are shown in Supplemental Fig. S10 shows similar plots for the other probes). We note that although the simulations are consistent with the in vivo trends, we would not expect the model to be able to predict quantitative differences in probe volumes; this is consistent with previous work where predictions for 3C-based data (interactions) were better than those for microscopy data  microscopy using fosmid probes . On the other hand, there is some interaction between the insert and LTO1. Using the simulations to predict gene expression as above, these results suggest that without any subsequent chromatin remodeling, the presence of the insert alone would not lead to CCND1 up-regulation. This is in contrast to the measured change in RNA levels via qPCR , which show ∼500-fold and 1200-fold increases in CCND1 expression in U266 and Z-138, respectively, compared to GM12878.CCND1 promoter still shows interaction peaks across the CCND1 gene body, at regions downstream (in and around LTO1) and to a lesser extent, the H3K4me3 broad domain regions upstream. Beyond the loss of interactions with the insert itself, the main effect of removing the insert is an increase in interactions between CCND1 and upstream DHSs, consistent with their reduced genomic separation. The simulations also predict that very little change in gene expression level would result from removing the insert while keeping the U266 chromatin states. Together these results suggest that it is chromatin remodeling which drives the changes in the 3D structure of the locus in terms of promoter-enhancer interactions, and this in turn drives up-regulation of cyclin D1 . In a simulation of the U266 cells with the insert where we ‘switched off’ protein binding at four DHSs within the promoter and gene body of CCND1, there was a loss of CCND1 interactions compared to the previous U266 simulations .As well as simulating different rearranged genomes, it is also possible to directly edit the input data; that is, we can edit the epigenome in silico. For example, we found little change in In this work, we have adapted the HiP-HoP simulation model to study chromatin 3D structure and the effect of genome rearrangements in malignant and nonmalignant B cells. HiP-HoP simulations predict 3D structures from DNA accessibility, chromatin states, and CTCF binding data. We used these structures to simulate Hi-C and 4C data and to make single cell-like measurements. Crucially, Hi-C data are not used as an input, so in this sense the model is truly predictive for 3D structures. Here, by “rearranging” the input data, we generated predictions for the effect of genome rearrangements which are found in MCL and MM cell lines.CCND1 gene locus by performing simulations of the healthy B cell–derived GM12878 lymphoblastoid cell line. The TAD pattern observed in Hi-C , and it may be that predictions could be improved by optimizing the parameters for this specific locus.We first confirmed that the HiP-HoP model gives good predictions for the  in Hi-C  is clearous work  showed tCCND1 is preserved in the U266 MM cell line which possesses a rearrangement where a super-enhancer from the IGH locus is inserted upstream of CCND1. This is consistent with previously published low-resolution Hi-C data in that cell line . The simulations predicted that the CCND1 promoter interacts with these new DHSs in the gene body, as well as with a number of DHSs within the inserted IGH region and the neighboring gene LTO1 (interactions which are not present in GM12878 cells).Further simulations predicted that the TAD structure around ell line . In thesCCND1 translocates with Chromosome 14 becoming juxtaposed with the IGH Eµ enhancer. In this cell line, a TAD boundary formed by a cluster of CTCF binding sites downstream from CCND1 was preserved after the translocation, as was a boundary to the left of the IGH super-enhancers . In other words, the simulations predict a fusion of the CCND1 and IGH regions into a new TAD—an oncogenic TAD fusion  showed only very minor changes in terms of 3D structure compared with the unaltered genome. We also performed a simulation using U266 chromatin states but without the insert . This showed little change compared to the U266 case with the insert. Together, this suggests that it is the local chromatin remodeling, rather than the proximity of CCND1 to the IGH enhancers per se, which drives gene deregulation. Or, in other words, a genomic translocation leads to an epigenomic translocation, which drives cyclin D1 overexpression. The remodeling includes the appearance of an H3K4me3 broad domain over CCND1; the results here support our previous work suggesting that such broad domains are associated with hijacked super-enhancers . The chromosomal locations which are predicted to strongly interact with CCND1 could be used for targeting in future experiments which aim to uncover the mechanisms through which broad domains are generated. For example, one could ask whether using CRISPR-dCas9 to tether KRAB or another enzyme at the U266 insert is sufficient to reverse the nearby epigenetic changes.In vivo, genomic rearrangements are accompanied by chromatin remodeling; within our simulation scheme, it was also possible to examine the effect of a rearrangement in the absence of remodeling. Simulations of GM12878 cells with the Enhancers . In U266CCND1 loci. We note that this is a limitation of the input data rather than the simulation scheme; if the full sequence of a given cell line or patient sample were available, ChIP-seq reads could be aligned in an allele-specific way. It may also be possible to use the simulations to explore hypotheses about differences between alleles; for example, if one were to assume one copy of the locus has active chromatin states and the other inactive states, how does the 3D structure differ? A simplifying assumption in the model is that all DHSs are binding sites for a general active factor, and it would be interesting to incorporate more detail in the future, for example, if transcription factors specific to a particular oncogene are known , and we evolve the configuration of this chain using a molecular dynamics scheme and the LAMMPS simulation software , then treated with M-MLV Reverse Transcriptase (Promega). Two micrograms of cDNA reaction were amplified with SYBR green master mix (Invitrogen), 10 µM forward and reverse primers for LTO1, TPCN2, CCND1, and MYEOV (Sigma-Aldrich) with 40 cycles of PCR  after initial denaturation (95°C for 10 min). Cycle threshold values were normalized by comparison with GAPDH expression. See Supplemental Methods for further details .For gene expression measurements, total RNA was extracted using an RNeasy kit (QIAGEN) from between 5 × 10Supplemental Methods.Breakpoint mapping in U266 and Z-138 cells was performed as described previously , 2022; fTPCN2 (G248P87917D11/WI2-1721H21), MYEOV (G248P87014D2/WI2-2222G4), and CCND1 (G248P86668E2/WI2-2191I3) loci were obtained from BACPAC resources. DNA was fluorescently labeled by nick translation. Probes and cell line DNA were denatured at 75°C for 5 min followed by hybridization at 37°C overnight. Further details are given in Supplemental Methods.For FISH microscopy, briefly, fosmid clones found to cover https://www.ncbi.nlm.nih.gov/bioproject) under accession number PRJNA635269. The FISH and simulation data generated in this study have been submitted to the Edinburgh DataShare repository (https://datashare.ed.ac.uk/handle/10283/4433). See also Supplemental Table S4.All raw and processed sequencing data generated in this study have been submitted to the NCBI BioProject database (


