Analysis of G4-seq and CUT&Tag data strongly supports the existence of G4-BS conformations genome-wide. We reconstructed a novel G4-B 3D structure located in the E2F8 promoter. This study defines a large family of G4-like sequences, offering new insights into the essential biological functions and potential future therapeutic uses of G4-B.DNA sequence composition determines the topology and stability of G-quadruplexes (G4s). Bulged G-quadruplex structures (G4-Bs) are a subset of G4s characterized by 3D conformations with bulges. Current search algorithms fail to capture stable G4-B, making their genome-wide study infeasible. Here, we introduced a large family of computationally defined and experimentally verified potential G4-B forming sequences (pG4-BS). We found 478 263 pG4-BS regions that do not overlap ‘canonical’ G4-forming sequences in the human genome and are preferentially localized in transcription regulatory regions including R-loops and open chromatin. Over 90% of protein-coding genes contain pG4-BS in their promoter or gene body. We observed generally higher pG4-BS content in R-loops and their flanks, longer genes that are associated with brain tissue, immune and developmental processes. Also, the presence of pG4-BS on both template and non-template strands in promoters is associated with oncogenesis, cardiovascular disease and stemness. Our G4-BS models predicted G4-forming ability   G4s a or Na+) . Such sts ,2. G4s or Na+) , DNA rep or Na+) , the oxi or Na+) ,8, and l or Na+) . Further or Na+) . G4s hav or Na+) ,12. Due  or Na+) .c-MYC (c-KIT (RET (hTERT (BCL2 (KRAS (The G4-forming DNA sequences (G4-S) themselves may also hold direct relevance to clinical practice, as G-rich genomic regions are prevalent in telomeric regions  and the c-MYC , c-KIT  were observed to frequently co-localize with another type of non-B DNA structure, R-loops, which are also characterized by high GC-content. Transcriptional R-loop consists of guanine-rich RNA:DNA hybrid, which is thermodynamically more stable than the original DNA:DNA duplex and thus displaces the non-template DNA. R-loops are important regulators of gene expression and telomere lengthening, and many other physiological processes, but have also been implicated as sources of cellular and genotoxic stress, genome instability and diseases ,51. Maized genes ,52. Theyet al.  has introduced quantitative models of the R-loop forming sequences , which provides highly accurate computationally predicted RNA:DNA hybrid/R-looping regions in natural or artificial DNA  and utilize these models to identify pG4-BS in the human genome. We show that about 90% of human protein-coding genes contain at least one pG4-BS in either the promoter region or the gene body, that the high density of pG4-BS is preferentially localized in transcription checkpoints, including intron–exon junctions, around the transcription start site (TSS) and transcription termination site (TTS). Additionally, many of the pG4-BS overlap with other regulatory elements, such as DNase I hypersensitive sites and transcription factor binding sites (TFBS). Using gene ontology (GO) analysis we identify subsets of pG4-BS enriched in specific genes associated with GO terms related to transcriptional regulation, the nervous system, and diseases. We show that genes with the pG4-BS co-occurred on both chromosome strands and were enriched for GO terms over genes with single-strand pG4-BS occupancy. Co-localization analysis of experimentally detected G4s with our computational prediction data, including RLFS, reveals that the mapping resolution of current genome-wide experimental approaches targeting the identification of potential G4-S is not sensitive enough to fully capture the complexity and localization of the genome-wide frequency distributions (FD) of G4-like sequences. In addition, we consider pG4-BS on both template and non-template strands in promoters and study the associations of these complex back-forward G4 architectures with genes and pathways. The results of our analyses integrating our computational predictions with G4-seq and G4 CUT&Tag data strongly support the existence of G4-B conformations. Additionally, we use nuclear magnetic resonance (NMR) spectroscopy, circular dichroism (CD) spectroscopy and ultraviolet (UV) melting experiments for the identification of stable pG4-BS predicted by our models in cancer-associated genes. Finally, we provide the in-depth structural characterization of the G4-B formed by a novel pG4-BS located in the promoter region of the In previous studies, we characterized the G4-forming ability of some artificial pG4-BS . Based o3+N1-7G3+N1-7G3+N1-7G3+G) from our sequences as well as pG4-BS sharing at least one nucleotide on the same strand with any pG4-CS. In the pG4-BS set, we kept only sequences with at least two G-tracts without bulge(s) and a maximum of two bulges in the sequence. In addition, we did not allow more than a single G linker between G-stems and excluded pG4-BS with continuous cytosine repeats .Here, ‘G’ denotes a guanine base, ‘I’ denotes any of the nucleotides A, T or C bases interrupting guanines with x number of nucleotides, ‘N’ denotes any bases  with y number of nucleotides. We have created seven sequence models for sequences that can potentially form G4-B . This clus study , and sea3+N1-7G3+N1-7G3+N1-7G3+; where N refers to any base) depending on the parameters traditionally used by other search algorithms. We used our program to identify individual G4-S in both strands of the human genome (Hg38).While tools exist that predict G4-S , traditihttps://genome.ucsc.edu/cgi-bin/hgTrackUi?g=rmsk).In the case of both pG4-CS and pG4-BS datasets, we identified and removed the sequences that coincide with low complexity regions or repeat elements, such as SINE, LINE, tRNAs or snRNAs. The full dataset of repeat elements used in our analysis, as well as the full list of repeat elements considered, is available from the UCSC Genome Browser RepeatMasker track .Nuclear magnetic resonance (NMR) experiments were performed on Bruker 600 and 700 MHz spectrometers at 25°C, unless otherwise specified. Resonances for guanine residues were assigned unambiguously by using site-specific low-enrichment 15N-labelling  and siteCircular dichroism (CD) spectra were recorded on a JASCO-815 spectropolarimeter over the range of 220–320 nm using a 1-cm path length quartz cuvette with a reaction volume of 500 μl. For CD-melting experiments, cooling and heating were successively performed across the temperature range of 15–95°C over a total of 14 h. At intervals of 1°C, the full spectrum was recorded as an average of three scans, the spectrum of the buffer was subtracted, and the data were zero-corrected at 320 nm. The molar ellipticity at 295 nm was extracted for melting analysis. Two baselines corresponding to the completely folded (low temperatures) and completely unfolded (high temperatures) states were manually drawn to determine the fractions of folded and unfolded species during the melting process.The stability of G-quadruplexes was characterized in UV melting experiments conducted on a JASCO V-650 spectrophotometer. Experiments were performed with 1-cm path-length quartz cuvettes. DNA concentration ranged from 4 to 6 μM. Solution contained 30 mM KCl and 20 mM potassium phosphate (pH 7.0) or 6 mM KCl and 4 mM potassium phosphate (pH 7.0). Samples were initially incubated at 90°C for 10 min and then cooled down to 20°C at a rate of 0.2°C/min; after a delay of 10 min, they were heated back to 90°C at the same rate of 0.2°C/min. Absorbance at 295 nm was recorded as a function of temperature ranging from 20 to 90°C. The linear pre- and post-transition regions of each absorbance-versus temperature curve were taken as the baselines corresponding to the completely folded (low temperature) and completely unfolded (high temperature) states. The fractions of the folded and unfolded states were derived by taking the ratio of the differences between the baselines and the experimental curve at each temperature.+ buffer), and second, under G4-stabilizing conditions (e.g. K+ or K+ and pyridostatin (PDS) containing buffer). Following sequencing, the results of the first and second run are compared and based on the detected mismatches (that are due to folded G4 structures interfering with the sequencing process) the potential sites of G4 formation  are mapped and quantified. From the downloaded datasets, using the preprocessing and peak calling methods reported in (+ buffer) or stronger (K+ and PDS buffer) G4-stabilizing conditions, thereby capturing the highest number of G4-seq identified regions (n = 949 162). The selected G4-seq hit scoring regions may or may not contain (or overlap) with computationally predicted G4-like sequences. We also specified a subset of G4-seq hit scoring regions that were observed under both G4-stabilizing conditions (both K+ and K+ + pyridostatin (PDS)). We termed this subset of G4-seq signals, ‘higher confidence’ G4-seq regions (n = 200 582).To evaluate the enrichment of our computational pG4-BS/pG4-CS data with G4-seq signals, we downloaded available G4-seq data from GSE110582 . During orted in , we extret al.  and MACS2 v2.2.7 with default parameters and a q-value cutoff of 1.00E-12. To compare data with G4-S models and G4-seq peak regions, we merged all peaks from all cell lines. In total, we identified 45968 merged peak calling regions in the DNA samples of cancer cell lines K562, HeLa, LM2 (MBD-231-LM2), SW1271, and two replicates of the embryonic kidney HEK293T cell line.Here, we reproduced the computational pipeline reported in the et al. . We downet al. . We usedIn order to count pG4-S enrichment in experimentally detected G4 signal regions genome-wide, we use a computational method that identifies a set of ‘background sequences’. In such a set, the number of sequences is the same as in the representative pG4-S set. In addition, the number of randomly chosen sequences follows the same FD of sequence lengths as in the representative pG4-S set, and random sequences that overlap with the original location of pG4-S are excluded.P-value, and confidence intervals, we used the Ratio of Two Binomial Proportions test in Cytel Studio 9. This method is also applied to calculate ES in the case of G4-CUT&tag datasets.In particular, let's examine the enrichment scoring method for merged G4-S (merged pG4-BS and merged pG4-CS) in G4-seq peak regions. First, the algorithm selects random samples of 10 000 sequences from our computationally predicted merged pG4-S dataset. Next, it selects 10 000 random background sequences from the human genome, controlling for length and selecting only sequences that do not overlap with the original position of any pG4-S. After that, the algorithm counts how many background sequences overlap with at least one G4-seq region. Positive and negative categorical events are the overlapped background sequences with the G4-seq peak region versus the non-overlapped background sequences. If at least one nucleotide is shared between two sequences of different sets, the event is positive. Using the positive and negative event numbers for pG4-S and background sequences relative to G4-seq peak regions, our method constructs 2 × 2 contingency tables and calculates the enrichment score (ES). ES is defined as the ratio between the proportion of positive G4-seq events in the