Saccharomyces cerevisiae lacking known elongation regulators, including RNA processing factors, transcription elongation factors, chromatin modifiers, and remodelers. We found that the opposing effects of these factors balance transcription elongation and antisense transcription. Different sets of factors tightly regulate Pol II progression across gene bodies so that Pol II density peaks at key points of RNA processing. These regulators control where Pol II pauses with each obscuring large numbers of potential pause sites that are primarily determined by DNA sequence and shape. Antisense transcription varies highly across the regulatory landscapes analyzed, but antisense transcription in itself does not affect sense transcription at the same locus. Our findings collectively show that a diverse array of factors regulate transcription elongation by precisely balancing Pol II activity.Understanding the complex network that regulates transcription elongation requires the quantitative analysis of RNA polymerase II (Pol II) activity in a wide variety of regulatory environments. We performed native elongating transcript sequencing (NET-seq) in 41 strains of  Transcription is a highly regulated and conserved process that consists of three phases: initiation, elongation, and termination . Post-inEscherichia coli polymerase transcribing the lac operon and lambda DNA  that measure Pol II density genome-wide at nucleotide resolution. Collectively, these techniques have highlighted the control of transcription elongation by regulatory factors. These approaches and other nascent RNA sequencing methods visualize the production of transcripts from RNA polymerases across the genome , and theNET-seq, PRO-seq, and other high-resolution methods have revealed both regions of high Pol II density, such as promoter proximal pausing, and specific sites of Pol II pausing across gene bodies . RegionsPol II transcribes much of the genome in all eukaryotes, yet only a fraction of its transcripts mature into stable, protein-coding RNA products . A majorSaccharomyces cerevisiae mutant strains lacking known elongation regulators. We investigated how each factor regulates nascent transcription, production of antisense transcripts, and pausing across gene bodies. Surprisingly, across these regulatory contexts, we find that antisense transcription at a locus does not affect its sense transcription. Metrics describing each transcription phenotype span a broad dynamic range with wild-type activity lying near the center. The loss of each factor revealed distinct sets of pause sites that we used to create machine learning models of Pol II pausing, highlighting which genomic features can classify pause positions. Together, our results show that Pol II transcription elongation is determined by the contrasting impacts of many regulatory factors.To gain insight into the regulation of the production of coding and non-coding transcripts by Pol II, we used NET-seq to analyze 41 S. cerevisiae, we individually deleted 41 non-essential transcription elongation regulators, including RNA processing factors, transcription elongation factors, histone variants, chromatin modifiers, and chromatin remodelers and chaperones, and assessed the transcriptional effects of each deletion using NET-seq , very few genes were transcribed at significantly altered levels relative to the wild-type, whereas upon loss of Rpb4, a subunit of RNA polymerase, over 10% of all protein-coding genes were differentially transcribed , a complex involved in autophagy, was enriched among upregulated genes in 17 deletion strains  or indirect mechanisms, such as altered chromatin states . HoweverDST1 had an effect on antisense transcription similar to its impact on sense transcription , poly(A) sites, and splice sites . At the cription .At the 3’ ends of genes, we observed changes in Pol II density upon loss of factors that regulate termination or polyadenylation. The screen included two subunits of the Ccr4-Not complex, which plays many roles in gene regulation including deadenylation . Deletiocac2∆ RNA-seq data  were deleted . CAF-I pseq data . We dete p=0.02; . Thus, C2=0.06; all R2 ≤0.10, p>0.05; To quantify Pol II pausing at each site, we defined a pausing index (PI), a length-normalized metric comparing Pol II density in the region of interest to that in the rest of the gene body . Interescdc73∆ to 2.81 in dst1∆, with wild-type at 1.68  can occur when the RT primer anneals internally within the nascent RNA rather than with the oligo ligated to the 3’ end . RT misp2=0.003, p=0.743; We calculated the pause site density, or the number of sites per kilobase, for genes that had sufficient coverage. The density varied widely across deletion strains , which chtz1∆ strain, consistent with its confined role at the +1 nucleosome  were also observed in chd1∆, consistent with their joint roles in maintaining chromatin structure  revealed that wild-type chromatin modifications were more powerful for Pol II classification (cbc1∆) to 0.82 (vps15∆), revealing the differences across the strains ; however, it performed the worst of all models in classifying pause sites in other deletion strains, obtaining a median AUC of 0.63 across them. These models indicate that the nucleotide sequence, DNA topology, position within a gene, and chromatin landscape all play roles in determining the location of Pol II pauses during transcription elongation.To ask whether features underlying Pol II pausing vary in different regulatory and chromatin landscapes, we built random forest models for each deletion strain. Across all deletion strains, an AUC of at least 0.78 was attained. These AUC values were only partially correlated with the total number of pauses detected in each deletion strain  sites, and both 5’ and 3’ splice sites. Interestingly, factors that impacted pausing at the 5’ ends of genes were not the same as those that impacted pausing at 3’ ends or at SS. Clearly, different mechanisms regulate Pol II pausing at different points during elongation. However, pausing around the TSS and pausing during antisense transcription were controlled by a similar set of factors that are highly enriched for established transcription elongation factors, such as Unexpectedly, we found that loss of the CAF-1 complex leads to pronounced Pol II peaks at 5’ and 3’ splice sites . The CAFWithin the regions of elevated Pol II density (e.g. TSSs and SS) and across gene bodies are discrete pauses at single nucleotides that represent locations where Pol II has a higher propensity to pause. This set of positions varies substantially across the deletion strains , indicatS. cerevisiae will serve as a foundation for learning more about transcription in multicellular eukaryotes.This work reveals the complex regulation of transcription elongation by a network of factors. In addition, it serves as a resource of NET-seq data to explore more specific hypothesis-driven research questions relating to individual factors and an open-source code base with which to analyze these data. Many of the transcription elongation regulators studied here are conserved in all domains of life, as are many of the transcriptional phenotypes we examined, including antisense transcription and Pol II pausing. These insights into transcription regulation in rpb3::rpb3-3xFLAG NAT)   was tran600=0.05 in 1 L of YPD medium and grown at 30°C shaking at 200 rpm until reaching an OD600=0.6–0.8. Cultures were then filtered over 0.45-mm pore size nitrocellulose filters (Whatman). Yeast was scraped off the filter with a spatula pre-chilled in liquid nitrogen and plunged directly into liquid nitrogen as described in cac1∆, cac2∆, and cac3∆, raw Fastq files were obtained from Cultures for NET-seq were prepared as described in ATCTCGTATGCCGTCTTCTGCTTG) was removed using cutadapt with the following parameters: -O 3 m 1 --length-tag ‘length=.’ Raw fastq files were filtered using PRINSEQ (http://prinseq.sourceforge.net/) with the following parameters: -no_qual_header -min_len 7 min_qual_mean 20 -trim_right 1 -trim_ns_right 1 -trim_qual_right 20 -trim_qual_type mean -trim_qual_window 5 -trim_qual_step 1. Random hexamer linker sequences (the first six nucleotides at the 5’ end of the read) were removed using custom Python scripts, but remained associated with the read. Reads were then aligned to the SacCer3 genome obtained from the Saccharomyces Genome Database using the TopHat2 aligner  and wild-type strains (four replicates) was performed using DESeq2  for all http://ontologizer.de/intro/)   using th2 RPKM of NET-seq reads was used. Analysis at coding genes ranged from 250 bp upstream of the TSS to 4000 bp downstream of the coding TSS. To allow comparison between mutant and wild-type samples, a pseudocount of 1 was added to every position in all samples before calculating the log2 RPKM. Differential heatmaps were calculated by taking the log2 ratio of mutant/ wild-type RPKM at each position.For analysis of antisense transcription, the coordinates of protein-coding transcription units from Pausing indices were calculated as the length-normalized Pol II density in the region of interest  sites, and ±10 bp around 5’ and 3’ splice sites) divided by the length-normalized Pol II density in the remainder of the gene, as illustrated in Only protein-coding, non-overlapping genes were included in the metagene analysis. The regions analyzed were –100 to +600 bp surrounding the most abundant TSS, –500 to +200 bp surrounding poly(A) sites, as identified in Cac2∆ and wild-type RNA-seq data were retrieved from 10 of pause strength (number of reads in pause) for each replicate was used as a proxy for pause score. IDR was calculated using the est.IDR function of the idr R package   . Reprodu, p=0.5) . Becauserent lab , these sPause density was calculated as the ratio of total number of pauses to the total length of the genome considered when extracting pause positions . To identify deletions that induced similar pausing patterns, 8644 pauses were found to be shared in at least eight strains and in regions sufficiently covered in multiple deletion strains. Shared pauses were visualized with a heatmap, clustered on both axes using the eisenCluster correlation clustering method in the hybridHclust R package , which tE-value significance threshold of 0.05  data and perform in-depth analysis of the dataset. Overall, the focus of this work was to determine the contributions of 41 transcription/chromatin related non-essential gene products to RNA Polymerase II transcription at different phases of transcription. This includes in-depth characterization of RNA Polymerase II pausing in each deletion strain and an analysis of sense and antisense transcription events. The introduction, which sets up the goals of the study, was very descriptive of transcription in general and lacked some focus discussing events that occur in multiple biological systems although this study was performed using yeast as the sole model system. It is stated that it is currently unknown how Pol II pausing contributes to gene expression levels however it could also be argued that Pol II pausing is, by nature, inhibitory to transcript production. Antisense transcription in yeast has also been shown by others to be inhibitory to sense transcription in multiple contexts including different yeast deletion backgrounds.The Churchman lab are leading experts in NET-Seq method development and data analysis and it is likely that the data produced for this study are of high quality. The major weakness in the context of this current study is that this study is NET-Seq focused with a lack of follow up experiments. This concern is partially mitigated by the breadth of the work that was performed. However, some data focuses on the reproducibility of specific events, such as Pol II pausing, and only two replicates were performed for each