We apply the model to predict genetic context effects, design σ70 promoters with desired transcription rates, and identify undesired promoters inside engineered genetic systems. The model provides a biophysical basis for understanding gene regulation in natural genetic systems and precise transcriptional control for engineering synthetic genetic systems.Transcription rates are regulated by the interactions between RNA polymerase, sigma factor, and promoter DNA sequences in bacteria. However, it remains unclear how non-canonical sequence motifs collectively control transcription rates. Here, we combine massively parallel assays, biophysics, and machine learning to develop a 346-parameter model that predicts site-specific transcription initiation rates for any σ Transcription rates are regulated by the interactions between RNA polymerase, sigma factor, and promoter DNA sequences in bacteria. Here the authors combine massively parallel experiments & machine learning to develop a predictive biophysical model of transcription, validated across 22132 bacterial promoters, and apply it to the design and debugging of genetic circuits. However, while DNA assembly techniques enable the construction of custom-designed genetic systems4, it remains challenging to a priori predict and control a system’s gene expression profile5, for example, by initiating transcription with desired rates at specific DNA start sites, while minimizing transcription from all other DNA sequence regions. Currently, transcriptional control relies on empirical characterization of promoters as modular genetic parts6. Applying modular design to transcriptional control ignores other sources of transcription, for example, inside coding regions, as well as local and long-distance interactions that alter transcription rates and start sites in unexpected ways7. Poorly controlled transcriptional profiles can lead to malfunctioning genetic systems, including the undesired production of anti-sense RNA and truncated proteins as well as the misbalancing of protein expression levels that lead to lower system activities9.Transcription is the gene expression process responsible for producing all RNA and is a common engineering target for creating novel products, including microbial chemical factories, toxin-sensing genetic circuits, and mRNA vaccines11. To address this challenge, researchers have characterized thousands of promoters in vivo using high-throughput cloning and developed models ranging in predictability17. Despite these efforts, it remains unclear how the strengths of multiple interactions26 collectively determine transcription initiation rates and start sites, particularly when bound to RNAP/σ70, which initiates transcription at the majority of bacterial promoters. Furthermore, it remains a challenge to accurately measure the strength of these interactions while taking into account differences in mRNA decay rates28, the presence of cryptic transcriptional start sites (TSSs)34, and incomplete self-cleavage by insulating ribozymes37.A key challenge is to quantitatively predict how polymerase initiation complex—RNA polymerase (RNAP) and a sigma factor (σ) in bacteria—interacts with arbitrary DNA sequences70 promoters  an upstream 6-nucleotide site called the −35 motif; (ii) a 20-nucleotide region that appears upstream of the −35 motif, called the UP element; (iii) a downstream 6-nucleotide site called the −10 motif; (iv) a spacer region that separates the −10 and −35 motifs; (v) a 4-nucleotide site upstream of the −10 motif, called the −10 extended motif (−10 ext); (vi) a typically 6-nucleotide region in between the −10 motif and TSS, called the discriminator (Disc); and (vii) the first 20 transcribed nucleotides, called the initial transcribed region (ITR).To begin, we designed 14,206 promoters with varied sequence motifs to systematically perturb the interactions that affect RNAP/σion Fig. . These i70 binding to a promoter is controlled by the interaction Gibbs free energies (ΔG) at the UP, −35, −10 extended, and −10 motifs as well as the torsional stress controlled by the length of the spacer region41. Bound RNAP/σ70 then undergoes a conformational change that catalyzes double-stranded DNA separation, creating a transcription bubble that initially encompasses half the −10 motif, the Disc, and the first two nucleotides of the ITR40. RNA polymerization begins at a TSS determined by where the catalytic site in the β subunit contacts the DNA template, canonically at position +1. The transcription bubble is then stabilized by interactions in the Disc22 and the formation of an R-loop, whereby the newly synthesized RNA strand immediately hybridizes to the DNA template42. The ITR sequence controls the R-loop’s thermodynamic stability. Finally, transcription initiation is successful once enough DNA is pulled into the stable transcription bubble that the accumulated stress exceeds the interaction strength between RNAP/σ70 and promoter DNA, causing promoter escape and a transition to processive RNA synthesis43.Initial RNAP/σ70 to all DNA and the multiple sequence contacts that control RNAP/σ70 recruitment at each promoter (Methods). So long as the internal states do not become abundant, for example, by significant transcriptional pausing44, the model indicates that we can decompose how a promoter’s sequence controls the interaction energies into a sum of free energies that can be related to the transcription initiation rate (TX), according to:ΔGtotal is the difference in free energy between an unbound promoter and a promoter- RNAP/σ70 complex with a stable transcriptional bubble, which is used to predict a promoter’s TX rate in comparison to a reference promoter sequence with calculated ΔGtotal,ref and measured TXref. β is a measurable model constant that converts free energies into state probabilities. Here, we arbitrarily set ΔGtotal,ref to zero so that stronger and more favorable interaction free energies have more negative values as compared to the reference promoter, which has a low transcription rate.From these interactions, we formulated a statistical thermodynamic model of transcriptional initiation that accounts for competitive binding of RNAP/σcoli genome, while eliminating cryptic hexamer motifs and restriction cut sites. The changes to the baseline promoter sequence included all possible −10 hexamer motifs (46 = 4096 variants), all possible −35 hexamer motifs (46 = 4096 variants), all possible −10 extended motifs placed within 8 combinations of consensus and anti-consensus hexamers (44 × 8 = 2048 variants), 229 spacer sequences with varied lengths and nucleotide compositions, 605 UP sequences with varied AT content placed within 4 combinations of consensus and anti-consensus hexamers (605 × 4 = 2420 variants), 735 discriminator variants with varied lengths and GC content, and 582 ITR variants with varied R-loop stabilities and pause elements . Barcodes were designed to have pair-wise Hamming distances of 2 or greater. To avoid potentially confounding effects, we used the two-step cloning procedure to position the barcodes within the 3′ untranslated region.We designed the 14,206 promoter sequences to measure how each motif sequence alters these free energies to create a sequence-complete model. The baseline promoter sequence contained consensus hexamers, consensus spacer length, and an optimized background sequence generated to match the G/C content of the E. nts Fig. . Using o70, the barcoded plasmid pool, NTPs, and buffer (Methods). TSS mapping was performed by harvesting product RNA, ligating a 5′ RNA adapter, converting to cDNA, circularizing, using PCR to generate amplicons containing the barcode and TSS, and obtaining over 323 million barcode-TSS mapped reads from Illumina sequencing. Across all promoters, we found 182 TSSs with at least 1000 mapped reads, revealing a primary TSS region and a less frequent, secondary TSS region arising from a downstream cryptic promoter. TX rates were then measured by carrying out DNA-Seq and RNA-Seq on triplicate in vitro transcription reactions, separately harvesting DNA and