set of the G4-S and the ratio of positive G4-seq events in the background sequence set. For statistical testing, and calculation of the Standardized Difference, 2-sided The few or no replicates, low signal region resolution and technical biases in available NGS-based G4 mapping data limit their use as a reference for identifying and validating pG4-BS and pG4-CS (see below). However, the quality of such datasets could be improved in the context of identifying and selecting stable pG4-S (pG4-CS/pG4-CS)-positive data from G4-seq/CUT&Tag peak regions. In these cases, our enrichment analysis can be used to filter out technically problematic and false positive experimentally defined signal peak regions by counting pG4-S(pG4-CS/pG4-CS)-positive G4-seq (or G4-CUT&tag) signal peak regions and pG4-S(pG4-CS/pG4-CS)-positive background signal peak regions. These cases were handled using our method.Bulges are structural elements of G4s, which form protrusions of bases from the G-tetrad core. Bulges occur due to the presence of non-guanine bases in one or more G-tracts of the G4 forming sequences. Previously, we have shown that G4 structures could incorporate bulges  or otherin vitro sequence models that varied based on the number of insertion sites and the number of uninterrupted stems  genome-wide. Based on our previous results, we selected three G4-BS models that correspond to the tested sequences that formed stable G4-B Figure . Namely,lgorithm . In theilgorithm . In paraUsing in-house Python search algorithms; we identified 1935686 pG4-BS and 954745 pG4-CS in the human genome. We removed sequences that overlapped between the two datasets and further excluded any sequence that co-localized with human repeat elements. After the filtering steps, we retained 245146 pG4-CS and 899687 pG4-BS sequences that constitute 25.7% and 46.5% of the originally identified sequences, respectively. Based on the differences in population size, our results suggest that pG4-BS represents an essential extension of potential sites of G4 formation in the human genome. Additionally, the majority (>65%) of the traditionally identified pG4-CS sequences overlap with one or more pG4-BS sequences. As such, the pG4-CS and pG4-BS could positively or negatively cooperate in their vicinity.We further classified the 899 687 non-repeat overlapped pG4-BS according to the sequence model they match Figure . We obseIn the next step, we plotted the frequency distribution (FD) for each category of merged pG4-S regions to determine how the lengths are distributed for each dataset. As shown in Figure n = 63 660; the lowest number of regions among the studied categories) to test whether the FD of the lengths statistically differed between pG4-BS regions and pG4-CS regions. We observed significant differences between the lengths of regions belonging to any of the pG4-BS models and pG4-CS regions . Additionally, the K-S statistic distance metrics between the paired distributions of same-size samples differ, showing the smallest differences between pG4-CS and non-model-specific pG4-BS regions (D = 0.447), followed by G2B2 type pG4-BS (D = 0.457), G3B2 type pG4-BS (D = 0.557) and G3B1 type pG4-BS (D = 0.664).We also observed that the length of the right tail of the FD belonging to each group varies; this feature correlates with the sample size of the sequence model. In particular, G2B2 sequences have the highest frequency of merged regions in the genome and have the longest FD tails. To account for this sample-size difference effect, we utilized same-size random samples  of protein-coding genes overlapped with both pG4-BS and pG4-CS on the non-template strand, while 5295 genes (28%) were only pG4-BS positive, and only 466 (2%) genes were uniquely pG4-CS-positive . Similarin vivo.In summary, we identified 8807 pG4-BS-positive/pG4-CS-negative genes and mapped the positions of such pG4-BS genome-wide . Thus, tin vitro.We note that our computational identification does not cover all potential forms of pG4-BS or pG4-CS. However, our selection criteria for both types of G4-S are based on experimental results that allowed us to develop a search strategy that can capture sequences that are most likely to be able to form stable bulged G4s The preferential occurrence of pG4-BS in promoters, 5′/3′UTRs, exons, and TTS boundaries suggests a regulatory role for pG4-BS in gene transcription initiation and splicing eventsIn our previous analyses, we saw that pG4-BS are present in most human protein-coding genes. In our next step, we wanted to identify where pG4-BS regions frequently occur within a given gene. First, we counted the number of gene-associated G4-S that occurred at each nucleotide position around the TSS (±2 kb) of G4-S positive genes Figure . pG4-BS The analysis of the distribution functions revealed that pG4-BS regions belonging to all three G4-BS models possess bimodal distributions. Out of our three G4-BS models, most of the pG4-BS regions that are localized near the TSS belong to the G2B2 model . Howeverk bases) the selected gene segments. We note that G4-S overlapping multiple gene segments were included in all associated categories. Our results show that pG4-BS and pG4-CS regions show similar distribution patterns across the studied boundaries of gene segments. Interestingly, pG4-BS regions are more abundant compared to pG4-CS regions, which can be due to the much higher pG4-BS presence in protein-coding genes that we observed earlier  upstream of the transcription initiation site (TIS), followed by a marked absence of G4-S in the immediate vicinity of the initiation site on both non-template and template strands  of the gene. As above, we calculated the per nucleotide frequency of pG4-BS and pG4-CS regions around  of pG4-BS region counts per protein-coding genes in Figure n = 17 993) in their promoter (region 2 kb upstream of TSS) or their gene body. First, we utilized the database for annotation, visualization, and integrated discovery (DAVID) bioinformatics resources using all annotated genes as the background. We observed strong tissue specificity , with the strongest association observed with brain tissue , many of which were associated with transcriptional regulation (e.g. ‘positive regulation of transcription from RNA polymerase II promoter (GO:0045944)’), or with cell signaling pathways .To investigate the potential regulatory roles of pG4-BS across specific biological classes of genes, we carried out GO analysis for the group of protein-coding genes that contain at least one pG4-BS region (n tissue . Specifin = 1000) from the beginning of the EDF (‘low’ pG4-BS content), the tail of the EDF (‘high’ pG4-BS content) and the area between these two (‘intermediate’ pG4-BS content). Among the three gene sets, we observed strong tissue specificity  with brain tissue in the case of the ‘high’ gene set. We also ran our gene lists through the Enrichr gene set enrichment tool to detect statistically significant enrichment of biological terms. We observed strong statistical significance with GO terms only in the case of the samples with ‘high’ pG4-BS content (data not shown).Next, we investigated if the degree of pG4-BS presence shown by the skewed EFD Figure  correspoP-value < 0.05) in 252 terms from the included databases   genes that contain at least one regulatory pG4-BS and/or regulatory pG4-CS region in their promoter region (2kb upstream of the TSS) . In the atabases . In the  < 0.05) . Between < 0.05) . The top < 0.05) .n = 3610), or both (n = 2761)  of 73 terms in the case of genes with regulatory pG4-BS located on both non-template and template strands. Among the results were terms related to the development and cellular differentiation, as well as those related to the regulation of transcription ’ , template strand ( = 2761) . For our045893)’ . We obsen = 2761) as input for the KEGG Pathway Database (2021). Amongst the enriched pathways  (data not shown). This indicates that the presence of bf-pG4-BS is likely not due to random chance, but rather appears in the genome due to evolutionary pressure and likely fulfills important biological roles.First, we did not observe significant differences in the distribution of relative exon count frequencies between our gene sets . In all n = 1870 and 2120, respectively) or both strands in a back-forward (bf) genome architecture (n = 698). Here, we took triplicate random samples of 300 genes for every gene set and performed GO analysis. Using same-size random samples, we observed similar trends, with bf-pG4-CS containing genes being the only gene set enriched for biological terms (data not shown).To determine if our findings are pG4-BS-specific, we carried out a similar analysis only for genes with promoter-associated regulatory pG4-CS. As before, these regulatory pG4-CS were either located on only a single strand only  method that was developed to detect putative G4 forming DNA sites in the human genome by Chambers et al  and lateet al. . The lat+ or K+ and PDS) in the G4-seq experiments. We observed that the G4-seq region length distribution functions show a unimodal distribution in all cases, and they are fitted well by the log-normal function or Weibull function . Additionally, the differences between the FDs of G4-S samples and G4-seq regions were also statistically significant in all cases . Overall, our findings indicate that the length of the G4-seq regions is generally longer and has lower GC% compared to pG4-BS and pG4-CS. Due to the length of G4-seq regions, the determination of the exact site of G4 formation becomes difficult . We repeated the analysis using a random sample of pG4-CS and corresponding background (random) sequences and observed a similar trend . Taken together, these results indicate that G4-seq signals are similarly enriched in pG4-BS as in pG4-CS, lending further support for the validity of our method and findings.As an additional step to ensure that our computational predictions are well-founded and persuasive, we analyzed how well G4-seq-derived data supports our pG4-BS dataset. We utilized same-sized samples of pG4-BS and background (random) sequences and calculated the proportion of ‘pooled’ G4-seq data-supported sequences in each sample . Based oNext, we analyzed the FD of ‘pooled’ G4-seq, pG4-CS and pG4-BS regions around the TSS of protein-coding genes Figure . We obsen = 45 968) .To assess the validity of our computational predictions, we also utilized data derived from G4 CUT&Tag experiments . We downTo start, we compared the lengths of CUT&Tag peaks to those of computationally predicted and G4-seq derived regions . We obsen = 45 968), 17802 (38.7%) peaks were overlapped with pG4-BS, pG4-CS or both. To determine where the overlapping pG4-S motifs are commonly located in experimentally derived G4 regions, we mapped pG4-S to both G4 CUT&Tag and G4-seq regions and identified the most common positions in relation to the center of the given experimental G4 region . Overall, we observed similar enrichment of both pG4-BS (ES = 6.1) and pG4-CS (ES = 7.5) over their respective backgrounds in the pooled dataset   and created corresponding background samples, while controlling for sequence length and chromosomal location. Then, we calculated the number of overlaps between each of our G4-S and background samples and the pooled G4 CUT&Tag peaks (< 0.001) . This suFinally, we overlayed the distribution of G4 CUT&Tag