mutant. In fact, Figure S5A suggests that pausing reproducibility across the two replicates may be poor. Figures 4-7 focus on this pause data so lack of reproducibility of this measurement is a major concern.The data presented covers many of the 41 mutant strains that were used in the study. It does a nice job of describing both extremes of changes for each aspect that is discussed relative to the parental yeast strain. The study often references data from other studies to suggest potential interpretation of the results but no major follow up studies were performed to provide strength to these interpretations or glean new mechanism. Many of the findings support prior studies by others making this a useful resource yet not necessarily providing many novel insights. The uncertainly regarding the pause site reproducibility limits the potential impact of that portion of the work.Recommendations for the author:The manuscript by Couvillion, Harlen, Lachance et al., describes the effect of deleting a set of elongation-related factors on Pol II pausing and Pol II antisense transcription using NET-seq in budding yeast. Pausing and antisense transcription were extensively compared between genes/regions and between strains. Overall, the work generated mainly expected results but did not highlight any clearly new concept or finding. One unexpected observation is that deletion of subunits of the CAF-I histone chaperone led to increased pausing near splice sites but this observation was not pursued further.Although no major breakthrough came out of this work, the dataset generated in this study represents a valuable resource for the "transcription community" (notwithstanding a concern described below).Besides the lack of a major breakthrough, enthusiasm for the work in its current form is dampened mainly by the first two issues detailed below:1) I am concerned that the conclusions about pausing might be mitigated by noise in the pause site calls. First, I was surprised to see that in most cases, deleting elongation factor genes led to decreased pausing. Intuitively, I would have expected elongation factors to help suppress pauses, not promote them. This is notably unexpected for the spt4 mutant since Spt4 has been clearly shown to suppress pausing, yet the NET-seq data suggest the opposite.E. coli, further suggesting that the current study might include a large number of artifactual pause site calls.This peculiar observation (which is not commented on by authors) raised some suspicions about the pause site calls. Scrutinizing the NET-seq literature quickly revealed that NET-seq peaks can often occur consequent to technical artifacts . The Mayer lab recently published a version of NET-seq that limits these artifacts (https://doi.org/10.1093/nar/gkab208). Using this protocol, the Mayer lab found that mammalian Pol II pauses every 3,000-30,000 nucleotides. This is far less frequent than the 31 nucleotides suggested in the current work. While this may reflect differences between species, this reinforced my suspicions about these pause site calls. The sequence bias around paused sites is also different in the current study compared to previous work in mammals and Can the authors comment on the possibility that some (perhaps a lot) of their called pause sites are not bona fide, and to what extent this might have affected their conclusions? Is it possible for them to leverage some of the improvements described in the Mayer paper to test whether this would affect some of their key conclusions?2) I am concerned about the use of the antisense/sense ratio as a measure of antisense transcription. This is a convoluted measure that is affected both by changes in sense and antisense transcription. Hence, a change in the antisense/sense ratio simply can not be assimilated to an effect on antisense transcription; it may just as well reflect effects on sense transcription or a combination of both sense and antisense.This mitigates several of the conclusions made by the authors. For example, on p.11: "This result implies that strong antisense pausing suppresses antisense transcription, perhaps by promoting termination and thereby preventing antisense transcription deep into gene bodies". This conclusion is mitigated by the use of antisense/sense ratio as a measure of antisense transcription. It appears just as possible that strong antisense pausing stimulates sense transcription.Similarly, on p.18: "Indeed, differentially transcribed genes showed pronounced changes in their antisense:sense transcription ratios, especially for a subset of sensitive genes that are differentially transcribed in many of the deletion strains". By definition "differentially transcribed genes" means that sense transcription is affected. This alone will affect the antisense:sense ratio.A measure of the absolute antisense transcription levels in WT and mutant strains should be attempted. While it may be difficult to compare such measurement across strains, it would - in principle - be a more accurate measure of antisense transcription. I suspect that most conclusions will remain, but the current analysis is simply not sound.3) Figures 1 and 2 are quite descriptive and have some presentation challenges. For instance Figure 2D, E, & F appear to show very subtle changes. In the scale used for those figures it is difficult to see the changes that are occurring. It is recommended that a smaller range be used so that the changes can be more clearly visualized. Many of the changes have been previously reported although not using NET-Seq analysis to my knowledge. In these cases the NET-Seq data could be used as a higher quality resource and perhaps that aspect could be discussed .4) Figure 3 presents some interesting data that are novel to my knowledge. These novel findings, such as the contribution of CAF-I to Pol II density changes at splice sites, should be discussed in more depth to increase the novelty of the work.Other comments:a) The title seems inappropriate. "Dynamics of transcription elongation" suggest that elongation parameters (speed and processivity) were assayed, which is not the case. Instead, the paper focuses on pausing and anti-sense transcription. While these phenomena are linked to elongation, this does not justify the current title.b) I am surprised that histone chaperones notoriously linked to elongation  were not included in this study.c) The abstract mentions co-transcriptional processing (presumably RNA processing). Yet, I do not think that RNA processing was monitored in this study .d) check spelling of all forms of the word (and processes related to) ubiquitin. There are multiple spellings/typos.e) Features for the AI modeling are described as "chromatin features" but use features both within and outside of chromatin considerations. I would consider renaming.f) There is a missed opportunity for more in-depth discussion of transcription factor contribution to potential pause sites and for discussion of potential RNA binding protein contributions.eLife. Your revised article has been evaluated by James Manley (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Transcription elongation is finely tuned by dozens of regulatory factors" for further consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission. As this is a complicated paper with varying effects of different deletions etc. the reviewers thought that some issues need to be clarified in the text to make absolutely clear what robust conclusions can be drawn from this study.Essential revisions:1) The revised manuscript by Couvillion, Harlen, Lachance et al. is vastly improved. The authors have adequately addressed my main concerns. The only aspect that remains unclear to me concerns the fact that mutants for elongation factors such as dst1D and spt4D lead to decreased number of paused sites . As stated by the authors, this is unexpected since these factors are known to prevent  pausing. Consistent with this expected behavior , dst1D cells harbor a clear increase in Pol II density in the 5' region. This was highlighted in my initial review and the authors have addressed this by adding some speculations on page 12. This explanation, however, is vague and not compelling. One possible explanation would be that, in strains such as dst1D, pause sites are fewer but stronger. In this scenario, Pol II would pause less often but have a harder time getting out of the pause state in dst1D (and others) cells. Does the data allow testing this possibility? I feel that the manuscript would benefit from straightening that aspect.2) For the most part my major concerns have been addressed. The reproducibility of the experiments was carefully assessed  and the use of the irreproducibility discovery rate (IDR) with clear cutoffs sets clear quantitative standards for each dataset. It is clear that some of the knockout strains have a low overall impact on pausing and this is discussed through comparison of the median TSS pausing index.3) Much of my major concern is with the use of a discussion of machine learning being used for prediction of pausing location. The machine learning section appears to more clearly provide new models for the contribution of different DNA/chromatin features to changes in pausing observed in any individual elongation factor deletion strain. This point can be addressed through writing to clarify what the machine learning analysis actually provides rather than what it could potentially do (predict pause sites) but appeared to fall short of.4) I appreciate the care taken to address the concerns raised about the sense/antisense ratio analysis from the initial manuscript. This clarity of this section is much improved. I have a comment regarding this specific statement:"The factors whose deletions led to the largest increase in the antisense transcription were those involved in the regulation of histone acetylation, including members of the Rpd3S-Set2 pathway (Set2) and the major histone H4 acetyltransferase complex NuA4 (Eaf1), emphasizing the role of acetylation in antisense transcription ."For this statement, the deletion of an acetyltransferase will decrease acetylation whereas the deletion of members of the Rpd3S-Set2 pathway increase acetylation. As a consequence it is recommended to state "emphasizing the role of acetylation / deacetylation in antisense transcription" for clarity. [Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]In this study, "Dynamics of transcription elongation are finely tuned by dozens of regulatory factors" the authors present an impressive amount of native elongating transcript sequencing (NET-Seq) data and perform in-depth analysis of the dataset. Overall, the focus of this work was to determine the contributions of 41 transcription/chromatin related non-essential gene products to RNA Polymerase II transcription at different phases of transcription. This includes in-depth characterization of RNA Polymerase II pausing in each deletion strain and an analysis of sense and antisense transcription events. The introduction, which sets up the goals of the study, was very descriptive of transcription in general and lacked some focus discussing events that occur in multiple biological systems although this study was performed using yeast as the sole model system. It is stated that it is currently unknown how Pol II pausing contributes to gene expression levels however it could also be argued that Pol II pausing is, by nature, inhibitory to transcript production. Antisense transcription in yeast has also been shown by others to be inhibitory to sense transcription in multiple contexts including different yeast deletion backgrounds.The introduction has been edited to focus on yeast-specific results and the regulation of transcription elongation. We also clarified our discussion of Pol II pausing and antisense transcription (pg. 3-5).The Churchman