RNA, converting RNA to cDNA, using PCR to generate barcode-containing amplicons, and obtaining between 81.3 and 130.5 million barcoded reads per reaction. From the RNA/DNA read count ratios, we obtained TX rates for 13480 promoter variants (mean and standard deviation), excluding any with fewer than 50 read counts in any replicate. Replicate read count measurements were highly reproducible , carried out tenfold cross-validation to identify the optimal hyperparameters of a machine learning model, and tested its accuracy on the remaining unseen test set . We evaluated several models ; (2) 10898 promoters using combinations of motifs to express a ribozyme-insulated transcript, characterized using genome-integrated test circuits inside E. coli cells 16; (3) 4350 non-repetitive promoters using highly diverse sequences to express a transcript, characterized using test circuits carried on multi-copy plasmids inside E. coli cells 12; and (4) 1493 inducible promoters that contain transcription factor binding sites and express a ribozyme-insulated transcript, also characterized using genome-integrated test circuits inside E. coli cells 17. We first confirmed that the statistical thermodynamic model accurately predicted free energies of the in vitro data collected by LaFleur et al. without utilizing TSS measurements  5391 designed promoters with high-precision TX rate and TSS measurements, characterized here using in vitro RNAP/σE. coli cells, utilizing RT-qPCR to measure their mRNA levels and flow cytometry to measure their fluorescent reporter protein levels. We found that the model’s predictions for these promoters were highly proportional to their measured mRNA levels  and fluorescent protein expression levels  over a 78640-fold dynamic range , but had similar accuracies when tested on the LaFleur and Hossain test datasets  as compared to the linear free energy model . When measuring accuracy using MAE or MSE, the non-linear model had lower accuracies on the LaFleur and Hossain test datasets  that can occur between the six promoter regions, which added 30 unknown coefficients. We then applied linear regression to determine the values of these coefficients, followed by model testing. When measuring accuracy using Pearson 6. However, the model predicts that the DNA sequences surrounding a promoter will greatly affect its TX rate, specifically the UP and ITR regions, which would explain why some promoters express certain proteins at much lower levels. We next tested the model’s ability to predict, explain, and overcome these genetic context effects. First, we designed UP and ITR sequences that the model predicted would greatly alter a promoter’s TX rate, inserted them around the commonly used J23101 promoter, and measured their in vivo TX rates using a fluorescent protein reporter inside E. coli cells (Methods). Changing only the UP region or only the ITR region reduced the promoter’s TX rate by up to 5.3-fold, while changing both regions reduced the promoter’s TX rate by up to 9.4-fold , and found that the model’s predictions were highly accurate  element, the discriminator, the spacer region, and the ITR with validated predictions across 22,132 diverse promoter sequences. We then applied the model to design synthetic promoter sequences with targeted transcription initiation rates and to debug sources of cryptic transcription in engineered genetic systems. The model provides a more complete understanding of how both canonical and non-canonical motifs collectively control transcription rates across all potential DNA sites, yielding the transcriptional profile of a genetic system.We developed a biophysical model of RNAP/σ70 enzyme, we eliminated several competing interactions that routinely confound in vivo transcription rate measurements. For example, our assays did not contain alternative σ-factors or RNases, resulting in mRNA level measurements that were solely controlled by RNAP/σ70 interactions during transcription. Second, by employing oligopool synthesis, we were able to rationally design the thousands of promoter sequences needed to perturb individual interactions and measure their effects on transcription rates. Third, we designed all barcodes to have dissimilar sequences (pair-wise Hamming distances 2 or greater) to avoid the possibility of mis-mapping barcodes during data analysis, which would lead to non-random biases in the transcription rate measurements. Finally, the transcription start site mapping was found to be essential to pinpointing sequence motifs for model training, particularly for weaker promoters that often exhibited multiple transcription start sites. Altogether, our experimental design, carried out in biological triplicate, yielded a firm ground truth on which to build and test the predictive model. Notably, our in vitro experimental approach can be readily extended to other sigma factors and organisms to measure the interaction strengths between their respective RNAP/σ complexes and double-stranded DNA. However, our in vitro transcription assays did not contain transcription factors and did not measure regulated transcription, although it is feasible to add purified transcription factors to study their regulatory effects.Careful experimental design was needed to maximize the information content of our transcription rate measurements. First, by carrying out in vitro transcription assays using only RNAP/σ70-DNA interactions , followed by the in vivo datasets .We also tested our model accuracy on several in vivo datasets. Overall, we found higher model accuracy when the in vivo dataset’s experimental design yielded more precise transcription rate measurements. For example, Urtecho et al. integrated all test genetic circuits into the 50. To assess this effect, we applied the RBS Calculator5 to predict the translation initiation rates of all mRNAs and then analyzed model accuracy on systems that exhibited high translation rates where mRNA decay contributes the least. We found that model accuracy improved in both cases . However, this mono-nt model had lower accuracies on the LaFleur in vitro dataset as well as the Urtecho and Hossain in vivo datasets (Supplementary Data R2 = 0.69) and was similar or lower on other datasets  characterization of promoters with randomly dispersed mutations or random sequences. This model calculates the proportion of time RNAP/σ spends bound to a promoter sequence using an energy matrix, which is then related to the measured transcription rates using several fitted parameters, including an apparent chemical potential, an effective slope, an effective intercept, and an optimized detection threshold. Overall, when carrying out equivalent data analysis (Methods), our model achieves higher accuracy on the LaFleur, Urtecho, and Hossain datasets  biophysical model of bacterial transcription initiation with demonstrated accuracy across thousands of σ70 promoters were rationally designed to include 2420 UP elements, 4096 −35 hexamers, 229 spacers, 2048 −10 extended motifs, 4096 −10 hexamers, 735 discriminators, and 582 ITRs. The 2420 UP element sequences were designed in two subsets: (i) the first set varied adenine and thymine (AT) content from 0 to 100% within a background sequence containing combinations of consensus, anti-consensus, and mutated −10 and −35 hexamers; and (ii) the second set introduced short cytosine and adenine repeats into the distal and proximal binding sites, while varying AT content within a background sequence containing the same combinations of consensus, anti-consensus, and mutated hexamers. The −10 hexamer set was designed to include the 4096 6 nt sequences in the −10 hexamer, a consensus −35 hexamer, and a 17-bp spacer. The −35 hexamer set was designed to include the 4096 possible 6 nt sequences in the −35 hexamer, a consensus −10 hexamer, and 17 bp spacer. The 229 spacers were designed