regions around the TSS of protein coding genes with those of pG4-BS, pG4-CS and ‘pooled’ G4-seq regions Figure . We obsen = 228 999) over 50% overlap with pG4-BS, pG4-CS or both , we saw slightly higher ratios of uniquely pG4-CS(+) RLFS (10% versus 13%), and a lower percentage of pG4-BS(+) RLFS (34% versus 32%)  and R-loops may be associated with increased DNA damage, genome instability and cancer ,51,75. Fh Figure . However) Figure . InteresP-value < 0.001) between the ratios of pG4-BS(+)/pG4-CS(+) RLFS versus background sequences  higher compared to the ratio of pG4-CS(+) RLFS. Overall, this suggests that pG4-BS and pG4-CS are more commonly embedded in RLFS regions than we’d expect due to chance. Promoter-associated RLFS are well-known transcriptional regulators and are reported to frequently co-localize with pG4-CS  our hypothesis that the co-localization of same strand RLFS and pG4-S (both pG4-BS and pG4-CS) in promoters are non-random, and that these DNA structures may constitute parts of a higher-order structural and functional unit.Next, we used a bootstrapping approach to select 100 samples of 1000 randomly selected RLFS regions and the same number of random background sequences. During the sampling of background sequences, we controlled for the length and chromosomal location, thereby eliminating chromosome size and sequence length-associated biases. In each of the RLFS and background samples, we determined the ratio of sequences overlapping with pG4-BS and pG4-CS in separate analyses. Our results revealed a statistically significant difference , BS15 (EGR1 promoter), and BS45 (BLZF1 1st exon)), displayed sharp G-tetrad associated NMR peaks (peaks in the region between 10.5 and 12.0 ppm on the NMR spectra), indicating the presence of a single major G4 conformation , BS26 (NF1 1st intron), and BS47 (CBX4 promoter)), showed broad/weak G-tetrad proton peaks associated with the presence of major + minor G4 conformations. pG4-BS with multiple nucleotide bulges (e.g. BS24 (RARA 4th intron), BS31 (ACTN 1st intron)) also showed a type of broadened NMR peaks, suggesting the presence of multiple G4 conformations for these sequences , BS26 (NF1 1st intron), and BS47 (CBX4 promoter)), showed broad/weak G-tetrad proton peaks. The lack of well-defined signal peaks suggests the absence of a stable G4 conformation and/or the presence of alternative structures. Such structures  were considered as ‘non-G4’ structures.A few other sequences (e.g. BS19 (RGL2 promoter), BS47 (CBX4 promoter))  Figure . These o) Figure .It has been previously shown that bulges may decrease the stability of G4s with right-handed conformations . To asseNRP2 1st intron) indicated the formation of a single major G4 structure, while others (e.g. BS48 (GDF15 1st intron)) may instead adopt several different conformations depending on their environment. The CD spectra revealed that despite the higher number of interruptions, most of the tested sequences were able to form parallel G4s, similar to pG4-BS with a single bulge . This pG4-BS belongs to the G2B2 model. BS33 is located on the non-template strand and is 162 nucleotides upstream of the E2F8 TSS. Furthermore, BS33 is co-localized with experimental G4 signals, RLFS, CpG islands and ENCODE promoter-like elements  with our previous set of experimentally tested pG4-BS  our total collection includes 82 samples (n = 56), ‘true negative’ , ‘false positive’  or ‘false negative’ . Using exact categorical analysis (Cytel Studio), we found that the accuracy, specificity, sensitivity, and positive likelihood score of G4-BS models are 91.5%, 73.0%, 100% and 3.71, respectively. The weighted Kappa correlation coefficient value was 0.78 ; two-sided P = 1.35E-12).To quantify the accuracy of our pG4-BS models, we considered the results of our experimental validation as well as data collected from our previous studies that includes 33 potential G4-forming DNA oligomers . The G4- samples . The num3+N1-7G3+N1-7G3+N1-7G3+G pattern) overlook these sequences. Importantly, while more lenient bioinformatics algorithms exist today , currently used in most bioinformatics searches and experimental studies for identifying potential G4FS in the genomes.The construction of the G4-BS models is based on the results of our NMR, CD and thermodynamic stability assays. Using these results, we established three stable G4-BS models that allow for varying degrees of sequence alterations without compromising the thermodynamic and structural characteristics of these sequences. The results showed that the bulges can be formed within G4 under different conditions; however, our G4-BS models only select sequences with certain nucleotide compositions and positions of non-guanine nucleotides, predicting stable G4 conformations. While the sequences tested in this study can form stable G4 structures at an accuracy of 92%, all of them defy the description of the traditional G4 motif , thereby filtering for sequences with more favorable G4 forming ability.Ultimately, we selected three G4-BS models that are most likely to encompass pG4-BS possessing the stability required for G4-B formation and, as an extension, G4-related biological and pathobiological functions. We further implemented filtering steps to remove sequences with contiguous sequence . SpecifiOur search algorithm identified 1935686 pG4-BS, over 60% (1 169 397) of which do not overlap with any pG4-CS. These unique pG4-BS represent a population of alternative G4-S that could possess novel structural elements and DNA conformations with multiple gene regulatory functions. We observed differences in the population sizes of pG4-BS belonging to each of the three G4-BS models. These differences in the size of different pG4-BS populations could be explained by the variations in the underlying sequence. We expect sequences belonging to the G2B2 model to be generally less stable compared to G3B1, due to the presence of two bulges. Due to this reduced stability, G2B2 type pG4-BS could potentially be involved in molecular processes requiring the transient presence of G4s. At the same time, the reduced stability could explain the abundance of these sequences in the genome, as they are less likely to form unscheduled G4s (compared to G3B1 type pG4-BS).TP3 DNA sequence  inserted into the second stem. The reason it is excluded from our analysis is due to the presence of contiguous C bases in its sequence. The results of thermodynamic stability assays of TP3 show that some of the excluded sequences may indeed be capable of G4 formation. Nevertheless, based on our analysis of the FD of the occurrences of pG4-BS, we expect our method to capture the majority of true positive pG4-BS within the human genome. Based on the total length of merged pG4-BS regions, we observed that pG4-BS regions cover in total 0.26% (3 494 639 bp) of the non-template strands of human protein coding gene bodies and promoters. This number is more than three times the coverage observed for pG4-CS regions  in the same context.We note that while our models allow for the selection of pG4-BS that likely possess the ability to form stable G4-B, not all identified pG4-BS can do so. Additionally, there likely are sequences that can form stable G4-B, but that do not adhere to any of our models. One such example is the G4-B formed by the sequence . TP3 forP-value < 2.2e–16)  .in vivo detected sites of G4 formation in multiple cell lines. While the differences in sequence lengths between CUT&Tag peaks and pG4-BS and the lack of replicate experimental samples made cell line-specific comparison of pG4-BS profiles unfeasible  showed stronger enrichment than the most common G2B2 model. This suggests that sequences belonging to different models may form G4-B that could possess unique functions depending on their 3D structure. Many of the identified pG4-BS are located in biologically important regulatory loci. Out of the 20 715 pG4-BS regions mapping to the promoter regions of protein-coding genes over 89% intersected with chromatin accessibility regions and/or transcription factor binding sites. Taken together with the observation that pG4-BS is similarly strongly enriched in the TSS-proximal regions as pG4-CS; this suggests that such pG4-BS could exert regulatory functions in both normal and pathological conditions.The distribution of the number of pG4-BS regions per gene assumes the pattern of a skewed distribution, where a few observations are especially common, and most others are rare. Such distribution functions can be approximated based on the K–W model ,70  were highly enriched for the previously observed terms, while genes with pG4-BS regions located only on either strand were not. These findings suggest that the combined presence of pG4-BS on both strands may be required for the effective regulation of certain genes. Similar findings were observed for genes containing regulatory pG4-CS in their promoters: genes with bf-pG4-CS (e.g. pG4-CS present on both strands) were enriched for biological terms, while those with pG4-CS on a single strand only were not. In a follow-up analysis, we saw that regulatory G4-S presence on both strands was associated with biological and functional term enrichment. This indicates that G4-C and G4-B could work in tandem and constitute hybrid bf-G4-S architectures. Finally, we found that genes with both types of bf-G4-S are enriched with GO terms over genes with only one type of bf-G4-S pair. Taken together, our findings suggest that G4-S likely fulfill at least some of their biological functions not as single entities, but as part of a functional unit, that spans both DNA strands. Furthermore, the presence of both bf-pG4-BS and bf-pG4-CS may be necessary for specific G4-mediated biological functions. The exact reasons for this are not yet clear. One possible explanation is that the simultaneous presence of pairs of different types of pG4-S on both strands can enable the more profound stabilization of open chromatin and recruitment of transcription factors. While we did not observe a strong divergence between the distributions and localizations of pG4-BS and pG4-CS in our analyses, these findings do indicate that G4-S specificity may be essential in some contexts. Specifically, genes with only bf-pG4-CS did not show significant GO term enrichment, while we did observe GO term enrichment for gene sets with only bf-pG4-BS. The combined presence of different G4-S on both strands could be essential for fine-tuning the expression of certain genes by having multiple levels of regulatory G4 signals in place.In our GO analyses, we observed several highly significant enriched terms between gene sets containing the genes with regulatory pG4-BS or pG4-CS. These terms included GO categories related to development, transcriptional regulation, and the nervous system. Such categories were previously reported for pG4-CS and duplex stem–loop containing G4s ,45. InteIn the pathway enrichment analysis of the 2761 regulatory bf-pG4-BS(+) protein-coding genes, we saw the enrichment of multiple signaling pathways. This included pathways  that are on one hand crucial regulators of cellular homeostasis, survival and growth, and on the other are potential drivers of disease . Furthermore, many of the bf-pG4-BS-containing genes are associated with more than one signaling pathway. This overlap between pathway members could cause the transcriptional dysregulation