lab are leading experts in NET-Seq method development and data analysis and it is likely that the data produced for this study are of high quality. The major weakness in the context of this current study is that this study is NET-Seq focused with a lack of follow up experiments. This concern is partially mitigated by the breadth of the work that was performed. However, some data focuses on the reproducibility of specific events, such as Pol II pausing, and only two replicates were performed for each mutant. In fact, Figure S5A suggests that pausing reproducibility across the two replicates may be poor. Figures 4-7 focus on this pause data so lack of reproducibility of this measurement is a major concern.Pol II pause sites that are not reproducible in NET-seq data arise due to stochastic technical fluctuations and biological fluctuations, which are especially pronounced at the single nucleotide level. To remove the pause sites that do not correspond across replicates, we used a stringent irreproducible discovery rate (IDR) analysis (IDR of 1%). The approach increases the number of pause sites considered initially, does not require an arbitrary threshold for pause site calling, and reduces false negatives. Thus, the irreproducible pause sites in Figure S5A do not represent a failure of the methodology, rather they arise as a result of the IDR analysis. Nevertheless, we understand the reviewer’s concern about the reproducibility of our pause sites, and we have added an additional analysis that compares our results across pairs of replicates, demonstrating that the majority of reproducible pauses overlap across sets of replicates . We edited the text in the Results (pg. 11) and Methods (pgs. 21-22) to explain our approach more clearly.The data presented covers many of the 41 mutant strains that were used in the study. It does a nice job of describing both extremes of changes for each aspect that is discussed relative to the parental yeast strain. The study often references data from other studies to suggest potential interpretation of the results but no major follow up studies were performed to provide strength to these interpretations or glean new mechanism. Many of the findings support prior studies by others making this a useful resource yet not necessarily providing many novel insights. The uncertainly regarding the pause site reproducibility limits the potential impact of that portion of the work.Recommendations for the author:The manuscript by Couvillion, Harlen, Lachance et al., describes the effect of deleting a set of elongation-related factors on Pol II pausing and Pol II antisense transcription using NET-seq in budding yeast. Pausing and antisense transcription were extensively compared between genes/regions and between strains. Overall, the work generated mainly expected results but did not highlight any clearly new concept or finding. One unexpected observation is that deletion of subunits of the CAF-I histone chaperone led to increased pausing near splice sites but this observation was not pursued further.Although no major breakthrough came out of this work, the dataset generated in this study represents a valuable resource for the "transcription community" (notwithstanding a concern described below).Besides the lack of a major breakthrough, enthusiasm for the work in its current form is dampened mainly by the first two issues detailed below:1) I am concerned that the conclusions about pausing might be mitigated by noise in the pause site calls. First, I was surprised to see that in most cases, deleting elongation factor genes led to decreased pausing. Intuitively, I would have expected elongation factors to help suppress pauses, not promote them. This is notably unexpected for the spt4 mutant since Spt4 has been clearly shown to suppress pausing, yet the NET-seq data suggest the opposite.We thank the reviewer for highlighting this possible point of confusion. In this study, we performed two types of analyses to look at Pol II pausing. The first is quantifying peaks of Pol II at different positions in the gene body . The other is the precise location on the DNA where Pol II prefers to pause. We found a strong trend for elongation factors to impact the peaks of Pol II at the start of genes near transcription start sites, which is consistent with their established roles. Where Pol II prefers to pause on DNA (i.e pause sites) is a different question and won’t necessarily depend on elongation factors. Pause site densities include only the locations at which Pol II typically pauses in many cells, so it is not a measure of the absolute frequency of Pol II pausing. In addition, the densities are not related to the Pol II catalysis rate. So, these transcription elongation factors may facilitate other aspects of transcription elongation or they only act locally to influence Pol II during specific points of regulation. We now include this discussion in the text .E. coli, further suggesting that the current study might include a large number of artifactual pause site calls.This peculiar observation (which is not commented on by authors) raised some suspicions about the pause site calls. Scrutinizing the NET-seq literature quickly revealed that NET-seq peaks can often occur consequent to technical artifacts . The Mayer lab recently published a version of NET-seq that limits these artifacts (https://doi.org/10.1093/nar/gkab208). Using this protocol, the Mayer lab found that mammalian Pol II pauses every 3,000-30,000 nucleotides. This is far less frequent than the 31 nucleotides suggested in the current work. While this may reflect differences between species, this reinforced my suspicions about these pause site calls. The sequence bias around paused sites is also different in the current study compared to previous work in mammals and We performed our pause site analysis on genes that were highly covered by NET-seq reads, which is possible in yeast where the nascent transcriptome is relatively small.Consistently, our pause site densities were not sensitive to the sequencing depth of our libraries . By contrast, it is too costly to sequence human NET-seq libraries deeply enough to determine all pause sites for many genes. Rather, the Mayer group searched for pauses in all genes regardless of coverage level, and the number of pauses identified was sensitive to the number of aligned reads used in the analysis. Thus, as they note, the number of pause sites they report is underestimated. Interestingly, they describe some genes where Pol II pause sites were identified quite frequently (every 146 nt), presumably due to high NET-seq coverage at those loci. The sequence motif differences between yeast and human pause sites could be due to species differences or the different subsets of genes analyzed in the two studies.Can the authors comment on the possibility that some (perhaps a lot) of their called pause sites are not bona fide, and to what extent this might have affected their conclusions? Is it possible for them to leverage some of the improvements described in the Mayer paper to test whether this would affect some of their key conclusions?We thank the reviewers for raising this critical point. Reverse transcription mispriming occurs when the RT primer associates internally within the nascent RNA instead of the oligo ligated to the 3’ end. Due to the large human genome and the long pre-mRNAs , these events occur more frequently in human NET-seq data. They are easily identifiable computationally because reads from mispriming events do not contain a unique molecular identifier that is added during the oligo ligation step. Furthermore, mispriming reads align proximal to a sequence that is the reverse complement of the start of the RT primer. We already remove these reads from our NET-seq analysis, although very few arise in our yeast NET-seq data. We were pleased when the Mayer lab developed the nested NET-seq protocol, because it is useful for our projects generating human NET-seq libraries. We did not expect the nested approach to impact our yeast data due to the relatively small yeast transcriptome. Nevertheless, considering the emphasis on pause sites in this study, we agree with the reviewers that we should determine whether the nested approach impacts yeast NET-seq data and pause site determination. We have now compared wild-type yeast data from libraries prepared using nested NET-seq to those prepared with the standard protocol. We found that in yeast, the genome is so small that using the nested NET-seq library approach does not change the number of pauses identified  nor does it decrease the fraction of pause sites with adapter-like sequence downstream, which is expected at sites of mispriming . Results of this analysis are now described on pages 11-12.2) I am concerned about the use of the antisense/sense ratio as a measure of antisense transcription. This is a convoluted measure that is affected both by changes in sense and antisense transcription. Hence, a change in the antisense/sense ratio simply can not be assimilated to an effect on antisense transcription; it may just as well reflect effects on sense transcription or a combination of both sense and antisense.This mitigates several of the conclusions made by the authors. For example, on p.11: "This result implies that strong antisense pausing suppresses antisense transcription, perhaps by promoting termination and thereby preventing antisense transcription deep into gene bodies". This conclusion is mitigated by the use of antisense/sense ratio as a measure of antisense transcription. It appears just as possible that strong antisense pausing stimulates sense transcription.Similarly, on p.18: "Indeed, differentially transcribed genes showed pronounced changes in their antisense:sense transcription ratios, especially for a subset of sensitive genes that are differentially transcribed in many of the deletion strains". By definition "differentially transcribed genes" means that sense transcription is affected. This alone will affect the antisense:sense ratio.A measure of the absolute antisense transcription levels in WT and mutant strains should be attempted. While it may be difficult to compare such measurement across strains, it would - in principle - be a more accurate measure of antisense transcription. I suspect that most conclusions will remain, but the current analysis is simply not sound.We thank the reviewer for the suggestion. We now analyzed antisense transcription without using a ratio to sense transcription. We achieved this directly using the statistical gene expression analysis package DEseq2 by annotating all the antisense transcripts. Furthermore, in the reanalysis of our data with DEseq2, we found fewer sensitive genes that were impacted by the removal of regulatory factors, and we decided to remove this analysis from the manuscript. These new analyses led to reformatted Figures 2 and S2 that combines the previous Figures 2 and 7.3) Figures 1 and 2 are quite descriptive and have some presentation challenges. For instance Figure 2D, E, & F appear to show very subtle changes. In the scale used for those figures it is difficult to see the changes that are occurring. It is recommended that a smaller range be used so that the changes can be more clearly visualized.We apologize, but we are unclear about what the reviewer is referring to. On our computer screens and printouts, the changes appear clearly.Many of the changes have been previously reported although not using NET-Seq analysis to my knowledge. In these cases the NET-Seq data could be used as a higher quality resource and perhaps that aspect could be discussed .We have edited the text to clarify where NET-seq data provides higher quality views of previously reported trends (pg. 8).4) Figure 3 presents some interesting data that are novel to my knowledge. These novel