by varying the spacer length from 1- to 32 bp, in between a consensus −35 and a consensus −10 hexamer, while varying the spacer nucleotide composition at each length. The −10 extended promoter variants were designed to include the 256 possible 4 nt sequence at positions −14 to −17 within 8 different background sequences containing combinations of consensus, anti-consensus, and mutated −10 and −35 hexamer sequences. The 735 discriminator variants were designed in two sets with a background sequence containing a consensus −10 hexamer: (i) a first set that varied the guanine-cytosine (GC) content from 0 to 100% and the discriminator length from 6 to 8 bp with 5 randomly generated sequences satisfying each criterion; and (ii) a second set that introduced all possible 3 nt sequences in the first half of the discriminator and varied the GC content in the remaining 4 nt. The 582 ITR variants were designed to vary the number of purine bases, the GC content, and the presence of −10 hexamer-like pause sequences within the ITR. Unless otherwise stated, promoter variants were designed within a background containing consensus hexamers and a canonical 17 bp spacer. A complete list of the promoter sequences is available in the Supplementary Data 14206 σk-mer list generated from the 14206 designed promoters and the E. coli genome. Primer binding site sequences were designed to have target melting temperatures of 55 °C, low probabilities of forming primer dimers, and minimal primer structure. Primer binding site sequences were also designed with maximum dissimilarity to barcode sequences, promoter sequences, and the E. coli genome sequence.An oligopool containing a mixture of 170 nt oligonucleotides was synthesized (Genscript). Each oligonucleotide design contained a promoter variant, four restriction enzyme cut sites, two primer binding sites, and a unique 20-nucleotide barcode sequence . All ligations used T7 DNA ligase (NEB) unless otherwise stated. Transformation recovery was 60 min at 37 °C using 1 mL of pre-warmed SOC media. Transformation efficiency was 1.2e107 CFU per mL recovery broth. The transformed cell library was used to inoculate duplicate cultures using 5 mL LB media supplemented with 50 ug/mL chloramphenicol. Shaking cultures were incubated overnight at 37 °C. One culture was used for cryostocking. Plasmid purification was performed on the other culture using an EZNA plasmid mini kit (Zymo). The plasmid library was sequentially digested with SpeI and EcoRI, followed by rSAP treatment. Digested plasmid pool was then purified via gel extraction.PCR was carried out using the oligopool as DNA template to create an insertion cassette library, using Q5 High-Fidelity DNA Polymerase (NEB), 20 cycles, and primers 1 and 2  and mRFP1 coding sequence . DNA was removed by adding 2.5 units of TURBO DNase and incubating at 37 °C for 30 min. The RNA product was purified using a RNA Clean & Concentrator Kit (Zymo).Replicate in vitro transcription reactions were carried out for 3 h at 37 °C, combining 1 ug of the expression plasmid library, 0.5 mM of each NTP, 1X For quantification of RNA variant levels after transcription (RNA-Seq), cDNA first-strand synthesis reactions were first carried out on the harvested RNA, using 200 units of SuperScript IV (Invitrogen), 1X buffer, and transcript-specific primer 2  to remove 5′ phosphates, purifying, ligating the RNA to an 5′ RNA adapter  promoter variants must have with at least 50 RNA-Seq and 50 DNA-Seq counts across all triplicate measurements; (ii) promoter variants must have one predominant TSS with at least twice the counts as the next most predominant TSS; and (iii) promoter variants must have a predominant TSS within a 10 bp window surrounding the anticipated TSS location. Data filtering yielded 5193 promoter variants that satisfied these criteria. For model testing, the set of promoter variants with high-precision transcription rate measurements was defined using a single criterion: all promoter variants must have coefficients of variation (CVs) that are <0.40. CVs are calculated by determining the standard deviation of the transcription rate measurements and dividing by the mean of the transcription rate measurements across the three independent measurements. Data filtering yielded 5388 promoter variants that satisfied this criterion.70, which is the predominant RNAP/σ complex during exponential growth conditions. In our in vitro transcription assays, only one σ-factor (σ70) is present in the reaction, enabling us to precisely measure transcription initiation rates from RNAP/σ70 binding events.Inside a cell or in vitro transcription reaction, free RNAP and σ-factor rapidly bind to form RNAP/σ complex. Whenever multiple σ-factors are present, they compete against each other to bind RNAP with the final concentrations of each RNAP/σ controlled by the σ-factor concentrations and their respective binding affinities. The RNAP/σ complexes then compete for binding to all available double-stranded DNAs in order to initiate transcription. This competition is enhanced as most copies of RNAP/σ are already bound to DNA and engaged in transcriptional elongation, leaving much fewer copies of RNAP/σ free for binding to DNA sites. Several factors enable us to assume that these binding interactions rapidly reach chemical equilibrium, including (i) the total amount of σ-factor is in excess as compared to the amount of freely available RNAP, leading to most free RNAP being bound by a σ-factor; (ii) the number of DNA binding sites is much higher than the number of freely available RNAP/σ complexes; and (iii) rapid RNAP turnover after transcriptional termination replenishes the supply of free RNAP and RNAP/σ complex. Accordingly, we can apply statistical thermodynamics to derive an equation that relates the transcription initiation rate of a DNA sequence region to the binding free energy of RNAP/σ. Equation P copies of double-stranded DNA  and R copies of freely available RNAP/σ70. On that DNA, the sequence region (upstream and downstream) around nucleotide position i controls how well it binds to RNAP/σ70 and initiates transcription. Pi is the copy number of DNA at position i. TXi is the transcription initiation rate that begins producing a mRNA transcript at a TSS located at position i. Here, we formulate a model for predicting the transcription initiation rate in the forward direction (TXFi) given arbitrary DNA sequences surrounding position i. We then apply the same model on the reverse complement of the double-stranded DNA to predict the transcription initiation rate in the reverse direction (TXRi). As the model depends on non-symmetric amounts of upstream and downstream sequence centered on position i, the predictions TXFi and TXRi are two separate calculations.Consider a genetic system with ΔGtotal).We consider the process of prokaryotic transcriptional initiation at a single promoter as a two-state system. In state 1, RNAP/σ is not bound to the promoter DNA (free state). In state 2, RNAP/σ is bound to the promoter DNA with a stable transcriptional bubble, which includes the transition from the closed-to-open conformation, the initial melting of promoter DNA to create an unstable transcriptional bubble, and the formation of a stable transcriptional bubble (R-loop) via initial transcription of the ITR region. For many promoters, RNAP/ σ does not engage in long-lived transcriptional pausing, enabling us to describe the system as having two-states. However, in a subset of promoters, it is possible to have long-lived intermediate states comprising of partially melted DNA bubbles or unstable scrunched R-loops, which could alter transcription rates. Here, the use of massively parallel measurements does not provide the ability to measure the presence of intermediate states, preventing us from incorporating their presence in a