of a single gene to affect multiple signaling pathways. The ability of G4s to regulate transcription has been well documented in recent publications ,85. Our Shank1 and Shank2  encode proteins that are members of the Shank family of proteins functioning as molecular scaffolds in excitatory synapses, and whose deletion was associated with increased susceptibility to autism spectrum disorder (Kirrel3 (274 associated pG4-BS regions) is a member of the transmembrane immunoglobulin superfamily and is expressed in the fetal and adult brain and kidney podocytes. Mutations of Kirrel3 have been associated with intellectual disability and autism (SORCS2 is another gene highly populated by pG4-BS regions (410 associated pG4-BS regions), whose mutations may carry the increased risk of reduced neuronal synaptic plasticity, bipolar disorder, attention deficit-hyperactivity disorder and schizophrenia (PTPRN2 (contains 669 pG4-BS regions), a protein tyrosine phosphatase previously shown to contain a large number of non-canonical G4-S of a different type , others either showed structural polymorphism (e.g. BS20:CASP8) or failed to form stable G4-B (e.g. BS42:UBE2D3).Accurate genome-wide prediction and identification of pG4-BS will further our understanding of the biological functions of these structures and broaden our definitions regarding G4s. To validate our findings, we selected a representative sample of pG4-BS from clinically relevant genes and carried out the structural characterization of these sequences using a combination of NMR, CD spectroscopy and UV melting experiments. The selected pG4-BS include sequences from various gene segments of cancer-associated genes, such as Our results suggest that the topology and stability of G4s formed by pG4-BS is highly diverse and is physically dependent on the underlying nucleotide's sequence composition. We hypothesize that the sequence composition of the loops connecting G-stems, and the nucleotide composition and location of the bulges all play a role in determining the G4-B-forming ability of these sequences. Generally, pG4-BS with fewer bulges (including both the number and length of bulges) tend to be more stable. However, even pG4-BS with a strong deviation from the G4-C motif can form stable G4-B, and thereby potentially fulfill biological functions. The highly diverse structural landscape of G4-B may allow for more varied roles compared to G4-C.NF1 . NF1 is a tumor suppressor, which was observed to be inactivated through loss-of-function mutations in mouse mammary tumors and human BCs  in the promoters of genes associated with transcriptional regulation, cancers and the nervous system. Finally, pG4-BS located in the proximal promoter of the E2F8 gene was experimentally validated by reconstructing a detailed folding topology of a G4-B with two G-stem bulges. pG4-BS forms novel and stable G4 structures, expanding the definition of physiologically and pathologically relevant G4-associated regulatory signals, and potentially revealing future therapeutic targets and regulatory molecules.In summary, we have, for the first time, modeled and validated DNA sequences that form stable G4-like structures Three experimentally validated bulged G4 sequence models for the identification of stable G4-B genome-wide are introduced.478263 merged pG4-BS were preferentially identified in the transcription regulatory sites of 90% of human protein-coding genes.Over 2800 genes with pG4-BS on both strands in the gene promoter region are overrepresented by the genes essential for transcription regulation, cell differentiation, ion channels, genome instability, immune response, cancer and neurodegenerative diseases.G4-B formation by a representative subset of pG4-BS associated with the human cancer genes located in the promoter or gene body regions was experimentally validated.G4-B conformations predicted by our G4-S models are highly enriched in independent G4-seq and G4 CUT&Tag datasets.E2F8 promoter.We predicted and reconstructed the 3D conformation of a stable G4-B in the https://doi.org/10.6084/m9.figshare.22110965. A UCSC genome browser session containing the tracks used in this study, including computational and experimental data is available (https://genome.ucsc.edu/s/Csaba_Papp/G4BS_project_Hg38). The track hub is also available via the URL https://github.com/pappc/g4_hg38/blob/main/bigBedFiles/trackDb.txt (archived version is available at: https://doi.org/10.6084/m9.figshare.22144427). Additional data from this study is also available in the supporting materials.Raw pG4-BS data is available in the Mendeley Data database under DOI: 10.17632/w37rx9hpb7.1. The processed datasets, including the coordinates of merged pG4-BS regions, merged pG4-CS regions and merged pG4-BS model-specific regions are available at the following link: gkad252_Supplemental_FilesClick here for additional data file.


Saccharomyces cerevisiae ENA1 gene, encoding a Na+-ATPase, responds transcriptionally to the alkalinization of the medium by means of a network of signals that involves the Rim101, the Snf1 and PKA kinases, and the calcineurin/Crz1 pathways. We show here that the ENA1 promoter also contains a consensus sequence, located at nt −553/−544, for the Stp1/2 transcription factors, the downstream components of the amino acid sensing SPS pathway. Mutation of this sequence or deletion of either STP1 or STP2 decreases the activity of a reporter containing this region in response to alkalinization as well as to changes in the amino acid composition in the medium. Expression driven from the entire ENA1 promoter was affected with similar potency by the deletion of PTR3, SSY5, or simultaneous deletion of STP1 and STP2 when cells were exposed to alkaline pH or moderate salt stress. However, it was not altered by the deletion of SSY1, encoding the amino acid sensor. In fact, functional mapping of the ENA1 promoter reveals a region spanning from nt −742 to −577 that enhances transcription, specifically in the absence of Ssy1. We also found that the basal and alkaline pH-induced expression from the HXT2, TRX2, and, particularly, SIT1 promoters was notably decreased in an stp1 stp2 deletion mutant, whereas the PHO84 and PHO89 gene reporters were unaffected. Our findings add a further layer of complexity to the regulation of ENA1 and suggest that the SPS pathway might participate in the regulation of a subset of alkali-inducible genes.The  Saccharomyces cerevisiae grows preferentially at acidic pH, and even a moderate alkalinization of the extracellular pH triggers an adaptive response that is largely based on the remodeling of gene expression affecting hundreds of genes. Work performed in the last 20 years has delineated in S. cerevisiae a number of signaling pathways that are responsible for such response, including the Rim101, Pho4, Slt2/Rlm1, Snf1 and PKA kinases, and calcineurin/Crz1 pathways [Adaptation to changes in the environment is critical for cell survival. The yeast S. cerevisiae ENA1 gene encodes a P-type ATPase able to export Na+ and K+ cations that become the only way to eliminate toxic Na+ cations in the absence of extracellular/intracellular H+ gradient. Under standard growth conditions, ENA1 is barely expressed, but its promoter is potently induced shortly after cells are shifted to a medium containing high levels of Na+ or under alkaline pH [ENA1 induction is predominantly due to the activation of the HOG pathway [ENA1 promoter to facilitate transcriptional activation [ENA1 by higher concentrations of Na+ also involves the activation of calcineurin [ENA1 promoter [The aline pH ,4,5,6,7. pathway , which a pathway  and recrtivation . Inducticineurin , which dcineurin ,12 and ipromoter .ENA1 is also potently induced by alkaline pH, and this effect is mediated by several mechanisms. One of them is the activation of calcineurin, promoted by a very fast influx of calcium cations into the cell [ENA1 expression seems to be also mediated by the regulation of calcineurin [ENA1 promoter, leading to transcriptional activation of the gene [ENA1 [ENA1 expression by high pH does not involve the HOG pathway nor the PKA-regulated Msn2/Msn4 transcription factors [ENA1 in response to environmental alkalinization [ENA1 in response to salt stress seem obvious, those derived from its activation by alkaline pH were less evident. This was clarified after the finding that the signaling network controlling the induction of PHO89 (encoding a high-affinity Na+/Pi cotransporter) in response to high pH was virtually identical to that identified for ENA1 [+ cations. Therefore, concomitant induction of ENA1 would counteract the accumulation of Na+, thus allowing maintenance of the extracellular/intracellular cation gradient required to sustain Pho89-mediated Pi uptake and/or prevent the intracellular accumulation of Na+ to toxic levels.As mentioned above, the cell . The impcineurin . A seconthe gene ,17. A thne [ENA1 . Inducti factors ,19, and for ENA1 . At alkaBioavailable nitrogen is indispensable for life, and yeast cells growing in different environments are exposed to a variety of nitrogen sources. Therefore, the sensing of such sources becomes essential for these cells in order to reprogram their metabolism for optimal proliferation . SensingENA1 promoter containing the known Nrg1 or Mig1/2 consensus sequences with the aim of down-regulating their basal expression. Surprisingly, whereas the Nrg1-containing fragment acted as expected, the one harboring the Mig1/2 sequence further activated expression. In this work, we demonstrate that this anomalous behavior is due to the existence of an Stp1/Stp2 consensus sequence adjacent to the Mig1 site. We also show that the elimination of components of the SPS pathway, but not that of the Ssy1 sensor, attenuates expression from the entire ENA1 promoter. These findings highlight a novel and unexpected regulatory input for the control of ENA1 expression and possibly other alkaline pH-responsive genes.During the course of our research on the generation of hybrid promoters able to respond to alkalinization, we incorporated to alkaline-responsive synthetic reporters short DNA segments from the CYC1 transcription sequence fused to eGFP and containing upstream consensus sequences for different alkaline sensitive transcription factors. PHO84 gene was placed upstream of the CYC1 basal transcription region. As shown in ENA1 promoter  a 60 nt region of the ENA1 promoter (−508/−567) known to contain a functional Mig1/2 repressor element (from now on denoted as MIG). However, to our surprise, the addition of this region did not repress the expression of the reporter but, on the contrary, resulted in a marked increase in GFP levels . Stp2 and its paralog Stp1  was also noticeable, yielding expression levels comparable to those observed in the stp2 deletion mutant for the native pMIG reporter. These results indicate that the MIG fragment of the ENA1 promoter contains a bona fide Stp1/2 consensus sequence that participates in the induction prompted by alkaline pH. They also suggest that Stp2 might be more important than Stp1 for regulation at this site.The transcriptional relevance of the putative Stp1/2 recognition sequence was investigated by cloning in the basal pG1, see . Both coENA1-derived β-galactosidase reporters (ENA1 promoter (reporter pMRK213) conferred a pH-dependent response to the reporter in wild-type cells, and