findings, such as the contribution of CAF-I to Pol II density changes at splice sites, should be discussed in more depth to increase the novelty of the work.We now discuss these findings in greater depth in the Discussion (pg. 17-18).Other comments:a) The title seems inappropriate. "Dynamics of transcription elongation" suggest that elongation parameters (speed and processivity) were assayed, which is not the case. Instead, the paper focuses on pausing and anti-sense transcription. While these phenomena are linked to elongation, this does not justify the current title.We have changed the title to “Transcription elongation is finely tuned by dozens of regulatory factors.”b) I am surprised that histone chaperones notoriously linked to elongation  were not included in this study.In this study, we only included non-essential factors. Unfortunately, many histone chaperones, including the ones mentioned, are essential, or their absence leads to extremely slow growth.c) The abstract mentions co-transcriptional processing (presumably RNA processing). Yet, I do not think that RNA processing was monitored in this study .We have removed this phrase from the abstract.d) Check spelling of all forms of the word (and processes related to) ubiquitin. There are multiple spellings/typos.We have done this.e) Features for the AI modeling are described as "chromatin features" but use features both within and outside of chromatin considerations. I would consider renaming.We have renamed “chromatin features” as “chromatin and DNA features.”f) There is a missed opportunity for more in-depth discussion of transcription factor contribution to potential pause sites and for discussion of potential RNA binding protein contributions.We now added to the Discussion to raise these points (pg. 18).[Editors’ note: what follows is the authors’ response to the second round of review.]Essential revisions:1) The revised manuscript by Couvillion, Harlen, Lachance et al. is vastly improved. The authors have adequately addressed my main concerns. The only aspect that remains unclear to me concerns the fact that mutants for elongation factors such as dst1D and spt4D lead to decreased number of paused sites . As stated by the authors, this is unexpected since these factors are known to prevent  pausing. Consistent with this expected behavior , dst1D cells harbor a clear increase in Pol II density in the 5' region. This was highlighted in my initial review and the authors have addressed this by adding some speculations on page 12. This explanation, however, is vague and not compelling. One possible explanation would be that, in strains such as dst1D, pause sites are fewer but stronger. In this scenario, Pol II would pause less often but have a harder time getting out of the pause state in dst1D (and others) cells. Does the data allow testing this possibility? I feel that the manuscript would benefit from straightening that aspect.E. coli RNA polymerase. It is unclear what percentage of Pol II pausing is expected to occur at the stereotypical sites and how that percentage changes upon the loss of regulatory factors. Nevertheless, as suggested by the reviewer, it is interesting to determine the overall pause strength at the stereotypical pauses, which we estimated by calculating the percentage of NET-seq reads at pause sites. The results of that analysis are shown in Figure 4 —figure supplement 1G.The pause sites detected in NET-seq data are only the “stereotypical” ones, meaning that Pol II stops at those positions across many cells. Pol II is likely to pause at many other sites randomly, as has been observed for the dst1∆ or spt4∆ cells than in wild-type cells. It is important to keep in mind that there could be substantial pausing at noncanonical sites in these strains. Indeed, Pol II density near transcription pause sites increases in dst1∆ and spt4∆ strains, reflective of more overall pausing in that region.This analysis shows that the Pol II density at stereotypical pause sites is lower in To increase clarity, we have added the following sentence on pgs. 10-11 to emphasize that our approach identifies only stereotypical pause sites and is blind to other Pol II pausing events. “Because NET-seq is performed in bulk on a population of cells, only the sites that consistently induce pausing are observed, and we refer to these as ‘stereotypical’ pause positions”. In addition, we added the following sentences to the end of the same paragraph.E. coli RNA polymerase pauses both at specific pause sites and randomly across a DNA template . Thus, Pol II is likely to similarly pause ubiquitously across gene bodies in noncanonical ways that would not lead to a detectable signal in NET-seq data. Nevertheless, the stereotypical pause sites identified here provide insight into the underlying features that induce Pol II pausing”.“Stereotypical pause sites in NET-seq data represent loci where Pol II pauses in many cells and represent a fraction of the overall pausing by Pol II. The 2) For the most part my major concerns have been addressed. The reproducibility of the experiments was carefully assessed  and the use of the irreproducibility discovery rate (IDR) with clear cutoffs sets clear quantitative standards for each dataset. It is clear that some of the knockout strains have a low overall impact on pausing and this is discussed through comparison of the median TSS pausing index.We are pleased that our edits addressed the reviewer’s concerns.3) Much of my major concern is with the use of a discussion of machine learning being used for prediction of pausing location. The machine learning section appears to more clearly provide new models for the contribution of different DNA/chromatin features to changes in pausing observed in any individual elongation factor deletion strain. This point can be addressed through writing to clarify what the machine learning analysis actually provides rather than what it could potentially do (predict pause sites) but appeared to fall short of.All references to “predicting pauses” have been changed to “classifying pauses”. We also added the following sentence to pg. 15 to clarify the role of the machine learning analysis. “Which features contribute the most to the random forest classifier can help shape models for the molecular underpinnings of stereotypical Pol II pausing”.4) I appreciate the care taken to address the concerns raised about the sense/antisense ratio analysis from the initial manuscript. This clarity of this section is much improved. I have a comment regarding this specific statement:"The factors whose deletions led to the largest increase in the antisense transcription were those involved in the regulation of histone acetylation, including members of the Rpd3S-Set2 pathway (Set2) and the major histone H4 acetyltransferase complex NuA4 (Eaf1), emphasizing the role of acetylation in antisense transcription ."For this statement, the deletion of an acetyltransferase will decrease acetylation whereas the deletion of members of the Rpd3S-Set2 pathway increase acetylation. As a consequence it is recommended to state "emphasizing the role of acetylation / deacetylation in antisense transcription" for clarity.The suggested edit was made on pg. 8.


The recent discovery of vast non-coding RNA-based regulatory networks that can be easily modulated by nucleic acid-based drugs has opened numerous new therapeutic possibilities. Long non-coding RNA, and natural antisense transcripts (NATs) in particular, play a significant role in networks that involve a wide variety of disease-relevant biological mechanisms such as transcription, splicing, translation, mRNA degradation and others. Currently, significant efforts are dedicated to harnessing these newly emerging NAT-mediated biological mechanisms for therapeutic purposes. This review will highlight the recent clinical and pre-clinical developments in this field and survey the advances in nucleic acid-based drug technologies that make these developments possible. Historically, therapeutic development efforts have focused on finding protein-targeting small molecules, predominantly with inhibitory or antagonistic effects. In such cases, the target proteins usually belong to the receptor, channel or enzyme functional classes . HoweverFortuitously, the recent discovery of vast non-coding RNA-based regulatory networks that can be easily modulated by nucleic acid-based therapeutics (NBTs) has opened numerous new therapeutic possibilities. Long non-coding RNA (lncRNA) class, and its natural antisense transcript subclass (NATs) in particular, play significant roles in these networks  and regulncRNA are defined as non-coding transcripts longer than 200 nucleotides. The NAT subclass of lncRNA comprises non-coding RNA that are transcribed from the antisense strand of the coding gene loci. In the current nomenclature NATs are distinct from the long intergenic non-coding RNAs (lincRNAs) that are defined as long transcripts expressed from intergenic regions. The most commonly proposed classification of NATs is based on their genomic position relative to their partner sense gene. In this case the groups usually include transcripts that are expressed from the DNA strand opposite to: 1) the coding gene’s promoter region (e.g. promoter upstream transcripts or PROMPTs); 2) coding gene introns or exons (termed overlapping or intronic/exonic NATs); 3) enhancers (e.g. eRNAs)  Agostin. HoweverDue to their chemical composition and 3D shape, NATs can efficiently scaffold complexes that contain both proteins and nucleic acids. Furthermore, due to their complementarity to DNA and RNA sequences, NATs can guide locus-specific or mRNA-specific targeting of regulatory protein complexes with genome-wide functions. As described in this review, NAT/protein complexes are known to regulate high level chromosome structures, epigenetic state of specific gene loci, transcription, translation, trafficking, subcellular localization and post-transcriptional modification of specific genes and mRNAs.As a result of recent successes in the development of diverse NBT modalities, including small interfering RNAs (siRNAs), antisense oligonucleotides (ASOs), therapeutic mRNAs, viral delivery vectors and others, NAT-mediated biological mechanisms are now accessible for precise gene-specific therapeutic modulation .This review will highlight the recent clinical and pre-clinical developments in the field of RNA-targeted therapeutics, survey advances in NBT technology and the increased understanding of nucleic acid-based mechanisms of action that make these developments possible.Extensive efforts dedicated to the development of NBTs can be explained, in part, by their ability to target multiple novel therapeutic targets from the wide ncRNA-based regulatory networks, rational design, simplified development cycle and alignment with the goal of achieving personalized medicine. The ability to precisely tailor NBTs to a unique disease or even a single patient can increase the efficacy of treatment and minimize off-target effects .The current range of NBTs includes multiple modalities, such as short (20–30 bases) single-stranded DNA (ssDNA) or double-stranded RNA (dsRNA) oligonucleotides, full length therapeutic mRNA and plasmid or viral-based vectors expressing single or multiple NBTs. In the cell, ssDNA oligonucleotides, termed ASOs, form a duplex with target RNA and induce its degradation by RNAse H. dsRNA oligonucleotides, termed small interfering RNAs or siRNAs, direct the target RNA to the RNA interference pathway, also leading to its degradation. Alternatively, these oligonucleotides can pair with and block protein- or RNA/DNA-binding sites on their target RNAs or, conversely, directly block the RNA-binding pocket on RNA-binding proteins. Inhibition of RNA-protein, RNA-DNA and RNA-RNA binding can also be achieved by oligonucleotide