model. Considering the thermodynamics of the two-state system, we compare the Gibbs free energy between the initial state (state 1) and the final state (state 2) to calculate the total change in the Gibbs free energy (R is the amount of available (free) RNAP/σ70, Ci is the number of promoter regions bound by RNAP/σ70, Pi is the copy number of DNA at position i, and ΔGtotal,i is the total change in free energy of RNAP/σ70 at a binding site that results in transcription with start site at position i. The total amount of available RNAP/σ70  is the sum of the free/unbound RNAP/σ70 and the RNAP/σ70 that is bound to DNA, which is:The chemical reaction and equilibrium condition for each DNA sequence region is therefore:R in terms of Rtotal, giving:Which can be re-arranged to solve for Ci and the total change in Gibbs free energy for RNAP/σ70 at each potential DNA binding site in the genetic system.Z) that takes into account the competitive binding of RNAP/σ70 to all potential DNA sites. When the genetic system is a bacterial cell, the summation to calculate the partition function Z takes place over all chromosomal and plasmid DNA inside the cell. When the genetic system is an in vitro transcription assay containing thousands of plasmid DNA variants (each variant having a different promoter sequence), the summation takes place over all unique plasmid DNA variants. In both scenarios, the value of the partition function Z is a large number that depends on the characteristics of the entire system  and not the specifics of a single promoter sequence that is being altered.Substituting Eq. th start site (TXi) proportionally increases with the number of RNAP/σ70 bound at that site (Ci), which is:The transcription initiation rate at the iPi. Rather than trying to predict, measure, or estimate Rtotal and Z, we can instead carry out a comparative analysis by selecting one promoter from each dataset and labeling it as the reference promoter. The reference promoter is characterized under the same conditions as the studied promoters such that the number of free RNAP/σ  and partition function values (Z) are the same. The reference promoter’s measured transcription initiation rate is labeled TXref and has a constant value. The model’s predicted total change in free energy for RNAP/σ70 is ΔGtotal,ref. We then take the ratio between TXi and TXref and simplify, yielding:Measured TX rates for each dataset were normalized by promoter copy number using DNA-seq, removing the dependency of the model Eq. ΔGtotal from sequence information. Our model considers each sequence-dependent interaction and then sums them together as linear, additive contributions. The free energy model is:ΔG−10 is the contribution from the −10 hexamer sequence, ΔG−35 is the contribution from the −35 hexamer sequence, ΔGspacer is the contribution from the DNA spacer’s length and rigidity, ΔGdisc is the contribution from the discriminator sequence, ΔGITR is the contribution from the ITR sequence, and ΔGUP is the contribution from the UP sequence.We then formulate a free energy model to calculate ΔGtotal,ref to zero. Therefore, the model is being trained to predict the differences in ΔGtotal between the promoter variants and the reference promoter, which leads to an interaction energy scale that spans both negative and positive values. In general, interaction energies have negative values when they are stronger than the average interaction strength. Interaction energies have positive values when they are weaker than the average interaction strength.The choice of a reference promoter is somewhat arbitrary, but it should have a well-measured transcription rate. Here, we selected a reference promoter with a measured transcription rate closest to the log-mean-center of the dataset. Without loss of generality, we also set We used the Utrecho et al. data for training the non-linear free energy model. For each promoter in the dataset, we used our linear free energy model to scan and predict the energy contribution for each of the promoter motifs considered in this study. These include (i) the UP element; (ii) the −35 hexamer; (iii) the spacer sequence, including the −10 extended; (iv) the −10 hexamer; (v) the discriminator; and (vi) the initially transcribed region. We then accounted for inter-motif interactions by extending Eq. Machine learning was used to train, test, and validate several linear models  and parameterize the unknown interaction energies. To do this, we first enumerated a list of sequence motifs and biophysical characteristics (Supplementary Table 2). Feature importance was then carried out using one-feature drop analysis. Model training and testing was repeated several times, each time dropping individual features or sets of related features. The differences in model accuracy were calculated and features were pruned if they did not appreciably increase model accuracy. Feature drop analysis identified several features whose values were highly correlated with other features in the dataset, for example, AT content in the UP region and the minor groove width in the UP region. The final set of pruned features was identified by including all features that yielded appreciable improvements in model accuracy. When faced with equivalent feature set choices, we selected the smaller set of features with fewer unknown coefficients, which often relied on biophysical calculations. We also compared each model’s trained interaction energies against the canonical interactions known to have the most effect on transcription initiation rate, which we refer to as positive controls, and selected models that achieved 100% of these positive controls. Notably, the differences in interaction energies for the highest performing models were small, but the small differences could affect the rank-order of the top/bottom 10 sequence motifs, which affected their positive control successes. In Supplementary Table We then carried out model training and hyperparameter optimization  using tenfold cross-validation on the training set. The best models were then evaluated on the unseen test set. Accuracy metrics included the mean absolute error (MAE), mean squared error (MSE), and the squared Pearson correlation R. Feature48. (iii) The energetic contributions from binding to the −10 extended motif were represented using one-hot encoding of the presence of each possible 2 nt motif located upstream of the −10 hexamer, totaling 16 categorical features. (iv) The energetic contributions from the discriminator region were represented using one-hot encoding of each possible 3 nt motif in the first 3-nucleotides of the discriminator region, totaling 64 categorical features. (v) The energetic contributions from the UP region were represented using the numerical value of the calculated minor groove width in the distal and proximal UP sites (each 10 bp long)45. (vi) The energetic contributions from the ITR were represented by calculating the thermodynamic stability of the R-loop within the first 15 nucleotides of the ITR region , which is the free energy of the duplexed DNA-DNA complex subtracted by the free energy of the duplexed RNA-DNA complex. Numerical feature values are divided by the maximum possible value for normalization.The final set of pruned features included: (i) The energetic contributions from binding to the −35 and −10 hexamer were represented using one-hot encoding of the presence of each possible 3 nt motif within each hexamer. All possible 3 nt motif sequences (64 3-mers) are included and their presence or absence is a categorical feature, totaling 256 features. (ii) The energetic contributions from the spacer region were decomposed into two sets of features, including the spacer length  and the spacer’s DNA rigidity In addition, during the initial model training, feature encoding relied on the positional identification of sequence