this response was substantially decreased when the reporter was introduced in stp1 or stp2 deletion mutants. This region comprises the Nrg1/2, Stp1/2, and Mig1/2 sites, as well as a CRE (cyclic AMP response element) element, albeit the latter has no relevance in alkaline pH transcriptional regulation [STP1 or STP2 genes. These experiments indicate that the Stp1/2 site identified here could be relevant for the regulation of the induction of the ENA1 ATPase gene in response to environmental alkalinization.To confirm the relevance of the newly detected Stp1/2 site, we resorted to alternative eporters A previougulation . When thENA1 promoter could be sensitive to amino acid signals. To test this possibility, we introduced the basal pCYC1 reporter, as well as the pMIG and pG1 versions in the strain CEN.PK113-5D, which is prototrophic for amino acids and auxotrophic only for uracil. Cells were grown in a synthetic medium with Pro as the only N source (a condition known to maintain inactive the SPS pathway) and then switched to a medium with Leu or Phe to activate the SPS pathway. As shown in ENA1 promoter was responsive to the activation of the SPS pathway. To explore in more detail the involvement of the SPS pathway in this response, we resorted to the 23344c background, a Σ1278b-based strain in which mutations in the pathway are available and have been widely characterized. As shown in As mentioned in the introduction, Stp1 and Stp2 transcription factors are known to be activated through the SPS pathway, which responds to amino acid availability. We then wondered whether the Stp1/Stp2 site present in the GGGG-3′ to 5′-TGCATCG-3′. As shown in We also tested the response of the pMIG and pG1 reporters in the 23344c genetic background upon alkalinization and incorporated a new reporter (pG2) in which the Mig1/2 site was changed from 5′-TGCENA1 gene, we resorted to the pKC201 reporter, which is a LacZ translational fusion containing the entire ENA1 promoter region, and introduced this construct into the wild-type BY4741 strain and its stp1Δ and stp2Δ derivatives. As shown in ENA1 was potently induced by alkalinization, and mutation of either STP1 or STP2 significantly decreased its expression. This suggests that both transcription factors might operate on the ENA1 gene. When the expression of the entire ENA1 reporter was analyzed in the 23344c background, simultaneous removal of both Stp1 and Stp2 transcription factors resulted in a marked decrease of the expression, and a similar effect was observed in cells lacking the Ptr3 and Ssy5 components of the signaling pathway  located between positions −681/−565.From this experiment, it was evident that the promoter region responsible for the abnormal behavior of PHO84-, PHO89-, TRX2-, SIT1-, and HXT2-LacZ-based reporters in the wild-type 23344c strain and its stp1 stp2 derivative. As shown in PHO89 and PHO84 reporters did not decrease when these were tested in the stp1 stp2 strain. For TRX2-, HXT2-, and SIT1-based reporters, the induction by alkaline pH was modest, but this was likely the result of abnormally high basal expression levels (see the Discussion section). In any case, both basal and pH-induced expression levels decreased moderately in the stp1 stp2 strain for the TRX2 and HXT2 reporters, whereas they strongly diminished in the case of SIT1. To further confirm these results, we tested the HXT2-based reporter in the BY4741 background. As shown in HXT2 reporter was more robust and, whereas the deletion of STP2 did not significantly alter expression levels, that of STP1 resulted in around a decrease of 25%. This observation further supports the notion that HXT2 might be under the control of the Stp1 transcription factor. Taken together, these results suggest that alkaline pH-responsive genes other than ENA1 could be subjected to regulation by the transcription factors acting downstream of the SPS pathway.We then wondered whether the participation of the SPS pathway could be observed in other gene promoters also responsive to alkaline pH. To this end, we tested previously characterized ENA1 promoter contains a previously unidentified sequence that is recognized by the Stp1/2 transcription factors and is relevant for response to the alkalinization of the medium. This sequence is located at position −553/−544 and is able not only to enhance the expression driven from other alkali-responsive transcriptional elements but also capable by itself of increasing the expression of a basal CYC1 promoter in response to alkalinization of the medium. In addition, this element responds to the shift from Pro as the sole N source to Phe or Leu, a situation that is known to trigger signaling through the SPS pathway [AGP1, BAP2, DIP5, or MUP1 [The results presented here indicate that the  pathway , and the or MUP1 ,34,35,36 or MUP1 .ENA1 is a key factor for tolerance to salt and alkaline pH stress and responds to the activation of multiple pathways in response to various signals, including nutrient availability [ENA1 is repressed by glucose in comparison with galactose [ENA1 also has the potential to respond to the activation of the SPS pathway, which senses external amino acids. Indeed, we show that the expression of ENA1 in response to both alkaline pH and moderate salt stress  in the Ssy1-deficient strain  regulation, was affected by the ssy1 mutation independently of the presence or absence of leucine (used to induce the SPS pathway) in the medium [GAT1, encoding a GATA transcription factor whose expression is normally down-regulated in nitrogen-rich media. This scenario would fit with the previous observation by Crespo and coworkers [ENA1 expression was induced in cells treated with rapamycin (an inhibitor of the TOR pathway). In addition, mutation of GLN3 and GAT1, encoding the positive NCR transcription factors known to act downstream TOR, resulted in reduced basal and salt-induced expression of ENA1 in response to salt stress.These findings lead us to two conclusions. From one side, the absence of effect of the e medium . Thus, ioworkers  that ENAENA1 promoter region spanning from −681 and −565 would be explained by a release from the NCR control. Gln3 and Gat1 are called GATA factors because they bind to (A/T)GATA(A/G) sequences. Crespo and coworkers [ENA1 promoter . Interestingly, one of these is located at positions −638/−633 and, therefore, is present in the two reporters tested here (pMRK212 and pMRK605) that exhibit abnormal induction in Ssy1-deficient cells. It is worth noting that a role for Gat1 in the regulation of ENA1 in Hansenula polymorpha has been proposed in response to the nitrogen source [S. cerevisiae.Therefore, in this scenario, the abnormal activation driven by the oworkers  already n source . HoweverENA1 gene, as a loss of response is also observed for TRX2, HXT2, and SIT1 reporter genes when both STP1 and STP2 genes are deleted  when cells were shifted from ammonium to various amino acids [SIT1 appears also dependent on the presence of Ssy1 [TRX2 , HXT2  and SIT1 . In contrast, we do not observe the loss of alkaline pH induction in the stp1 stp2 mutant when the PHO84 and PHO89 reporter genes are tested  plus 2% glucose except otherwise stated, or synthetic medium (YNB without amino acids) lacking the appropriate selection requirements . The strEscherichia coli DH5α cells were employed as the plasmid DNA host and were grown at 37 °C in the LB medium  supplemented, when required, with 50 μg/mL ampicillin. Transformations of E. coli and standard recombinant DNA techniques were performed as described [S. cerevisiae cells were transformed by the lithium acetate method [escribed . S. ceree method .URA3, 2µ) reported in [CYC1 terminator, was amplified from the pUG35 vector  with primers GFP-term Fw and GFP-term Rev to provide pGFP-CYCterm. Vector pCYC was made by amplification of 269 bp fragment, containing the basal CYC1 promoter region, from the pAMS366 plasmid [CYC1 sequence and a 4x tandem of the CDRE site present in the FKS2 gene) with primers 4x-CDRE5′ and CYC1promoter3′ and cloning into pGFP-CYCterm upon XhoI digestion.Constructs using eGFP as a reporter protein were based on the pRS426 , pMRK706 (−753/−666), and pMRK605 (−681/−565) were made by amplification of the relevant region  and grown until an OD600 of 0.5–0.6. Then, 292.5 μL of culture were added onto 96 well plates and supplemented  with 7.5 µL of KOH from a 1 M stock to raise the pH 7.9–8.0 or with the same volume of 1 M KCl (control cells). Plates were transferred to a Varioskan LUX Multimode Microplate Reader , and growth resumed at 28 °C with intermittent shaking. Fluorescence (relative fluorescence units at 488 nm excitation/520 nm emission) and absorbance (600 nm) were simultaneously measured every 15 min using the SkanIt™ software . Normalization of the fluorescence values was conducted by dividing by the corresponding OD600 values after subtracting the fluorescence and absorbance blank values.For determination of alkaline pH induction, aliquots of cultures of BY4741 or 23344c cells transformed with the appropriate reporters were grown until an OD600 of 0.5–0.6 and were made 25 mM KCl (non-induced) or 25 mM KOH (raising the pH to 7.9–8.0). Samples (0.8 mL) were taken at time zero and after different periods and fixed with formaldehyde (2.7%) for 5 min. After various washes with cold PBS, cells were diluted 1:20 to 1:40 with PBS and analyzed in a FACSCanto  flow cytometer.600 = 0.6. Samples were taken just before adding Phe or Leu at 1.5 mM (t = 0) and then after different periods of incubation and processed for flow cytometry as described above.Monitoring the reporter’s response to amino acids was carried out as follows. A colony of the wild-type strains CEN.PK113-5D or 23344c (as well as its derivatives), transformed with the suitable plasmids, was grown in a minimal medium with 10 mM Pro as the only amino acid source to saturation and then inoculated into the same medium and grown until ODLacZ reporters was determined as in [HXT2-LacZ reporter, YP supplemented with 4% glucose was used, as described in [The activity of the ed as in , except ribed in . Inducti