binding to any site on the nucleic acid or protein that would induce significant conformational change in these molecules. In all of these cases, oligonucleotides can be rationally designed to interact with high specificity with their target through base-pairing, with minimal off-target interactions .The development of NBTs was largely fueled by successes in chemical modifications of nucleic acids that increased nuclease resistance of DNA and RNA oligonucleotides. Phosphorothioate (PS) bonds and sugar moiety modifications, such as 2′O-methyl (2OMe), 2′-O-methoxyethyl (2MOE), fluoro (F), locked nucleic acids (LNAs), as well as phosphorodiamidate backbones and phosphorodiamidate morpholino oligomers (PMO), have already been used in approved drugs . These cFurther development of chemical modifications that permit administration of NBTs via alternative delivery routes is now underway. In parallel, advanced clinical techniques with less invasive administration routes, including inhalation, oral, or minimally invasive intranasal depot (MIND), are helping to expand the use of NBTs in the clinic . ChemicaFurthermore, dsRNA-based NBTs, such as siRNA or small activating RNA (saRNA), and therapeutic mRNAs require a lipid-based carrier for nuclease protection, immunogenic suppression and efficient entry into cells. Currently these areas are undergoing intense development .Technologies developed in the oligonucleotide field were utilized and inspired further development of therapeutic mRNAs. Additionally, codon optimization strategies, ‘non-natural’ caps and chemical modifications of nucleotides are being utilized to increase protein expression from therapeutic mRNAs. One of the significant successes in the therapeutic mRNA field is the RNA-based COVID19 vaccines .Another type of NBT, viral-based vectors, initially used in gene therapy, is now undergoing rapid development, with several approved drugs and many ongoing clinical trials . Gene thFor each therapeutic goal, the modulation of relevant RNA targets may be achieved using multiple therapeutic targets and NBT modalities. For example, several different therapeutic modalities are currently being pursued in spinal muscular atrophy, including gene therapy, therapeutic mRNA delivery, small molecule modulators of splicing and ASOs targeting different biological mechanisms .As more data about NATs involvement in regulation of well-studied and novel biological mechanisms becomes available, the applicability and therapeutic value of NAT-targeting NBTs steadily increases, further fueling the development of the NBT field.After the initial survey of cellular RNA conducted in the early 2000s revolutionized biology by revealing the presence of multitudes of ncRNA transcripts, extensive work has been directed at establishing their biological functions . This wolncRNA and NATs in particular are involved in the intricate regulatory networks that modulate gene expression at the level of transcription. Notably, this regulation substantially influences post-transcriptional processes as it can determine sequence and chemical modification of mRNA and its trafficking, subcellular localization and translation efficiency.As described below, the main role of NATs in transcription complexes frequently lies in guiding their assembly at specific gene loci. Focusing on the RNA components of the transcriptional machinery facilitates development of highly gene-specific NBTs. Targeting the ‘general purpose’ protein components of transcriptional complexes on the contrary can result in multiple off-target, pan-genomic effects.Regulation of transcription is highly complex and can occur at different levels . BesidesTranscripts that do not successfully complete these steps are detected by the mRNA surveillance machinery and targeted to RNA decay pathways, such as nonsense-mediated decay (NMD) pathway. These processing steps not only define the length and sequence of RNA transcripts, but also regulate RNA stability, localization, and translation and ultimately control transcript expression and protein levels. As described below, NATs have been reported to regulate many of these steps. In the following sections we give examples of multiple existing or proposed NBTs that can specifically modulate these NAT-mediated regulatory mechanisms.Imprinting is an epigenetic process in which expression of one of the 2 alleles of a gene is constitutively repressed. In an extreme case of imprinting, X-chromosome inactivation, the majority of genes on one copy of X-chromosome are shut down. Although imprinting mainly engages extra-long non-coding transcripts, frequently expressed over multiple gene loci and intergenic regions , particiIn some cases, targeting NATs that are known to participate in silencing of imprinted transcripts can be utilized in disease treatment. For example, in Angelman syndrome, insufficiency of ubiquitin protein ligase E3A (UBE3A) caused by mutations in maternally inherited allele can be rescued by de-repression of the imprinted paternal copy. Such de-repression could be achieved by targeting the Ube3a NAT (Ube3a-ATS) with ASOs. A single ICV injection of an anti-NAT ASO in a mouse disease model increased UBE3A protein in the brain to 74% of wild type levels. The increased UBE3A levels normalized behaviors in open field, forced swim and reversed rotarod tests and increased delta power in EEG recordings . This NAAddition of a methyl group to genomic DNA, termed DNA methylation, is an enzymatic reaction underlying multiple epigenetic processes that regulate gene expression, including imprinting, epimutations, modulation of splicing and others .Several mechanisms through which NATs coordinate DNA methylation have been described. The process of antisense transcription through CpG islands could lead to dissociation of factors that maintain their methylation status. Alternatively, NATs could form RNA:DNA:DNA triplexes with genomic DNA in the promoter regions to facilitate the binding of methylation protein complexes or inhibit activity of demethylases. Triplexes can be formed by homopolypurine/homopolypyrimidine regions of duplex DNA and single-stranded RNA molecules that insert into the major groove of DNA in a parallel or antiparallel orientation. The triplex is supported by Hoogsteen hydrogen bonding between RNA and dsDNA .In a disease-relevant example, lncRNA PARTICLE (promoter of MAT2A-antisense radiation-induced circulating lncRNA) is predicted to form triplexes at >1600 genomic locations including WWOX (WW domain containing oxidoreductase) and its neighboring gene MAT2A (methionine adenosyltransferase 2A) . PARTICLIn another example, activity of PEBP1P3, a NAT encoded by the opposite strand of the CD45  locus, initiates a complex chain of events that indicates involvement of DNA methylation in alternative splicing. Knockdown of PEBP1P3 led to decreased DNA methylation of CD45 gene at intron 2 and increased trimethylation of histone H3K9 and H3K36 at the alternative exons of CD45. Knockdown of PEBP1P3 also increased the binding levels of chromatin conformation organizer CTCF (CCCTC-binding factor) at intron 2 and alternative exons of CD45. These changes resulted in altered ratio of CD45 alternative splicing isoforms that may represent a therapeutic target in autoimmune or immune deficiency diseases .Recent studies have elucidated cases where changes in DNA methylation patterns at protein-coding genes mimic effects of disease-causing mutations by inducing haploinsufficiency. These cases are referred to as epimutations and can be separated into primary, induced by environment, and secondary, due to DNA mutations affecting components of epigenetic machinery that includes NATs.de novo appearance of a novel NAT due to a splicing mutation in the peroxiredoxin (PRDX1) gene. This mutation leads to activation of several cryptic splice sites and production of a read-through novel antisense transcript encompassing the promoter of the TESK2 (testis-specific protein kinase 2) gene and the bidirectional promoter of 2 genes, MMACHC  and CCDC163P (coiled-coil domain containing 163). The appearance of this new antisense transcript changes DNA methylation profiles in the CpG islands of the promoters, initiated by deposition of SETD2-generated H3K36me3 marks that facilitate binding of the heterochromatin protein 1 (CBX5) and recruitment of DNMT3B1 or DNMT2B. DNMT3B1/2B mediate methylation of CpG islands and silencing of MMACHC, CCDC163P and TESK2 genes. Phenotypically, it leads to epi-cblC, an inherited disorder of intracellular vitamin B12 metabolism with hematological, neurological and cardiometabolic outcomes. Silencing the PRDX1 gene partially reduced hypermethylation at affected promoters  and HBQ1 (hemoglobin-theta-1 locus) genes, initiating readthrough transcription of the antisense LUC7L (LUC7-like) gene over the HBA2  gene locus. This leads to hypermethylation of HBA2 CpG islands and silencing of HBA2 expression .Another case is described in a subset of Lynch syndrome patients where deletion of the upstream EPCAM  gene leads to expression of EPCAM/MSH2 (MutS homolog 2) fusion transcripts, MSH2 promoter methylation and inhibition of MSH2 expression . ModulatThe mechanisms that mediate communication between successive cellular generations could be viewed as a unique case of primary epimutation. nAS25, a NAT encoded in exon 25 of the notch-1 locus, transmits information about cell cycle perturbations from mother to daughter cells and confers cell cycle adjustments in progeny. nAS25, transcribed from a bi-directional E2F1-dependent promoter in the mother cell, is accumulated over the G1 phase of cell cycle thus providing a measure of G1 length. nAS25 is transmitted from mother to daughter cells, where it binds and stabilizes notch-1 sense transcripts in G0 phase to the level defined by nAS25 abundance. Resulting modulation of notch-1 signaling reprograms G1 phase in daughter cells to compensate for perturbations that occurred in the mother cell. NBT-mediated modulation of nAS25 would permit fine control of cell cycle duration .cis, or of a group of related genes in trans  gene, facilitating the recruitment of histone acetyltransferase p300 and E2F1 and thereby promoting transcription of DLX6 in endometrial cancer. Additionally, DLX6-AS1 could enhance cell proliferation and cancer development by sponging multiple miRNAs, including miR-26a (leading to up-regulation of PTEN), miR-203a (targeting MMP2), miR-197-5p (to relieve E2F1) and miR-199a. Silencing DLX6-AS1, on the contrary, inhibited tumor growth .in vitro and in vivo and significantly improved disease phenotype in a mouse model of Dravet syndrome  suppressed ZC3H12D (zinc finger ccch domain-containing protein 12D) expression by facilitating EZH2 recruitment and subsequent deposition of repressive H3K27me3 marks, thus promoting the progression of hepatocellular carcinoma (HCC) . KnockdoAnother antisense transcript, FAM83C-AS1 (family with sequence similarity 83 member C antisense 1) inhibits expression of SEMA3F (semaphorin 3F) through H3K27me3 methylation at the SEMA3F promoter. Stabilization of EZH2 is achieved by FAM83C-AS1-mediated recruitment of deubiquitinase ZRANB1 (zinc finger RANBP2-type containing 1). Inactivation of SEMA3F is associated with the development of colorectal cancer .in vitro and in vivo (A more complex chain of events was triggered by the antisense transcript CDKN2B-AS1 (cyclin-dependent kinase inhibitor 2B antisense). CDKN2B-AS1 recruited the CREBBP (CREB-binding protein) and SMYD3 (SET and MYND domain-containing 3) epigenetic-modifying complex to the promoter of NUF2 (Ndc80 kinetochore complex component) to facilitate H3K27ac and H3K4me3 deposition, thereby epigenetically activating NUF2 transcription. At the same time, CDKN2B-AS1 was stabilized through its interaction with IGF2BP3 (insulin-like growth factor 