motifs using the following procedure. The predominant TSS was determined from TSS Mapping measurements. The promoter variant sequence was then scanned, taking into account that discriminator lengths varied from 5 to 10 nt and spacer lengths varied from 15 to 20 nt. The positions of the −10 and −35 hexamer motifs were then identified by scanning across the sequence, evaluating a position weight matrix tailored for each motif, and calculating a motif score. The positions with the highest motif score were labeled as the −10 or −35 hexamer. If two or more locations had equivalent motif scores, the motif location with the most optimal spacer length (17 nt) was selected. This procedure was tailored to function alongside our experimental promoter library design, which included the 4096 possible −35 hexamer motif sequences alongside a consensus −10 hexamer as well as the 4096 possible −10 hexamer motif sequences alongside a consensus −35 hexamer. Even though these designs used a canonical spacer length of 17 nt, an alternate procedure that assumed that the spacer length was always a constant yielded scenarios where sequence changes led to shifts in the locations of the actual −10 or −35 motif and corresponding changes in motif sequence. Failing to account for these shifts led to mismatched interaction energies during model training, for example, non-canonical motifs having interaction energies similar to canonical motifs, created by the formation of a canonical motif with a shifted location.E. coli DH10B cells were transformed with each plasmid, following by quantification of their mRFP1 expression levels during exponential growth, using spectrophotometry and flow cytometry measurements. Cells were grown overnight in LB media supplemented with 50 ug/ml chloramphenicol, followed by a 1:100 dilution into 200 ul M9 minimal media supplemented with 50 ug/ml chloramphenicol within 96-well optical bottom microtiter plates. OD600 and mRFP1 fluorescence measurements were recorded every 10 min until cultures reached an OD600 of 0.20. Cultures were then serially diluted 1:10 into pre-warmed supplemented M9 media and grown again until they reached an OD600 of 0.20. 10 ul aliquots were then extracted and added to 190 ul PBS with 2 mg/mL kanamycin to stop protein production. Flow cytometry was then carried out on the fixed cells to record their fluorescence distribution (BD LSR Fortessa). The mRFP1 fluorescence level was the arithmetic mean of the measured fluorescence distribution subtracted by autofluorescence, which was the arithmetic mean of the fluorescence distribution of wild-type E. coli DH10B cells. An example of our gating strategy is provided in Supplementary Fig. Individual promoters were constructed to test the effects of changing UP and ITR regions on transcription rates Fig.  and to tThe core promoter sequences for the Anderson Library were retrieved from the iGEM website. We randomly generated 10,000 pairs of 30 bp DNA sequences with equal weighting of all four nucleotides. These DNA pairs were used to flank the core promoters creating a simulated DNA context. The Promoter Calculator was then used to scan and predict the transcription rate of each member of the promoter library in all 10,000 contexts. Box plots were generated using each promoter’s predicted transcription rate distribution.We used an optimization algorithm  to automatically design 35 promoter sequences with systematically varied transcription initiation rates, using the statistical thermodynamic model prediction within the optimization algorithm’s objective function. Designed promoter sequences were allowed to have variable TSS positions and use the surrounding upstream/downstream DNA sequence as part of the promoter. The surrounding upstream/downstream DNA sequences were kept constant for all promoter designs. The promoter lengths varied from 60 to 77 bp, yielding an average sequence similarity of only ∼27%.8, and identify desired and undesired/cryptic TSSs on both its sense and anti-sense DNA strands. We labeled a location as a TSS when its predicted transcription initiation rate was at least threefold higher than the average transcription initiation rate per each strand. We used RNA-Seq measurements and RNAP flux inferences from Borujeni et al.8 to assess the accuracy of these TSS predictions , as studied and characterized in Borujeni et al.793 bp, a15 Predictions on promoters using the Lagator models were carried out by using their provided source code and data files, which were retrieved from https://github.com/szarma/Thermoters. We show all Lagator model predictions on their training and test datasets  and log10(TX), where Pon is the Lagator model prediction and TX is the measured transcription rate. In our model formulation, comparisons are made between the predicted ΔGtotal and log(TX). To compare between models, we converted Lagator predictions to natural log scale. Both models assume a linear relationship between these predicted and measured quantities. We therefore calculated the Pearson R2, the MAE, and the MSE for each model on each dataset  for each model and dataset, we identify the best-fit slope via linear regression and use it to calculate MAE and MSE (Supplementary Table F-tests were carried out to compare model errors (Supplementary Data We carried out benchmarking by comparing transcription rate measurements to model predictions, using our model and the models developed by Lagator et al.Further information on research design is available in the Peer Review FileSupplementary InformationReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5


CohesinDB includes 2043 epigenomics, transcriptomics and 3D genomics datasets from 530 studies involving 176 cell types. By integrating these large-scale data, CohesinDB summarizes three types of ‘cohesin objects’: 751 590 cohesin binding sites, 957 868 cohesin-related chromatin loops and 2 229 500 cohesin-related CRMs. Each cohesin object is annotated with locus, cell type, classification, function, 3D genomics and cis-regulatory information. CohesinDB features a user-friendly interface for browsing, searching, analyzing, visualizing and downloading the desired information. CohesinDB contributes a valuable resource for all researchers studying cohesin, epigenomics, transcriptional regulation and chromatin organization.Cohesin is a multifunctional protein responsible for transcriptional regulation and chromatin organization. Cohesin binds to chromatin at tens of thousands of distinct sites in a conserved or tissue-specific manner, whereas the function of cohesin varies greatly depending on the epigenetic properties of specific chromatin loci. Cohesin also extensively mediates cis-regulatory modules (CRMs) and chromatin loops. Even though next-generation sequencing technologies have provided a wealth of information on different aspects of cohesin, the integration and exploration of the resultant massive cohesin datasets are not straightforward. Here, we present CohesinDB ( Cohesin is an essential protein complex that encircles chromatin within its ring-shaped structure . While tWithin the past few decades, advances in next-generation sequencing (NGS) technologies have enabled the genome-wide analysis of cohesin from different aspects. However, the integration and exploration of the massive cohesin datasets are not straightforward. First, the chromatin binding sites of cohesin are highly diverse. Unlike typical transcription factors (TFs) or CTCF, cohesin does not show any binding motifs, impeding the computational prediction of cohesin sites. Cohesin can bind to chromatin at tens of thousands of distinct sites in a conserved or tissue-specific manner ,12, wherhttps://cohesindb.iqb.u-tokyo.ac.jp), a comprehensive multiomics cohesin database in human cells that integrates 2,043 ChIP-seq, ChIA-PET, Hi-C, Hi-ChIP, RNA-seq and microarray