FactorFinder, a tool that identifies CTCF binding sites at near base-pair resolution. We leverage this substantial advance in resolution to determine that the fully extruded (CTCF-CTCF) state is rare genome-wide with locus-specific variation from ~1–10%. We further investigate the impact of chromatin state on loop extrusion dynamics, and find that active enhancers and RNA Pol II impede cohesin extrusion, facilitating an enrichment of enhancer-promoter contacts in the partially extruded loop state. We propose a model of topological regulation whereby the transient, partially extruded states play active roles in transcription.DNA looping is vital for establishing many enhancer-promoter interactions. While CTCF is known to anchor many cohesin-mediated loops, the looped chromatin fiber appears to predominantly exist in a poorly characterized actively extruding state. To better characterize extruding chromatin loop structures, we used CTCF MNase HiChIP data to determine both CTCF binding at high resolution and 3D contact information. Here we present  Topological alterations and subsequent changes in enhancer-promoter (EP) contacts can modify gene expression4 and cause aberrant phenotypes8. CCCTC-binding factor (CTCF) can act as an extrusion barrier through its ability to bind and stabilize cohesin on DNA, serving to preferentially localize and anchor one or both ends of cohesin loops. Genes with promoter-proximal CTCF binding sites have been shown to have increased dependence on distal enhancers11, although the exact mechanisms involved are not well understood.Topologically associated domains (TADs) and regulatory enhancer-promoter chromatin loops are largely formed by the cohesin complex through the process of CTCF-mediated loop extrusion12. Recent live cell-imaging studies of two TADs found that the fully extruded state with a loop formed between two convergent CTCF-bound anchors was present only 3–30% of the time14. While these findings suggest that CTCF loops spend the vast majority of their time partially-extruded, the partially-extruded state has not yet been well characterized.Although conventional 3C techniques give an impression of static loops, cohesin-mediated chromatin loops are actually dynamic with an extrusion rate of ~1kb/s11. Putting this together with the high prevalence of partially extruded CTCF-mediated loops, we hypothesize that promoter-proximal CTCF sites enable gene regulation by halting cohesin on one side while cohesin continues to extrude on the other side. Enhancers then slow down extrusion, thus enabling an increase in EP contacts without requiring a fully extruded loop. The relationship between EP contacts and transcription can be nonlinear such that small increases in EP contacts may cause large changes in transcription4. As a result, even minor decreases in extrusion rate through enhancer regions may affect gene expression.Several studies have found evidence of promoter-proximal CTCF binding sites (CBS) having large impacts on EP contact frequencies and transcription18. A key advantage of using MNase over sonication-based protocols is the shorter fragment size obtained, which directly leads to higher resolution TF binding site identification. More recently, MNase DNA fragmentation has also been applied to proximity ligation assays to map 3D genome architecture with nucleosome (~150 bp) resolution, enabling precise characterization of 3D architecture including at TAD boundaries and punctate enhancer-promoter interactions22. Since MNase HiChIP enables precise characterization of both TF-binding and 3D contacts, it is uniquely poised to define how CTCF enables 3D contacts.The ability of MNase to efficiently digest naked DNA while sparing protein-bound DNA has been employed in various strategies to footprint the binding sites of proteins such as transcription factors with near base-pair resolutionTo better characterize the partially extruded chromatin loop state, we first develop a computational technique for high-resolution footprinting of CTCF using MNase HiChIP data. We then employ this to study how, through its interaction with the looping factor cohesin, CTCF can facilitate long-range DNA contacts. We further characterize how the length of loops extruded by cohesin is affected by local chromatin state factors such as enhancer and RNA Pol II density.23 with a CTCF antibody to profile 3D architecture in K562 cells, generating 150 bp reads with over 380 million unique pairwise contacts across four replicates. Briefly, following cell fixation with DSG and formaldehyde, chromatin is digested by MNase, immunoprecipitated to enrich for CTCF-bound DNA, and free ends are then ligated. After reverse-crosslinking, the resulting ligation products are sequenced from both ends and the mapping locations of the paired reads can be used to infer chromosomal locations of the physically interacting loci. In cases where the pre-ligation fragments are shorter than the read length it is also possible to infer the fragment length as the ligation junction position will be observed within one or both of the reads. If multiple fragments within a read are short enough to be aligned to distinct genomic locations, this is termed an ‘observed ligation’  HiChIPigation’ .24  fragments have a 10-fold higher overlap frequency with CTCF motifs than long (>120 bp) fragments  fragments are depleted at CTCF binding sites, they still represent a significant fraction of reads at these sites  and downstream (negative strand) of the CBS . We refeQi, I ∈ {1,2,3,4}. Because true CTCF binding induces a strong read pile-up in both quadrants 2 and 4 in addition to a depletion of reads in quadrants 1 and 3  are within 20 bp of a CTCF motif center, with a median separation of 5 bp  had contacts at substantially longer genomic distances than nucleosome-protected fragments , suggestmotif 28 .−10) for short TF-protected fragments as would be expected if these footprints represent CTCF/cohesin  interactions, however, is significantly enriched  and three CBS (−).We first estimated the frequency of fully extruded CTCF-CTCF chromatin loops genome-wide. By obtaining fragments overlapping CTCF binding sites and estimating the fraction of interaction partners overlapping a downstream convergent CTCF motif, we obtain 5% as the genome-wide frequency of the fully extruded CTCF-CTCF state.. We also find a wide CBS to CBS variability with an estimated range of ~1–10% . This su31) from the interaction length distribution. To determine the impact of chromatin state on cohesin extrusion, we first annotated the 1 MB regions downstream of FactorFinder identified CBS with ChromHMM states32  than through active regions (144kb), corresponding to a difference in average extruded loop size of ~110kb, p < 10−10  right. T−10 . This esoop size .We sought to establish that the observed differences in loop extrusion length as a function of chromatin state are not confounded by locus-specific effects on cohesin extrusion. Each CBS has locus-specific genetic architecture and a different number of overlapping fragments, so we fit a linear mixed effects model to account for this group-level heterogeneity. Specifically, we compute the ‘cohesin effect’ on loop length, defined as the increase in average interaction length for CTCF/cohesin bound fragments compared to nucleosome bound fragments for each CBS. Controlling for the background interaction frequency of a region in this way confirms that cohesin-associated loops are significantly shorter in active chromatin  left. TaFactorFinder, a transcription factor footprinting method for MNase HiChIP data and used it to identify CTCF binding sites with near base-pair resolution. We show that the DNA protection footprints of nucleosomes and transcription factors can be readily distinguished based on pre-ligation fragment size and strand origin and use these features to identify CTCF binding sites. Significance is then assessed through a multinomial approach, which avoids placing distributional assumptions on read counts. Using this method, the median distance between FactorFinder peak summits and motif center is 5 bp, with 93% of peak summits identified within 20 bp of a CTCF motif center.We have developed 14.We then leverage this methodological advance to investigate how chromatin state affects cohesin extrusion dynamics. A close examination of CTCF-protected fragments revealed an additional CTCF-adjacent footprint downstream of the CBS, which we propose represents cohesin given its positioning relative to looping orientation as well as its strong association with both long range interactions and cohesin occupancy. We estimated the frequency with which a CTCF bound locus forms a loop with a downstream CTCF site and found that it varies considerably from CBS to CBS, with a genome-wide range from ~1–10%. This is consistent with recent live-cell imaging work that found that CTCF-mediated loops predominantly exist in the partially extruded state at two studied lociFactorFinder identified CBS and HiChIP 3D contact information to look at differences in extruded loop size in regions with different chromatin states. We observe an approximately 2-fold increase in extruded loop size comparing quiescent chromatin to active chromatin, and this effect is similarly observed when examining the impact of H3K27ac and RNAPII binding. Our finding that RNAPII binding obstructs cohesin-mediated loop extrusion is consistent with two recent studies that investigated RNAPII’s impact on cohesin through RNAPII and enhancer perturbations35 as well as polymer simulations, CTCF depletion, and Wapl knockout experiments36. These substantial differences in average extruded loop size observed for different levels of RNAPII binding and H3K27ac suggest that gene and enhancer activity obstruct cohesin-mediated loop extrusion.We next sought to characterize how cohesin impacts genome contacts in different chromatin contexts. To this end, we employed our high-resolution 37.The obstruction of cohesin by gene and enhancer activity implies a model of CTCF-mediated gene regulation where a fully extruded, stable, and convergent CTCF-CTCF loop is not required for CTCF to mediate enhancer-promoter contacts. Instead, a promoter-proximal CTCF can halt cohesin next to the TSS of a gene while cohesin continues to extrude on the other side, effectively behaving as an enhancer recruiter. Cohesin slowing down through enhancer regions would then enable an enrichment of enhancer-promoter contacts without requiring a stable CTCF-CTCF loop . This at11, that enhancer-promoter contacts are unstable39, and that CTCF and cohesin-mediated chromatin loops are dynamic14. The dynamic nature of EP contacts has contributed to the development of the “kiss and kick” model40 as a potential explanation for how enhancers and promoters come into contact but move away from each other at the time of transcription. Our findings are compatible with the “kiss and kick” model, but additionally suggest a potential mechanism by which distal enhancers can locate gene promoters without being stuck in a stable conformation. This model would use promoter- or enhancer-proximal CTCF sites to enable distal enhancers to both come into contact with gene promoters and subsequently disengage during transcription. In this way, CTCF’s role in long-range enhancer promoter contact would be as a dynamic functional element recruiter instead of mediating continual stable contact between distal enhancers and gene promoters.The dynamic CTCF-mediated enhancer-promoter contact model proposed here is consistent with recent findings that promoter proximal CTCFs have important roles in gene regulationFour MNase K562 CTCF HiChIP (150 bp paired-end) libraries were generated using the Cantata Bio / Dovetail Genomics MNase HiChIP kit. CTCF MNase HiChIP was performed as described in the Dovetail HiChIP MNase Kit protocol v.2.0. Briefly, 5 million K562 cells per sample were crosslinked with 3mM DSG and 1% formaldehyde and digested with 1ul MNase (“YET” samples) or 2ul MNase (“GW” samples) in 100ul of 1X nuclease digestion buffer. Cells were lysed with 1X RIPA containing 0.1% SDS, and CTCF ChIP was performed using 1500ng of chromatin (40–70% mononucleosomes) and 500 ng of CTCF antibody . Protein A/G beads pull-down, proximity ligation, and library preparation were done according to the protocol. Libraries were sequenced to a read depth of ~172 million paired end reads per sample on the Illumina Nextseq 2000 platform.https://github.com/aryeelab/cohesin_extrusion_reproducibility. Data figures in this paper were made in R v.4.1.2 using ggplot.Preprocessing, analysis and figure code used in this paper