2 mRNA-binding protein 3). IGF2BP3 is upregulated in renal clear cell carcinoma together with CDKN2B-AS1 and NUF2. CDKN2B-AS1 knockdown suppressed cell proliferation and invasion  in vivo .Gene expression regulation involves the formation of three dimensional (3D) chromosomal structures that bring genomic regulatory elements, such as promoters or enhancers, into spatial proximity to each other . These iOne such NAT, STX18-AS1 (syntaxin 18 antisense 1), antagonized CTCF-mediated repressive 3D promoter interactions at the MSX1 (Msh Homeobox 1) gene, thereby augmenting MSX1 gene expression. MSX1 is a transcription factor essential for embryogenesis and cancer development .In another disease-relevant example, transcription of an ncRNA ThymoD (thymocyte differentiation factor) promoted demethylation at CTCF bound sites and activated cohesin-dependent looping which led to translocation of the Bcl11b (BAF chromatin remodeling complex subunit BCL11B) enhancer from the lamina to the nuclear interior. This led to the alignment of Bcl11b enhancer and promoter in a single loop and deposition of activating epigenetic marks across the loop . Bcl11b Another layer of regulation that affects the exact sequence and therefore the biological activity of the resulting protein comes into play during excision of intronic sequences from pre-mRNAs. RNA splicing is currently viewed as a tightly regulated co-transcriptional process that generates sets of mRNA isoforms, frequently with different functions, from the same pre-mRNA/genomic DNA sequence .The sequence of each isoform is determined by divergent use of the 5′ (donor) and 3′ (acceptor) splice sites in the pre-mRNA sequence. The splice site sequences are characterized by the presence of a 5′ sequence marked by the GU dinucleotide at the extreme 5′ end of the intron, followed by a downstream ‘branch point’ sequence marked by an A, a region high in pyrimidines  further downstream, and a splice acceptor site marked by an AG sequence that terminates the intron at the 3′ end. The usage of the splice sites can be regulated by lncRNAs  and protFurthermore, the multi-step splicing reaction is catalyzed by a ribonucleoprotein complex, termed spliceosome, that assembles at introns, as guided by the lncRNAs and splicing factors. The core spliceosome is composed of five small nuclear ribonucleoproteins (snRNPs) that contain small nuclear RNAs (snRNAs). snRNA U1 pairs with the 5′ splice sites in pre-mRNAs. The 3′ splice site and the polypyrimidine tract are recognized by U2 auxiliary factors 1 and 2 (U2AF1 and U2AF2). The branch point sequence at the splice site is bound by U2 snRNP. As a result, the splicing process extensively relies on RNA-RNA and RNA-protein interactions and can be readily targeted by NBTs.Indeed, multiple splice-modulating NBTs targeting pre-mRNA elements essential for splicing have been proposed . NusinerAlthough not yet at the clinical stage, NBTs targeting NATs that modulate RNA splicing have been considered. Knockdown of NATs frequently leads to altered expression of individual splice isoforms, which is of particular importance in cases where isoforms exhibit distinct physiological functions and/or full gene knockdown leads to serious adverse effects.One such case is the antisense transcript ZEB2-AS1 (zinc finger E box-binding homeobox 2 antisense 1) that binds the 5′UTR of Zeb2 pre-mRNA after epithelial–mesenchymal transition in cancer. Upon binding, ZEB2-AS1 modulated the spliceosome activity, facilitating the retention of an intron containing internal ribosome entry site (IRES) in Zeb2 mRNA. The IRES promotes cap-independent translation of Zeb2 protein and down-regulates E-cadherin promoting cancer development .+ handling and increased susceptibility to cardiac arrhythmias in response to catecholamine challenge  and CHKA  loci, respectively, altered the promoter usage of CERS2 and CHKA. HNF4A-AS1L selectively activated the P1 promoter of HNF4A , which is not associated with malignancy, while having no effect on the oncogenic P2 promoter .Another way of generating alternative isoforms is through the usage of alternative polyadenylation sites . 3′ end The polyA signal is recognized and proteolytically processed by a large protein complex consisting of four main subcomplexes (the cleavage and polyadenylation factor (CPSF), cleavage stimulation factor (CSTF), and cleavage factors I and II (CFI and CFII)). PolyA polymerase (PAP) then adds a polyA tail to the 5′ end of the cleavage product. PABPN1 (polyA binding protein 1) binds to the expanding polyA tail and controls its length by disrupting interactions between CPSF and PAP .NATs are known to be involved in the regulation of polyadenylation. One such example is AtLAS, a NAT from the synapsin II locus. AtLAS regulates alternative polyadenylation of synapsin II to increase expression of the synapsin 2b (syn2b) isoform. Syn2b binds the AMPA receptor (AMPAR) at the postsynaptic site and reduces AMPAR-mediated excitatory synaptic transmission. In mice, silencing or overexpression of AtLAS increased or decreased the social rank, respectively .The RNase P-mediated endonucleolytic cleavage plays a crucial role in the 3′ end processing and thus cellular localization of mRNA. TALAM1, a widely expressed NAT at the lncRNA MALAT1 (metastasis-associated lung adenocarcinoma transcript 1) locus, promoted the 3′ end cleavage and maturation of oncogenic MALAT1 RNA. TALAM1 preferentially localized at the site of transcription and directly interacted with MALAT1. Depletion of TALAM1 impairs 3′ end cleavage and reduces the levels of MALAT1. Transcription and stability of TALAM1 is positively regulated by MALAT1, establishing a feed-forward positive regulatory loop .N6-methyladenosine (m6A) methylation of RNA is a reversible modification that can affect alternative splicing, transport, stability and translation of mRNAs . m6A is m6A is deposited co-transcriptionally, likely by the METTL3/METTL14/WTAP m6A methyltransferase complex (MTC) within nuclear speckles. m6A can be dynamically removed by “erasers” such as FTO  or ALKBH5  in response to environmental stimuli including memory formation, cancer development and stress responses .m6A methylation of 3′UTRs and coding regions of mRNA is epigenetically regulated by deposition of H3K36me3. One example of NAT-regulated m6A deposition on a 3′UTR is GATA3 (GATA-binding protein 3) mRNA. GATA3-AS NAT binds to GATA3 mRNA and scaffolds m6A deposition on its 3′ UTR mediated by VIRMA (vir-like m6A methyltransferase-associated protein or KIAA1429). This leads to separation of the RNA-stabilizing protein HuR, degradation of GATA3 pre-mRNA, tumor growth and metastasis .m6A methylation of 5′UTRs is believed to be driven by NATs that bind directly to the 5′UTR and/or by recruitment of methyltransferase complexes (possibly Mettl14/Mettl3). miRNAs were also shown to mediate binding of Mettl3 to target sites on mRNAs. m6A modification of 5′UTRs is thought to be involved in cap-independent translation and thus facilitates the cellular stress response .In a disease-relevant example, UBA6-AS1 (ubiquitin like modifier activating enzyme 6 antisense RNA 1) directly associates with UBA6 mRNA and increases its m6A methylation by recruiting RBM15 (RNA binding motif protein 15), a key regulator of m6A deposition. IGF2BP1 (insulin like growth factor 2 mRNA binding protein 1) was identified as the m6A reader protein for this modification which enhanced UBA6 mRNA stability. Increased expression of UBA6 and UBA6-AS1 suppresses proliferation, migration and invasion of ovarian cancer cells .Removal of m6A modification can also be controlled by NATs. For example, FOXM1-AS increases the interaction of FOXM1 (forkhead box M1) and m6A demethylase ALKBH5. Demethylation of FOXM1 increased both FOXM1 expression and tumor growth. Depleting ALKBH5 or FOXM1-AS disrupted glioblastoma stem-like cell tumorigenesis .The m6A modification is also used to modulate NAT activity. For example, ALKBH5 demethylase-mediated m6A removal from KCNK15-AS1  upregulated KCNK15-AS1 levels and inhibited pancreatic cancer progression. KCNK15-AS1 bound to the 5′UTR of KCNK15 and inhibited KCNK15 translation. KCNK15-AS1 also recruited the MDM2 proto-oncogene (MDM2) to promote REST (RE1 silencing transcription factor) ubiquitination leading to upregulation of PTEN and suppression of the AKT pathway .in vitro  or promoter upstream transcripts, or PROMPTs) are generated, with low frequency, by the same promoter region as their partner coding gene transcripts, but are transcribed in the opposite direction, a process sometimes termed “divergent transcription” . An asymDivergent antisense transcription at promoters can be suppressed by the ZWC complex that is composed of ZC3H4 (zinc finger CCCH domain-containing protein 4), WDR82 (WD repeat-containing protein 82) and CK2 (casein kinase 2). The ZWC complex phosphorylates SPT5, a subunit of the transcription-elongation factor DSIF, resulting in divergent transcription inhibition. Reduced ZWC activity, imposed by depletion of ZC3H4, increases divergent transcription .One of the recently elucidated functions of pRNA is Pol II promoter-proximal pause release. In a therapeutically relevant example, induction of pRNA transcription by activated estrogen receptor-α (ERα) at more than 800 ERα target genes led to recruitment of H3K9me3 demethylases KDM4B (lysine-specific demethylase 4B) and KDM4C (lysine demethylase 4C) mediated by compact stem-loop structures in pRNA. This resulted in significant loss of H3K9me3 and release of HP1α and KAP1 factors from target gene promoters, thereby destabilizing the 7SK snRNP  complex and NELFA  that mediate promoter-proximal Pol II pausing . IntroduAs pRNAs represent the gene-specific component of the pause-release mechanism, NBTs targeted to pRNA allow for fine modulation of transcription and are likely to have fewer side effects than therapeutics targeting general purpose protein factors, thereby increasing the therapeutic potential.Enhancer RNAs (eRNAs) are “pervasive transcripts” generated by RNA Pol II. They are rapidly degraded by the nuclear exosome complex after 3′ endonucleolytic cleavage by the Integrator complex (Integrator) approximately 1–3 kb downstream of the transcription start site. Notably, PAF1C (polymerase-associated factor 1 complex) has a role in termination of eRNAs by facilitating recruitment of Integrator . At the Both long and short eRNAs play important roles in modulating transcription by regulating chromatin accessibility, histone modification and Pol II pausing through different mechanisms . One of Furthermore, eRNAs were shown to form local RNA-DNA duplexes (R-loops). For instance, 5′ capped antisense eRNA PEARL (Pcdh eRNA associated with R-loop formation) is transcribed from the protocadherin α (Pcdh) HS5-1 enhancer region and regulates Pcdhα gene expression by forming R-loops within the HS5-1 enhancer region. Upregulation of PEARL led to strengthened local three-dimensional chromatin organization within the Pcdh topologically associating domains .In another mechanism of action, eRNAs were shown to scavenge NELF from paused RNApol II, which led to RNApol II phosphorylation by positive transcription factor b (P-TEFb) complex and subsequent activation of pause-controlled genes.eRNAs may also increase their partner gene transcription rates by modulating histone modification. For instance, eRNAs are known to stimulate the histone acetyltransferase activity of CBP (CREB-binding protein) and coordinate assembly of transcription factors, such as YY1 and BRD4 (bromodomain-containing protein 4) at regulatory elements to modulate transcription . InteracCRED9 (CEBPA regulatory elncRNA downstream 9 kb) is an enhancer-associated long non-coding RNA (elncRNA), transcribed from an enhancer located 9 kb downstream from the transcriptional start site of