datasets from 530 studies (176 cell types). CohesinDB offers a sophisticated interface for browsing, searching, visualizing and analyzing cohesin datasets and three types of ‘cohesin objects’: cohesin binding, cohesin-related chromatin loops and CRMs. We examined the reliability of the cohesin information in CohesinDB by comparing it with known knowledge. We also compared CohesinDB with related databases. CohesinDB contributes a valuable resource not only for cohesin research but also for research on various genomic events, including chromatin folding, enhancer activity, CRMs, epigenetics, transcriptional regulation, and human diseases.Here, we present CohesinDB . RNA-seq reads were mapped with STAR version 2.7.3  were aligned with Bowtie2 version 2.4.1 , followeon 2.7.3 , and genon 2.7.3 . Differeon 2.7.3 . All of Only high-quality ChIP-seq data, defined according to a normalized strand coefficient (NSC) > 1.2 and number of peaks > 1000, were retained to construct cohesin binding information. After excluding indirect factors , we used 550 ChIP-seq datasets to generate the universal binding sites of cohesin. In total, we obtained 34 534 315 independent cohesin sites. We then retained cohesin sites identified in more than two ChIP-seq samples to increase the reliability of binding sites. Two sites at a distance of <200 bp were merged to avoid clustered small peaks. Finally, we obtained 751 590 cohesin binding sites (coverage 27.93% whole genome), each of which was assigned a unique ID, for the construction of CohesinDB.https://www.ebi.ac.uk/gwas/) and somatic mutations from the COSMIC project (https://cancer.sanger.ac.uk/cosmic). (vi) Hyperlinks to the associated cohesin loop objects and cohesin CRM objects are provided. (vii) Selected cohesin sites, related cohesin loops, and target genes of related cohesin CRMs are visualized in a genome browser.CohesinDB provides comprehensive annotations for each cohesin binding site . (i) Basundaries , chromatundaries  and A/B undaries . (iv) Thundaries , chromatundaries , co-bounundaries , and tarundaries   <0.05. The loops obtained from Hi-C and Hi-ChIP were called by using HiCCUPS  with the2-fold change| >1 and FDR <0.01. Only DEGs identified in more than two studies were retained, to obtain a more reliable gene list. Then, cohesin sites less than two megabases away from the promoter of a DEG were considered regulatory sites, providing pairings between genomic loci and genes. The second type of evidence comes from cohesin-mediated loops. The pairing of cohesin sites with genes was identified based on cohesin-mediated loops that connect a cohesin site to the promoter of a gene. Cohesin sites directly located at promoters were also included in the cohesin-loop evidence. To obtain ‘double-evidenced’ CRMs, only pairs of cohesin sites with genes that appeared in both the cohesin-DEG and cohesin-loop evidence were retained. Finally, we obtained a total of 2 229 500 cohesin-related CRMs involving 15 203 target genes. Cell type information on the involved cohesin binding sites, chromatin loops and DEGs was recorded for each CRM.Cohesin-related CRMs are defined based on the pairing of cohesin sites with target genes, together with epigenomics and chromatin structural features. In CohesinDB, two types of evidence are used to identify cohesin-related CRMs. The first type, cohesin-DEGs evidence, comes from transcriptomic data in the context of cohesin depletion. Cohesin DEGs  were filtered according to a |Loghttps://gtexportal.org) and the cell-type specificity of the regulatory cohesin sites ; (iv) cohesin-DEG information, including whether the gene is a cohesin DEG, the cell types involved, the original studies of cohesin-DEGs, the number of studies involved, and the cohesin subunits involved; (v) cohesin-loop information, including hyperlinks to chromatin loop objects, the loop type, the cell types involved, the original studies of the loops and the cohesin subunits involved and (vi) visualization of selected gene, related cohesin sites and cohesin loops.CohesinDB assigned an ID to each gene and annotated  each genhttps://djangoproject.com) version 3.0.14. All data in CohesinDB were organized and queried by using MySQL (https://mysql.com) version 8.0.28. The front-end interface was implemented by using HTML, CSS, jQuery and JavaScript. Bootstrap (https://getbootstrap.com) was used to achieve a responsive web design. CohesinDB was deployed using Ubuntu 20.04 through Nginx web server (http://nginx.org) version 1.18.0. CohesinDB is available online at https://cohesindb.iqb.u-tokyo.ac.jp without any registration or login required. CohesinDB can be used in mainstream web browsers, including Google Chrome, Mozilla Firefox, Safari and Microsoft Edge, with JavaScript enabled. The CohesinDB Django project and the data processing script are available as open-source resources at https://github.com/wangjk321/CohesinDB_public.CohesinDB was written by using Python3 with Django framework (https://cohesindb.iqb.u-tokyo.ac.jp/help/).The contents of CohesinDB consist of four components: processed files obtained from raw sequencing data, and three types of objects concerning cohesin-binding sites, cohesin-related loops and cohesin-related CRMs . Using tThe ‘Browse’ page has a drop-down list that provides browsing and searching functions for three types of cohesin objects: cohesin-binding sites, cohesin-related loops and CRMs Figure . For eacBased on the CRMs and chromatin loops in CohesinDB, we designed an analysis page with three modules Figure . The firThe second module, ‘predict target gene’, takes a set of genomic regions as input and then discovers genes regulated by the input regions as well as the KEGG pathway enrichment of the discovered genes Figure . The outThe third module, ‘loop discovery’, takes a file containing genomic regions as input and then outputs chromatin loops anchored to the input region as well as the other end of the chromatin loops Figure . Users cUsers can also specify the cell type of interest when analyzing CRMs and chromatin loops. All three modules can be tested with a ‘Run example’ button. Given the extensive involvement of cohesin in transcriptional regulation and chromatin organization, these analyses can be used for a wide range of purposes.https://genome.ucsc.edu/). Visualizations in CohesinDB can be downloaded as PDF files.CohesinDB prepares visualizations of not only each cohesin object Figure  but alsoAll processed data from the 2043 samples are listed on the ‘Data’ page Figure . This pahttps://cohesindb.iqb.u-tokyo.ac.jp/pipeline/#maintain). The web server, web domain and SSL certificate of CohesinDB are provided by an academic institution, and we anticipate that CohesinDB will be maintained for a long time.The contents of CohesinDB will be maintained and updated in two ways: through a Django admin page, where we can manually modify the information of each dataset and cohesin object, or a programmable pipeline, through which we can process new datasets and update all contents . This is reasonable because cohesin plays context-specific roles in transcriptional regulation (P < 10−17), indicating a tendency for cell-type specific cohesin to regulate tissue-specific genes. Indeed, conserved cohesin and cell-type-specific cohesin exhibit a significant difference (P < 10−37) in the tissue specificity of the target genes . Taken together, the statistics and examinations above support the richness and reliability of the multiomics information contained in CohesinDB.Since the discovery of SMC  protein family in 1997 , the undNAR database issue, 2013) is a database designed for CTCF binding sites. Although some cohesin co-localizes with CTCF, many reports have shown that cohesin is different from CTCF .CohesinDB can be accessed online at gkac795_Supplemental_FileClick here for additional data file.