are available at Raw and Processed HiChIP data produced in this study will be uploaded to NCBI GEO (GSE Record ID pending).CTCF27 .K562 ChIP-seq RAD21 BED file (Accession ID: ENCFF330SHG), CTCF BED file (Accession ID: ENCFF736NYC), CTCF bigWig signal value (Accession ID: ENCFF168IFW), RNAPII BED file (Accession ID: ENCFF355MNE), and H3K27ac BED file (Accession ID: ENCFF544LXB) were obtained from ENCODE, and CTCF motifs were obtained from the R package 41. Ligation events were then recorded using pairtools parse v. 0.3.042, PCR duplicates were removed, and the final pairs and bam files were generated. HiChIP loop calls were then made using FitHiChIP Peak to Peak29 with 2.5kb loop anchor bin size. The MNase HiChIP processing protocol is based on guidelines from https://hichip.readthedocs.io/en/latest/before_you_begin.html. Reproducible code is available at https://github.com/aryeelab/cohesin_extrusion_reproducibility.4 replicates of K562 MNase CTCF HiChIP data were aligned to the reference genome using the BWA-MEM algorithmFactorFinder as the set of CTCF binding sites. This p-value threshold was chosen based on the precision recall curve (We use CTCF motifs identified as significant (p < 1e-05) by ll curve , and corFactorFinder sliding window. The Benjamini-Hochberg method43 was used to obtain q-values.For genome-wide footprinting analysis adjustment for multiple testing, CTCF motifs are assigned the p-value of the closest The cohesin footprint is observed by obtaining motif-level coverage estimates +/− 80 bp around CBS, summing up the coverage across all motifs (within strata), and subtracting the upstream coverage from the downstream (downstream coverage - upstream coverage) at each base pair. Note that downstream and upstream are defined relative to the motif strand, so downstream is to the “left” of CBS (−) and to the “right” of CBS (+) in terms of reference genome base pairs. The aforementioned strata are defined by RAD21 ChIP-seq signal level (high vs low) and whether the fragment is the upstream or downstream interaction partner in its pair. RAD21 ChIP-seq high and low correspond to the top 25% and bottom 25% of ChIP-seq signal value of the adjacent (within 50 bp of CBS) RAD21 ChIP-seq peak. Note that only mid-size (fragment length between 80 and 120), long range fragments (interaction length > 10kb) are used for this analysis.34 by dividing this estimate by two. The point estimate (5%) is the number of interaction partners overlapping a downstream convergent negative strand CTCF motif genome-wide / the total number of fragments genome-wide, and the range (1–10%) are the 1st and 99th percentile of the CBS-level CTCF-CTCF chromatin loop estimate.We estimated a genome-wide range for the fully extruded state by obtaining CTCF/cohesion-protected upstream fragments overlapping CBS (+) and estimating the fraction of interaction partners overlapping a downstream convergent negative strand CTCF motif. CBS (+) were required to have at least 50 CTCF/cohesin-protected upstream fragments overlapping the motif to enable sufficient sample size for the motif-specific percent convergent calculation. We then accounted for CTCF occupancy (estimated as ~50%)32 to quantify the percentage of bp assigned to each of the 15 chromatin states. To simplify annotation, we grouped the 15 chromatin states into three categories  based on their correlation (44 (We used upstream fragments overlapping CTCF binding sites (+) for this analysis. 1 MB regions downstream of the CBS (+) were annotated using ChromHMMrelation . Regionstion . High/low RNAPII corresponds to the top 10% and bottom 10% of the number of RNAPII ChIP-seq peaks located in the 1 MB regions downstream of CBS (+). Extruded loop size estimates were obtained in the same way for these annotated regions; long range TF-protected fragments were used to estimate the average log10 interaction length.25.Interestingly, the strength of the nucleosome positioning signal is related to the orientation of the DNA contact. Stratifying nucleosome-bound fragments based on whether they are the upstream or downstream long-range (>10kb) fragment in a pair (effectively single-cell left or right loop anchor) produces a differential nucleosome signal inside and outside the loop . For bot1


Integrins are integral to cell signalling and management of the extracellular matrix, and exquisite regulation of their expression is essential for a variety of cell signalling pathways, whilst disordered regulation is a key driver of tumour progression and metastasis. Most recently non-coding RNAs in the form of micro-RNA (miRNA) and long non-coding RNA (lncRNA) have emerged as a key mechanism by which tissue dependent gene expression is controlled. Whilst historically these molecules have been poorly understood, advances in ‘omic’ technologies and a greater understanding of non-coding regions of the genome have revealed that non-coding RNAs make up a large proportion of the transcriptome.This review examines the regulation of integrin genes by ncRNAs, provides and overview of their mechanism of action and highlights how exploitation of these discoveries is informing the development of novel chemotherapeutic agents in the treatment of cancer. MiRNA molecules have been the most extensively characterised and negatively regulate most integrin genes, classically regulating genes through binding to recognition sequences in the mRNA 3′-untranslated regions of gene transcripts. LncRNA mechanisms of action are now being elucidated and appear to be more varied and complex, and may counter miRNA molecules, directly engage integrin mRNA transcripts, and guide or block both transcription factors and epigenetic machinery at integrin promoters or at other points in integrin regulation. Integrins as therapeutic targets are of enormous interest given their roles as oncogenes in a variety of tumours, and emerging therapeutics mimicking ncRNA mechanisms of action are already being trialled. Cell surface molecules involved in adhesion are vital to cancer progression as they mediate cell interactions with their surrounds and bridge these interactions to intracellular signals that change gene expression. A particularly important group of these molecules are the integrins. Human integrins comprise 24 protein heterodimers, composed from 18 α and 8 β subunits [Several integrins have been shown to be important in cancer progression including the formation of distant metastases associated with poor survival. There has been extensive characterization of the integrin family of adhesion molecules in tumour progression, reporting a myriad of expression changes during tumour progression . HowITGA1, ITGA2, ITGA4, ITGA7, and ITGA9 [ITGA2) are known to be regulated by histone modifications including H3K27me3 [Regulatory mechanisms controlling integrin expression are diverse and complex and a large body of evidence now exists showing that epigenetic mechanisms play an important role in regulation of integrins. Epigenetic mechanisms encompass heritable changes in DNA expression that do not change the genetic sequence. DNA is organised around nucleosomes with nucleosome positioning and chemical modifications contributing to transcriptionally accessible euchromatin, transcriptionally inaccessible repressive and compact heterochromatin, and boundary regions . By adjund ITGA9 –31, and H3K27me3 , 32. MorThe ncRNAs are diverse regulators of epigenetics, transcription, and translation, that provide another facet of gene regulation. NcRNAs, and lncRNAs (long non coding RNA) in particular, account for most of the genes in human cells  . MicroRNMiRNAs are small RNAs (averaging 22 bp) and are transcribed as a primary transcript (pri-miRNA), and often with multiple miRNAs transcribed together in the pri-miRNA as a miRNA family . CanonicMultiple miRNAs can engage single mRNAs and conversely, a given miRNA can engage 100s of mRNAs, allowing great complexity in regulatory abilities, with miRNAs acting as tumour suppressor genes (TSGs) or oncogenes depending on their target . MiRNAs ITGΑ2, ITGA5, ITGAV, and ITGB3 through 3′-UTR interactions, and while ITGB1 and ITGB5 are not targeted directly, the loss of their heterodimer partners results in a reduction in their protein expression [ITGA2, ITGB1, and ITGB3 appear to have the most distinct miRNAs targeting their mRNAs   [Amongst the plethora of miRNAs now described, there are several that are notable master regulators in key pathways. MiR-31-5p is a master regulator of integrins in metastatic BC and PC cells, impairing metastatic adhesion and spreading on ECM components collagen, laminin, and vitronectin . MiR-31-pression . Many inpendent) . For sompendent) . The mecITGB3 rs3809865 T/A variant was significantly associated with oral squamous cell carcinoma risk and reduced integrin expression, the SNP being in a putative MRE for miRNAs miR-26b, miR-330, and miR-324-5p [ITGB4 rs743554 G/A allele, at a miR-34a MRE, was associated with BC progression, reduced survival, and metastasis [ITGB5 A/C variant of rs2675, which occurs at a putative MRE for mir-192, miR-215, miR-449, and miR-504, had increased risk of bladder cancer, but no relationship with recurrence-free survival [ITGAV with a GC variant of rs11902171 had a decreased risk of PC, presumably by disrupting the interactions of predicted micro RNAs  [ITGΑ2 3′−UTR contains SNPs rs6898333 and rs6880055 that have been associated with PC risk, and these SNPs flank MREs including that of miR-373-3p [The interaction between a miRNA and a MRE is dependent on sequence complementarity, and therefore sequence changes (genetic lesions or polymorphisms) to a response element may decrease how strongly an interaction occurs, possibly permitting unchecked upregulation of an otherwise downregulated gene. Such mutations have been reported, though infrequently, in integrin genes. Three beta group integrins have such sequence changes. The R-324-5p . ITGB4 rtastasis . The ITGsurvival . As for R-373-3p , 81.Mutations can also occur within the miRNA gene itself to influence integrin gene regulation. Mutation of the seed sequence of miR-184 inhibits its ability to compete with miR-205 for a MRE in the ITGB4 mRNA 3′-UTR, though this was not described in a cancer context .