CEBPA . CRED9 positively regulates the expression of CEBPA by maintaining H3K27ac levels at the +9 kb CEBPA enhancer. Regulation of CEBPA expression has dramatic implications for the treatment of acute myeloid leukemia and multiple other diseases .In another disease-relevant example, experience-induced eRNA ADRAM (activity-dependent lncRNA associated with memory) is expressed from an enhancer located ∼500 nucleotides upstream of the Nr4a2  TSS. ADRAM binds directly to Nr4a2 gene through a 25 nucleotide-long region complementary to exon III of ADRAM. The binding is associated with increased accumulation of H3K4me3 at the Nr4a2 promoter and ADRAM-mediated scaffolding of the chaperone protein 14-3-3 to the Nr4a2 promoter immediately after fear extinction training. This results in displacement of HDAC3 and HDAC4, followed by CBP binding at the Nr4a2 promoter and Nr4a2 expression activation. It has been shown that ADRAM is necessary for the formation of fear extinction memory which has therapeutic significance in anxiety disorders .Expression rates and activity of eRNAs are dynamically regulated by diverse mechanisms and are cell type and cell cycle-dependent. One of eRNA-targeting regulatory mechanisms is mediated by p53 tumor suppressor protein that was shown to bind multiple enhancer regions and stimulate production of eRNAs that were required for induction of a p53-dependent cell-cycle arrest .The activity of eRNAs and enhancers can also be regulated by NATs that are transcribed from the same or different loci. For example, KHPS1 (sphingosine kinase 1a antisense transcript) forms a triple-helical RNA:DNA:DNA structure at the SPHK1 enhancer. The resulting triplex recruits E2F1 and p300 and activates transcription of the eRNA-Sphk1, which evicts CTCF. CTCF is an architectural protein that insulates the enhancer from the SPHK1 promoter, therefore its removal activates transcription of SPHK1 (sphingosine kinase 1). Interestingly, when triplex-forming region of KHPS1 was replaced with a triplex-forming region from the lncRNA MEG3, that normally regulates TGFBR1 , KHPS1 associated with TGFBR1 locus thus altering the regulation of TGFBR1 expression . This meFurthermore, eRNA activity may be regulated by RNA editing . N6-methNotably, eRNA expression patterns are known to be tissue-specific. Such local eRNAs represent potential therapeutic targets downstream of glucocorticoid receptor (GR), for example, and can be used to modulate glucocorticoid response in a cell type-specific manner to reduce the possibility of side effects .Expression of eRNA is responsive to environmental stimuli. For instance, eRNAs are preferentially expressed at inflammation-related genes in response to inflammatory stimuli . Global Super enhancers are clusters of enhancers that usually regulate genes linked to cell fate specification that generate super enhancer RNAs (seRNA). Importantly, activation of seRNAs is cell and tissue-specific and may serve as a target for fine modulation of cell fate .Several methods for targeting promoter RNA or modulating eRNA expression have been proposed. For instance, a repurposed RNA-guided RNA-targeting CRISPR-Cas13 machinery was used to specifically degrade eRNAs produced by an enhancer located 30 kb upstream of colony stimulating factor 1 (CSF1) .Furthermore, saRNAs, synthetic dsRNAs containing a guide strand complementary to promoter or enhancer regions of a target gene, were shown to induce gene expression . SeveralThe activity of saRNAs may involve their recognition by dsRNA loading factors, followed by AGO2 protein binding. The passenger strand of saRNA is then discarded, and a complex consisting of guide saRNA strand, AGO2, and heterogeneous nuclear ribonucleoproteins (hnRNPs) is formed. This complex is imported into the nucleus where it binds to chromosomal DNA and participates in the RNA-induced transcriptional activation (RITA) complex. RITA interacts with RNA polymerase II to initiate transcription, leading to target gene upregulation through reduced acetylation at histones H3K9 and H3K14, increased di/trimethylation at histone H3K4, reduced dimethylation of H3K9, increased dimethylation of H3K4 and monoubiquitination of H2B at the target gene locus .MiNA Therapeutics’ saRNA-based NBT, MTL-CEBPA, is currently being evaluated in multiple clinical trials as a supplemental therapy to enhance the efficacy of standard-of-care cancer drugs in hepatocellular carcinoma . MTL-CEBA second saRNA NBT, designed to upregulate STING (stimulator of interferon response cGAMP interactor 1) aims to address immune evasion and improve the effectiveness of existing cancer immunotherapies and is now at early developmental stages at MiNa.NATs have also been shown to regulate DNA break repair which can have implications for protein expression . In a diNATs are also involved in direct regulation of translation . AlthougA newly discovered class of NATs termed SINEUPs (inverted SINEB2 sequence-mediated upregulating molecules) is encoded antisense to the 5′ end of the target sense mRNA and can enhance its translation without upregulating mRNA levels . SINEUP It has been shown that co-localization of a SINEUP and its mRNA target in the cytoplasm is essential for SINEUP activity. Sub-cellular distribution of SINEUPs and assembly of translational initiation complexes is mediated by RNA-binding proteins PTBP1 (polypyrimidine tract binding protein-1) and HNRNPK (heterogeneous nuclear ribonucleoprotein K). SINEUPs associate with PTBP1 and recruit ribosome subunits to target mRNAs which leads to formation of EF1A1 -containing translational initiation complexes .Interestingly, endogenous SINEUP Uchl1-AS  localizes mainly to the nucleus under normal physiological conditions, but translocates to the cytoplasm under rapamycin-induced stress, leading to increased allocation of Uchl1 mRNA to heavy polysomes . FurtherIn another disease-relevant example, overexpression of the endogenous SINEUP RAB11B-AS1 (ENSG00000269386) enhanced translation of its partner sense gene RAB11B (RAS-associated protein RAB11B), with only modest upregulation of transcription. Mutations leading to RAB11B haploinsufficiency are associated with intellectual disability and microcephaly. RAB11B-AS1 is downregulated in autism spectrum disorder related to CHD8 haploinsufficiency . Introduin vitro, purified and transfected into cultured cells or injected in vivo. Modified nucleotides  are introduced to increase the activity and stability of therapeutic SINEUPs. Structural studies using circular dichroism spectroscopy identified common core structures that govern the activity of miniSINEUPs with different modifications in solution that could further simplify their design and manufacturing , termed MIR-NATs, were also shown to form effector domains that regulate mRNA-ribosome pairing . MIR-NATIn a disease-relevant example, MAPT-AS1 (microtubule-associated protein tau antisense 1) is a MIR-NAT overlapping head-to-head with MAPT 5′UTR. The overlap includes domain 2 of the MAPT-IRES that binds to 40S ribosomes. By competing for ribosomal pairing with MAPT mRNA, MAPT-AS1 repressed IRES-dependent translation of the MAPT gene product, protein tau. Overexpression of MAPT-AS1 or minimal essential sequences from MAPT-AS1 (including MIR) shifted MAPT mRNA from heavy to lighter polysomes and reduced tau levels in human induced pluripotent stem cell-derived neurons. NBT mimics of MAPT-AS minimal essential sequence may be therapeutically beneficial in tauopathies, Alzheimer’s and Parkinson’s disease .PLCG1 (phospholipase-C gamma 1), a possible alternative to amyloid-beta targeting in Alzheimer’s disease (AD), can be modulated via PLCG1-AS, a MIR-NAT with an inverted MIRb repeat. PLCG1-AS overlaps the 5′UTR of the PLCG1 gene and competes with PLCG1 5′UTR for binding to ribosomes, thus reducing PLCG1 protein expression .Multiple NATs have been shown to regulate translation and transcription of gene subsets or single genes through sponging endogenous miRNAs . In somein vitro model of diabetic nephropathy and promoted inflammation and apoptosis of podocytes by sponging miR-23b-3p and increasing expression of Sema3A, its direct target  was induced in an t target . DecreasExpression of protein components of biological clock coordinates multiple cellular processes and aligns them with circadian rhythms of the whole organism. Loss of such synchronization is thought to cause circadian rhythm disturbances in numerous diseases. PER2AS and CRY1AS are NATs expressed from the loci of the core circadian clock genes (PER2 and CRY1 respectively). Their expression is oscillating in phase with their partner coding genes in a 24-h period and upregulates corresponding mRNAs by blocking their binding to cognate miRNA .Interestingly, endogenous miRNA-sponging NATs can be delivered to target cells by exosomes. For instance, exosomes derived from endometrial stromal cells were shown to deliver AFAP1-AS1 (actin filament associated protein 1-antisense RNA 1) to other cells. AFAP1-AS1 blocks BCL9 (B-cell CLL/lymphoma 9) degradation by miR-15a-5p, facilitating cell migration and invasion. This effect was reversed by inhibition of exosomal AFAP1-AS1 in nude mice which could be beneficial in the treatment of cancers .NATs can also regulate miRNA activity by other mechanisms. For example, LYPLAL1-AS1 binds to promoter of microRNA let-7b gene (MIRLET7B) and represses its transcription. miR-let-7b is upregulated and LYPLAL1-AS1 is downregulated during senescence of human adipose-derived mesenchymal stem cells. Overexpression of LYPLAL1-AS1 attenuated senescence .Using a different mechanism, interferon-α1 antisense RNA (IFN-α1AS) stabilizes its sense partner mRNA by sense-antisense duplex formation and inhibition of miR-1270-induced mRNA decay. Pulmonary-administered ASOs representing functional domains of guinea pig IFN-α1AS stabilized IFNA1 mRNA and inhibited influenza virus proliferation .As many NATs contain miRNA binding sites, synthetic anti-miRNA molecules (antimirs or RNA mimics) can be used to regulate their activity. In a disease-relevant example, a cluster of microRNA binding sites is present in the Ube3a1 NAT, including sites for miR-134. ICV injection of an antimir to miR-134 (Ant-134) reduced audiogenic seizure severity in Angelman syndrome (AS) mice carrying a maternal deletion of Ube3a (Ube3a m-/p+). Ant-134 also improved distance traveled and center crossings of AS mice in the open-field test, an approach being developed by Roche .One of the possible functions of NATs is to bind their mRNA partner, thereby modulating its half-life in a miRNA independent manner . This meIn another disease-relevant example, FAM83A-AS1 promoted lung adenocarcinoma progression by elevating FAM83A expression and activating ERK  signaling pathways. FAM83A-AS1 formed an RNA duplex with FAM83A mRNA thus increasing its stability and expression. Knockdown of FAM83A-AS1 can be achieved using NBTs .Transcript stability regulation can also be mediated by sponging protein factors that regulate degradation of mRNA. ADORA2A-AS1 (adenosine A2A receptor) bound HuR and thus prevented its binding to FSCN1 (fascin actin-bundling protein 1) transcripts, which decreased FSCN1 transcript stability and repressed AKT pathway activation. Low levels of ADORA2A-AS1 were correlated with poor prognosis in hepatocellular carcinoma .in vivo . VPS9D1-AS1, expressed from the opposite strand in the VPS9D1 (VPS9 domain-containing protein 1) locus, bound HuR protein to regulate stability and expression of the CDK4 mRNA. VPS9D1-AS1 is overexpressed in hepatocellular carcinoma and multiple other cancers. VPS9D1-AS1 silencing suppressed hepatocellular carcinoma tumor growth in vivo . VPS9D1-in vivo .Besides regulating stability of mRNAs, NATs are known to modulate protein stability . One of In another disease-relevant example, ARHGAP5-AS1 (NR_027263) interacted with SMAD7 (SMAD family member 7) through its PY motif and inhibited SMAD7 interaction with E3 ubiquitin ligases SMURF1 (SMAD-specific E3 ubiquitin protein ligase 1) and SMURF2. This led to SMAD7 protein stabilization and inhibition of breast cancer cell migration. NBT mimics of the SMAD7 interaction domain of ARHGAP5-AS1 could have therapeutic potential in breast cancer .,NATs have also been reported to modulate subcellular distribution of proteins , althougInterestingly, short ORFs present in some NATs can be translated into peptides that possess biological functions . For exaAccumulating data on the biological activity of NATs has established that each NAT can regulate protein expression through multiple mechanisms. For example, inhibition of APOA1-AS (apolipoprotein A-I antisense) expression resulted in upregulation of its sense partner gene APOA1 and of the two neighboring genes in the APO cluster by recruitment of lysine (K)-specific demethylase 1 (LSD1) and modulation of distinct histone methylation patterns . At the In another example, TRAF3IP2-AS1 (TRAF3-interacting protein 2 antisense 1) directly bound to PARP1 (poly (ADP-ribose) polymerase 1) mRNA and facilitated recruitment of the N6-methyladenosine methyltransferase complex, leading to accelerated decay of PARP1 mRNA. At the same time, TRAF3IP2-AS1, acting as a tumor suppressor, sequestered miR-200a-3p/153-3p/141-3p and prevented degradation of the tumor suppressor PTEN mediated by these miRNA .ILF3-AS1 associates with ILF3 (interleukin enhancer-binding factor 3) mRNA and inhibits its degradation via recruiting m6A RNA methyltransferase METTL3 and enhancing ILF3 mRNA interactions with the m6A reader IGF2BP1. Knockdown of ILF3-AS1 resulted in the suppression of proliferation, migration and invasion in hepatocellular carcinoma cells . FurtherMUNC lncRNA  acts as an eRNA, maintaining open chromatin structure and inducing expression of the Myod1 (myoblast determination protein 1) gene in cis, and stimulating the expression of other promyogenic genes including Myog (myogenin) and Runx1 (runt-related transcription factor 1) in trans by recruiting the cohesin complex to their promoters . MUNC caMany of the multiplex NAT functions could be independently and specifically modulated by distinct NBTs. However, in cases where different aspects of NAT functions regulate the same disease-relevant pathway it could be beneficial to design NBTs that simultaneously affect multiple functionalities. In one example of this combinatorial approach, ASOs inhibiting the activity of SMN-AS1, known to repress SMN1 transcription by recruiting PRC2 (polycomb repressive complex 2), delivered together with SMN2 splice-switching ASOs synergistically increased SMN protein expression and improved survival of a mouse model of severe SMA. This approach is investigated by Ionis . In anotin situ cleavage of linked oligonucleotides into separate active components  NAT (Dio3os) in fetal brown fat. The resulting enhanced expression of Dio3, which catabolizes triiodothyronine (T3), leads to intracellular T3 deficiency, suppression of brown adipose tissue development and obesity in the offspring. NBTs that could recruit DNA demethylases to Dio3os promoter may reverse this condition .Long range promoter-enhancer interaction can play a role in regulation of NAT expression. It has been shown that the A allele of the SNP rs2647046 located in an intergenic enhancer in the HLA (human leukocyte antigen) locus leads to increased transcription of HLA-DQB1-AS1 via facilitating CTCF-mediated long-range loop formation, enhancer-promoter interaction and transcription factor binding. The rs2647046 A allele is associated with increased risk of hepatocellular carcinoma. NBTs editing or blocking the effects of the A allele could permit a minimally disruptive intervention in this cancer .Epigenetic DNA methylation of promoters is also involved in the regulation of NAT expression. ZNF582-AS1 is downregulated in clear cell renal cell carcinoma involving DNA methylation at the CpG islands within its promoter .NAT expression can be affected by the same transcription factors that regulate expression of mRNA. For example, active vitamin D receptor (VDR) binds to consensus VDR-binding motifs in NAT genes. One of the VDR-regulated NATs is AS-HSD17b2, that is transcribed from the antisense strand of the 17-beta-hydroxysteroid dehydrogenase type 2 (HSD17b2) locus. Upregulation of AS-HSD17b2 inhibits HSD17b2 expression .NAT transcription can be activated by Ets/TCF transcription factor family members, such as GABP  and Ets1/2 . Such activation is observed in an interesting case of oncogenic mutations −124C>T and −146C>T in the bidirectional promoter that drives expression of TERT  and its partner NAT TAPAS. These mutations create novel Ets/TCF binding sites  in the promoter, which leads to Ets/TCF factor binding and upregulation of TAPAS expression .Protein products of their partner coding genes are also known to regulate NAT expression. The HIF-1α  NAT (HIFAL) recruits prolyl hydroxylase 3 (PHD3) to pyruvate kinase 2 (PKM2) protein to induce its prolyl hydroxylation. HIFAL also facilitates the transport of the PKM2/PHD3 complex into the nucleus by binding hnRNPF (heterogeneous nuclear ribonucleoprotein F), which leads to enhanced expression of HIF-1α. Moreover, HIF-1α protein induces HIFAL transcription thus forming a positive feed-back loop. High HIFAL expression is associated with an aggressive breast cancer phenotype .In a “cross-loci” feed-back example, HIF-1α bound to a hypoxia response element in the ZEB1-AS1 promoter and upregulated its expression. ZEB1-AS1, in turn, upregulated expression of ZEB1, a transcription factor that induces epithelial-mesenchymal transition and plays a crucial role in the progression of cancers. Furthermore, ZEB1-AS1 scaffolded the interaction of HIF-1α, ZEB1, and HDAC1, leading to deacetylation and stabilization of HIF-1α protein, thus forming a positive feedback loop. Knockdown of ZEB1-AS1 inhibited progression and metastasis of pancreatic cancer in nude mice .NAT stability can also be actively regulated through post-transcriptional modification and modulation of miRNA and protein binding. For example, BDNF-AS expression is increased in response to decreased levels of N6-methyladenosine on BDNF-AS. Upregulation of BDNF-AS is associated with decreased BDNF expression . While tThe critical question for the NBT field in general, and NAT-targeted therapies in particular, is whether the initial successes achieved in recent years would lead to lasting development. To a large extent this will be determined by safety, efficacy and commercial viability of the NBTs. This was demonstrated by mipomersen, that was discontinued due to liver toxicity and also pegaptanib, which was abandoned by the manufacturer for poor commercial performance .As nusinersen is the last generation NBT with the longest history of clinical use, and as the chemistry, PK and PD of ASO drugs have proven to be similar, nusinersen results could forecast the clinical experience with other ASOs and future prospects of the NBT field in general.Nusinersen (SPINRAZA®) was approved by the FDA in 2016 for the treatment of spinal muscular atrophy (SMA). As described above, nusinersen increases expression of functional SMN protein for the treatment of SMA. Nusinersen is administered intrathecally, starting with three loading doses at 2-weeks intervals, followed by a fourth loading dose 30 days later and maintenance dosing at 4-months intervals.Reports on long-term efficacy and safety of nusinersen have been encouraging. Meta-analysis of more than 20 clinical studies evaluating motor function in type 2 and 3 SMA patients treated with nusinersen revealed consistently positive outcomes . Review Good safety record of nusinersen over more than 5 years in the clinic is particularly notable because of its IT route of administration that potentially is an independent source of adverse events, especially in SMA where scoliosis and osseous fusion are frequent. In fact, most commonly reported adverse events during treatment with nusinersen were consistent with symptoms of SMA and lumbar puncture . Notablyn = 22) experienced longer median time to loss of ambulation  and attenuated rates of pulmonary decline (p < 0.0001) compared to standard-of-care external controls , golodirsen (VYONDYS 53™), viltolarsen (VILTEPSO®) and casimersen (Amondys 45) are clinically approved splice-switching antisense oligonucleotides to treat Duchenne muscular dystrophy (DMD) associated with exon 51, 53 or 45 mutations . Due to controls . In an econtrols .Clinical experience with NBTs also elucidates possible adverse events that could be common to NBT as a class. Most frequent NBT-associated adverse events are related to NBT delivery methods. Injection site reactions of different severity are observed for all current administration techniques, with more severe ones being associated with intraocular, intracerebroventricular and intrathecal routes. Furthermore, flu-like symptoms, such as fatigue, influenza-like illness, chills, pyrexia, arthralgia, myalgia, or malaise were reported. Other class AE for oligonucleotide NBTs may include liver and kidney toxicity, as ASOs are preferentially accumulated in these organs. Some of the oligonucleotide drug-treated patients developed thrombocytopenia. Golodirsen-treated patients exhibited hypersensitivity reactions, including rash and skin exfoliation .Encouragingly, the accumulation of clinical and animal testing data combined with data mining allow for the development of in-silico safety pre-screening and identification of toxicity screens that are highly predictive of patient outcomes. Importantly, with each new generation of NBTs there has been improvement in their safety and tolerability, especially in kidney and liver toxicity and flu-like symptoms .Recent discoveries in genomics and proteomics have shed light on the central role of ncRNA-based mechanisms in regulating protein expression. This has opened a vast new set of therapeutic targets that historically were inaccessible to traditional protein-targeted small molecule inhibitors. Furthermore, technological progress in NBT chemistry and biology has provided a convenient tool for modulating RNA-based mechanisms. Importantly, more streamlined design and development procedures of NBTs make them perfectly suited for the treatment of rare diseases and subsets of ‘common’ diseases associated with particular genes and for the goals of personalized medicine.As more information on NBTs in the clinic is available, it is becoming clear that, in spite of unprecedented clinical successes in treating some severe disorders, the current NBT technologies do not lead to a complete cure. Therefore, further studies of the genetic determinants and pathophysiology of diseases are essential for better identification of NBT targets and for improving treatment regimens. Combinatorial approaches, delivering mixes of NBTs or NBTs and small molecules that target different but complementary biological pathways may further improve treatment outcomes. The development of early diagnostic techniques would allow early treatment initiation crucial in genetic disorders and fast developing cancers.Furthermore, it is obvious from the clinical experience that less invasive and more convenient administration routes are necessary for the NAT-targeting NBTs to become widely adopted. Recent clinical trials with NBT administration through inhalation and work on oral, intranasal and MIND techniques is encouraging, but significant further effort is needed.Overall, in spite of setbacks in the NAT-targeting field described above, the experience of companies that are actively working to improve NBT technology has been fruitful and demonstrates progress toward better treatment outcomes.