LMNA, encoding nuclear A-type lamins, are one of the major causative causes of dilated cardiomyopathy (i.e. CardioLaminopathy). A-type lamins are type V intermediate filament proteins, which are the main components of the nuclear lamina. The nuclear lamina is connected to the cytoskeleton on one side, and to the chromatin on the other side. Among the models proposed to explain how CardioLaminopathy arises, the “chromatin model” posits an effect of mutated A-type lamins on the 3D genome organization and thus on the transcription activity of tissue-specific genes. Chromatin contacts with the nuclear lamina via specific genomic regions called lamina-associated domains lamina-associated domains. These LADs play a role in the chromatin organization and gene expression regulation. This review focuses on the identification of LADs and chromatin remodeling in cardiac muscle cells expressing mutated A-type lamins and discusses the methods and relevance of these findings in disease.Cardiomyopathy is a myocardial disorder, in which the heart muscle is structurally and functionally abnormal, often leading to heart failure. Dilated cardiomyopathy is characterized by a compromised left ventricular function and contributes significantly to the heart failure epidemic, which represents a staggering clinical and public health problem worldwide. Gene mutations have been identified in 35% of patients with dilated cardiomyopathy. Pathogenic variants in  Next, an antibody specific to the protein of interest is used to extract the DNA-protein complex by immunoprecipitation and the extracted DNA sequences are identified by sequencing (ChIP-seq) approaches . Expression of this fusion protein in vivo leads to the addition of a methylation group to the adenine in GATC sequences (adenine-6-methylation (m6A)). Because adenine methylation does not occur endogenously in eukaryotic cells, it provides a unique tag to mark protein interaction sites. The adenine-methylated DNA fragments are next isolated using a specific endonuclease, and are sequenced after PCR amplification. This method allows the identification of LADs in diseases caused by mutations in A-type lamins . This approach allows to map the chromatin accessibility at the genome-wide scale with an in vitro transposition of sequencing adaptors into native chromatin  . This ahromatin . Prokaryhromatin . ATAC-sehromatin . A recenhromatin .in situ hybridization (FISH) and more recently, by 3D-FISH (FISH on 3D-preserved nuclei with immunofluorescence) in cells  . RecentlThe mammalian genome is organized in different three-dimensional levels, which are finely regulated during normal and pathological development . In thise.g., cardiac fibroblasts and endothelial cells) could participate to further understand the pathogenesis of Cardiolaminopathy (This review targets the genome organization specifically in cardiomyocytes. However, studying other cardiac cell type present in the heart environment (inopathy .CardioLaminopathy. Here, we have outlined recent findings and methods that uncovered the role of chromatin compartment dysregulation in both mice and humans, giving novel insights into the nature of cardiomyocyte dysfunction during disease progression. Nevertheless, there is still a long list of open questions that needs to be answered. While recent studies have elegantly identified the role of chromatin regulation during the pathogenesis of CardioLaminopathy, the distinct roles of chromosomal compartmentation remain elusive. Application of selective tools targeting only the context-dependent A-type lamins gene expression by Cre recombinase-mediated gene targeting or defined drugs might provide new insights into the nature of chromatin organization in the heart. To date, little is known about the temporal regulation of chromatin organization and the transcriptional level during development of CardioLaminopathy. Analyzing chromatin organization and gene regulation in heart along the progression of the disease will illuminate another aspect of genome regulation and help to identify specialized functions for the pathogenesis of age-related disease. In addition, whether such chromatin phenotypes are detrimental or beneficial for disease progression still remain unclear. Answering such critical questions, together with profiling heterogeneous subclusters of chromatin organization in healthy and diseased situations, can open new avenues for the development of therapeutic targeting of cardiomyocytes in CardioLaminopathy. These studies open up new perspectives in an attempt to explain the pathophysiology of Cardiolaminopathies and pinpoint possible new A-type lamins functions still unexplored.Recent advances to study chromatin organization shed exciting new light on the pathogenesis of


SIRT1 also known as NAD-dependent deacetylase), an enzyme that requires NAD+ as a cofactor to deacetylate several histones and transcription factors. It stimulates various oncogenic pathways promoting tumorigenesis. This data suggests that SIRT1 is a potential novel transcriptional target of CD44-downstream signaling that promote BC cell invasion/metastasis. This review will discuss the evidence supporting this hypothesis as well as the mechanisms linking SIRT1 to cell proliferation and invasion.Our tetracycline-off-inducible CD44 expression system previously established in mouse model, revealed that activation of CD44 with its major ligand hyaluronan (HA) promoted breast cancer (BC) metastasis to the liver. To identify the mechanisms that underpin CD44-promoted BC cell invasion, microarray gene expression profiling using RNA samples from (Tet)-Off-regulated expression system of CD44s in MCF7 cells, revealed a set of upregulated genes including, nuclear sirtuin-1 ( Breast Cancer (BC) is the most common malignant tumor in women worldwide including the state of Qatar, which may arise either as a result of family history or exposure to harmful environmental factors such as radiation, high alcohol consumption, and lifestyle –3. Unforin vitro (in vivo (Sirtuin 1 (SIRT1), was selected for further validation studies as potential target of CD44 because of its involvement in cell proliferation, invasion, and metastasis.Since CAMs play a vital role in invasion, our own work has concentrated since 2006 on a CAM protein family known as CD44 –14. CD44in vitro  and in v  . Nuclearfunction , 22. Fursistance . This reSIRT1 is encoded by a gene located on the long arm of chromosome 10 (10q21.3), that is composed of 747 amino acids forming four regions, the nuclear localization signal 41-46 amino acids found at the N-terminal, the allosteric side located from 184 to 243 amino acid, the preserved catalytic domain, where deacetylation occur at the centre of the domain, and the C-terminal region located from 631 to 365 amino acid. The N-terminal region is significant as it is where the nuclear reading occurs allowing SIRT1 to translocate to the nucleus  a helical module (residues 269 to 324) and ii) a Zn2+-binding module (residues 362-419). The NAD+-binding domain consists of six-stranded parallel β sheets and eight α helices. However, the Zn2+-binding domain is composed of 3 β strands and a single α helices. SIRT1 C-terminal regulatory segment was found to form a quaternary complex with NAD+-binding domain, by binding to its lower edge to match the central parallel β sheet of its Rossmann fold  regulate Wnt Signaling pathway by translocating β-catenin from the cytoplasm into the nucleus activating several oncogenic pathways. The cytoplasmic SIRT1 colocalizes dsh protein in the cytoplasm and enhances the expression of DNA Methyl-transferase 1 (DNMT1) that promote DNA hypermethylation in the promoter domain of APC, thereby inhibiting its tumour suppressor function. The Dsh protein will inhibit the phosphorylation of β-catenin allowing its accumulation in the nucleus, upregulating the transcription factors TCF/LEF (T-cell factor/Lymphoid enhancer factor). Nuclear SIRT1 will bind to the LEF1 lysine residue to deacetylate the present histones regulating the transcription of the downstream targets such as cyclin D1, C-Myc, and surviving, thereby inducing tumour proliferation and migration , MA-M: Editing, AO: Conceptualization (lead), funding acquisition (lead), writing-review and editing. All authors contributed to the article and approved the submitted version.This research was funded by Qatar University Internal grant number: QUST-1-CAS2019-22 and the Qatar Foundation grant number: UREP24-117-1-027.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.