− phosorylated − β1 pathway essential for PC metastases where integrin adhesion was essential  [DNMT3B, preventing ITGΑ1 promoter methylation and thus upregulating α1β1 [The interactions of miRNAs with integrin genes can also occur by a fascinating variety of other mechanisms. A relatively common, and likely important mechanism in cancer progression, are the several TSG miRNAs modulating integrin expression by targeting upstream activators and epigenetic machinery controlling integrin gene transcription. MiR-124-3p directly downregulates Talin 1 mRNA, blocking the activation of β1 integrins and thus their downstream (often oncogenic) signalling including kinase cascades, EMT progression, and pro-invasive MMP2/9 expression . For conastases) . MiR-760astases) . The latastases) . In a meing α1β1 .These diverse examples of how miRNAs regulate integrin dependent pathways, merely suggest at the immense number of possible miRNAs and pathways that may be relevant to integrins when not considering those miRNAs that directly target the integrin mRNAs themselves.cis or trans [2.1.1 lncRNAs acting as competitive endogenous RNAs (ceRNAs) via miRNA regulation, 2.1.2 lncRNAs that directly complement a mRNA transcript, 2.1.3 lncRNAs guiding transcription factors to integrin promoters, and 2.1.4 lncRNAs mediating integrins via interactions with chromatin or recruitment of epigenetic machinery. Each category is defined below, with examples listed in Table Long non-coding RNAs (LncRNAs) are a far more diverse and poorly understood group of transcripts when compared to miRNAs, with 20,000–68,000 potentially functional lncRNA genes in the human genome, far outnumbering miRNA or protein coding genes , 88. Altor trans –92. The Perhaps the most commonly reported mechanism of lncRNA gene regulation is by acting as competitive endogenous RNAs (ceRNAs), sequestering miRNAs that would otherwise bind integrin mRNA 3′−UTR regions (and thus upregulating the integrin). This mechanism is depicted in Fig. ABHD11-AS1 permits ITGA2 − p-AKT − p-P85(PI3K) − p-AKT1 signalling for proliferation, invasion, and migration [HOXA11-AS permits ITGA9 induced cutaneous melanoma progression and EMT [NEAT1 facilitates ITGA5 induced p-FAK signalling, cyclin D1 (proliferation) and antiapoptotic prevention of cleaved caspase 3 formation [SNHG16 increases ITGA6 mediated EMT [TUG1 facilitates ITGB1 mediated EMT and MMP2/9 upregulation [In recent years the number of putative ceRNA interactions involving integrins has increased greatly  that can be either oncogenic miRNAs (UCA1 thus acting as a TSG) or TSG miRNAs (UCA1 thus an oncogene) [ITGA2 by sequestering one of these miRNAs (miR-107 in PC) [Several putative ceRNAs are reported to sequester multiple miRNAs. The lncRNA ncogene) –111, wit7 in PC) .ITGB8-AS1 was found to upregulate ITGA3, ITGA5, and ITGB3 by sequestering four different miRNAs  [Regarding other integrin genes, e ceRNA) .LncRNAs often engage mRNAs directly via complementary interactions between the sequences, the RNA–RNA duplex coordinating with RNA binding heterogeneous nuclear ribonucleoproteins (hnRNPs) to form lncRNA − hnRNP − mRNA complexes. These complexes either stabilize mRNA and promote translation of oncogenes or destabilize mRNAs (downregulating the target gene) –114. ThiRP11 downregulated SIAH1 and FBXO45 mRNAs via interactions with the CDS and 3′−UTR regions of the mRNAs [I2ALR were in the ITGA2 mRNA UTR [in silico.Interestingly, the binding sites are often in the 5′−UTR and 3′−UTR, e.g., lncRNA he mRNAs , while amRNA UTR . This inITGB2-AS1, transcribed in antisense to ITGB2, upregulated the integrin via a 231 bp complementary region to the mRNA, this upregulation resulted in BC progression [I2ALR, transcribed in antisense from a promoter adjacent to the integrin gene, which acted as a TSG against the oncogenic ITGA2. The mechanism of action was hypothesized to be mRNA to lncRNA complementary interactions (in 5′ and 3′−UTR regions) in coordination with some RNA binding proteins [Examples of this mechanism of integrin regulation are limited. gression . Integriproteins .A limited number of lncRNAs have been demonstrated to upregulate integrins by recruiting traditional DNA binding transcription factors to gene promoter elements, thus acting more as transcriptional regulators rather than through epigenetic mechanisms.LINC00355 interacted with the ITGA2 promoter and recruited transcription factor GTF2B, upregulating ITGA2 to promote proliferation and metastasis of colon cancer [MALAT1 upregulated several integrins to promote metastasis in BC, and while this was likely through unspecified chromatin interactions at promoters, the lncRNA was generally co-localized with transcription factors Sox5 and Sox9 among others (discussed later) [LncRNA n cancer . The oncd later) . As an oHOTTIP blocks expression of its bidirectional transcript HoxA13, preventing the HoxA13 transcription factor upregulating ITGA1 expression [HOTTIP also coordinates with the upregulation of miR-101, which downregulates DNMT3B, preventing methylation of ITGA1, and thus enabling transcription and access by transcription factors such as HoxA13 [In the normal physiological context of chondrogenic differentiation, lncRNA pression . The dows HoxA13 . This laEcCEBP and Dali block DNMT1 to upregulate genes in cis or trans respectively, the latter binding transcription factors at active genes whereupon the lncRNA blocks DNMT1 [Dum and Dacor1 bind DNMTs to guide their methylation [lncRNA-p21 guides DNMT1 as well as suppressive histone methyl transferases [FOXP1-IT1) upregulate targets in cis by inhibiting histone deacetylases and removing repressive histone linker variants to promote histone acetyl transferase (HAT) and RNA Polymerase II access [XIST/TSIX in X-chromosome inactivation or global PRC2 repositioning by lncRNA HOTAIR in cancer to upregulate oncogenes and downregulate TSGs [Many lncRNAs have been implicated in gene regulation by interacting with the chromatin, including the modification of chromatin or DNA by recruiting or blocking epigenetic machinery. For example, lncRNAs ks DNMT1 , 116. Byhylation , 118, ansferases , 119. OtI access , 92. TheI access . Many lnate TSGs , 122. Otate TSGs .MALAT1 in BC promotes proliferation, metastasis, and adhesion by upregulating multiple integrin genes  and their associated focal adhesion pathways, as well as cytokine receptor genes [MALAT1 and lncRNA NEAT1 coordinated their chromatin interactions to bind to active chromatin of the transcription start sites (TSS) and transcription termination sites (TTS) of genes, but also were enriched at nuclear speckles/paraspeckles where they formed splicing scaffolds for ITGA2b and other genes [LOC284454 in BC acted as an anti-proliferative/migratory TSG and downregulated ITGΑ2 via direct engagement to chromatin as well as p68 [AY927503 upregulated integrin ITGAV  by engaging promoter histone linker H1.2, 1.3, and 1.4 and removing histone linker variant H1FX (via NCL chaperone), inducing transcriptionally active histone modifications (H3K9/14ac and H3K4me3), and decreasing suppressive histone modifications such as H3K27me3, and permitting RNA Polymerase II access [AY927503 regulation of ITGAV was dependent on a small (150 bp) domain of the lncRNA engaging H1FX specifically [AY927503 would be responsible for sequence recognition of the ITGAV promoter. Finally, lncRNA BORG directly bound the TRIM28 epigenetic mediator and guided it to the ITGA6 promoter in BC stem cells (and metastatic MDA-MB-231 BC cells) to promote α6 upregulation dependent self-renewal, stemness, tumorigenicity, and metastasis [Regarding integrins, their regulation through this grouping of lncRNAs is diverse, as may be expected. LncRNA or genes . MALAT1 er genes , 124. Lnl as p68 . The lnctastasis . TRIM28 tastasis . In the tastasis . As a meHOXD-AS1 (downregulated to promote CRC progression) recruited PRC2 to the HOXD3 promoter, silencing it with H3K27me3 histone modifications, thus preventing the HOXD3 transcription factor from upregulating the oncogenic ITGB3 [Epigenetic machinery regulating lncRNAs can also upregulate integrins by controlling upstream regulators of integrins. For example the TSG lncRNA ic ITGB3 . Given tPICSAR downregulated ITGA2, ITGA5, and ITGB1 mRNAs and SRC kinase (c-Src), thus reducing α2β1 and α5β1 col I and fibronectin adhesion and increasing metastasis, and permitting increased adhesion independent migration [LINC01354 in osteosarcoma upregulated ITGB1 mRNA  in a manner consistent with a ceRNA  but no investigation of the mechanism was made [BLNCR was transcribed in antisense from a shared promoter with ITGB1, with the lncRNA localized in the cytoplasm and both genes correlating in expression, consistent with a ceRNA mechanism [Further lncRNAs regulate integrin genes by unknown mechanisms. In these instances, the mechanisms were not investigated by study authors, or they were unable to be investigated due to problems encountered while trying to test mechanisms. In squamous-cell carcinoma (SCC) lncRNA igration . This stwas made . LncRNA echanism . Howeverechanism .ITGA3 (adjacent promoters) was not investigated as a direct regulator of ITGA3, but it downregulated all components of the p-AKT/p-GSK3B/β-catenin signalling cascade as a TSG, a pathway known to be regulated by ITGA3 [LncRNA PICART1, transcribed antisense to by ITGA3 . PICART1LncRNAs and miRNAs are not current targets of chemotherapy but their apparently widespread action across different cancers has resulted in strong interest in their possible use as chemotherapy targets or biomarkers of disease. However, the incomplete understanding of these transcripts  presently hinders realisation of this potential. Antisense oligonucleotides (ASOs) are one such therapy, these are synthetic single stranded deoxyribonucleotides (DNA of 12–30 bps) that bind their target RNAs (complementary base pairing) and decrease expression via RNase H (which degrades the RNA in a DNA–RNA heteroduplex). ASOs can also sterically block translation or modify RNA-splicing, by inducing exon skipping or conversely inclusion, as is the case for the FDA approved eteplirsen and nusinersen which are approved for the treatment of specific forms of Duchenne muscular dystrophy and spinal muscular atrophy respectively (Reviewed—). The soThe ASO gapmers, Inotersen and Volanesorsen are two of the few therapeutics approved that act via RNase H, binding to the transthyretin mRNA (knocking down a defective form of the transcript) and apolipoprotein CIII mRNA, in transthyretin amyloidosis and familial chylomicronemia syndrome respectively , 131. InTherapeutics that act as small interfering RNAs (siRNA or RNAi) are also in use. These are introduced in the double-stranded form, comprise only RNA bases, and bind to and degrade mRNAs via recruiting a RISC complex, similar to a native miRNA . FrequenSMN-AS1 to prevent it silencing the neighbouring SMN2 gene via PRC2 recruitment [Whilst the large size of many lncRNAs precludes their suitability for use as therapeutics themselves, both ASOs (including ASO ‘gapmers’) and siRNA-based therapeutics may be employed to specifically target oncogenic lncRNA molecules. Various ASOs, siRNAs, and similar molecules have been extensively used in preclinical studies to explore ncRNA functionality and regulation , and theruitment .Efficient delivery of ASO/siRNAs to specific tissues and cancers is challenging as the molecules are large, prone to degradation, and are hydrophilic. Innovative approaches such as chemical modifications, and the use of lipid nanoparticles, oligonucleotide conjugates, and oligonucleotide coated metallic nanoparticles for targeted delivery, have been developed to overcome some of these limitations . For exaOverall, ncRNAs regulate the majority of integrin genes in a wide range of cancer subtypes, and are likely to be critical for the functioning of these integrin genes in cancer progression. These molecules regulate the integrins in complex pathways, however knowledge of their mechanisms of action and delineation of their scope of activities will assist in rationalizing approaches to target these molecules therapeutically. The recent development of tissue specific delivery mechanisms for ASOs/siRNAs, highlights that the mechanisms to deliver ncRNA targeting therapies to specific tissues or the tumours themselves offers great promise. This is of particular importance given the dearth of effective therapies for the treatment of many tumour types once they have progressed to metastatic disease.