Streptomyces have the ability to produce a wide range of secondary metabolites through a shift from primary to secondary metabolism regulated by complex networks activated after vegetative growth terminates. Despite considerable effort to understand the regulatory elements governing gene expression related to primary and secondary metabolism in Streptomyces, system-level information remains limited. In this study, we integrated four multi-omics datasets from Streptomyces griseus NBRC 13350: RNA-seq, ribosome profiling, dRNA-seq, and Term-Seq, to analyze the regulatory elements of transcription and translation of differentially expressed genes during cell growth. With the functional enrichment of gene expression in different growth phases, one sigma factor regulon and four transcription factor regulons governing differential gene transcription patterns were found. In addition, the regulatory elements of transcription termination and post-transcriptional processing at transcript 3′-end positions were elucidated, including their conserved motifs, stem-loop RNA structures, and non-terminal locations within the polycistronic operons, and the potential regulatory elements of translation initiation and elongation such as 5′-UTR length, RNA structures at ribosome-bound sites, and codon usage were investigated. This comprehensive genetic information provides a foundational genetic resource for strain engineering to enhance secondary metabolite production in Streptomyces.Bacteria belonging to  Streptomyces is a rich source of various secondary metabolites that exhibit a broad range of bioactivities, such as antimicrobial, antifungal, and anticancer  the ticancer , and 3) ticancer . Thus, tg growth .Streptomyces genomes encode approximately 60 sigma factors and hundreds of transcription factors that play critical roles in transcription initiation, thereby affecting the metabolic shift during growth methyl-2-aminoethanesulfonic acid (pH 7.2), 0.08 mg/L ZnCl2, 0.4 mg/L FeCl3∙6H2O, 0.02 mg/L CuCl2∙2H2O, 0.02 mg/L MnCl2∙4H2O, 0.02 mg/L Na2B4O7∙10H2O, and 0.02 mg/L (NH4)6Mo7O24∙4H2O) and 8 g glass beads (3 ± 0.3 mm diameter) in a 250 ml baffled flask for the pre-culture. The preculture was grown for 24 h at 30 C with agitation at 250 rpm. The mycelium was then diluted 100-fold and transferred to fresh R5-medium for the main culture.For RNA-seq and ribosome profiling, cells were sampled at exponential (E), transition (T), and stationary (S) phases. Additionally, for ribosome profiling samples, the ribosomes were stalled and cross-linked with mRNA by adding thiostrepton (Sigma) to cultures to attain a final concentration of 20 μM, as previously described . The culData processing for RNA-seq and ribosome profiling has been previously described . BrieflyLibraries for dRNA-seq and Term-seq were constructed as described previously . The conTranscription start sites (TSSs) and transcript 3′-end positions (TEPs) were determined as described previously with some modifications . The seqFor TSS determination, the peak with the maximum intensity among the peaks of a sub-cluster was selected. Peaks separated by less than 100 bp were clustered. Adjacent peaks with <10 standard deviations in their positions in the same cluster were further sub-clustered. As the 5′-end of the processed RNAs is expected to be abundantly located downstream of the TSS, peaks showing more than a 1.5-fold intensity at the position in TAP + than in TAP− conditions were selected. Finally, peaks that were not present in duplicates were removed. Assigned peaks were further manually curated according to RNA-seq profiles and peak enrichment patterns to determine the peak as the TSS.http://cholab.or.kr.For TEP determination, the enriched peak compared to adjacent peaks was selected using a machine learning algorithm with positive and negative control sets as inputs. For the positive control learning set, the peak with the maximum intensity among the peaks of a subcluster was selected to identify true-positive peaks among the peak shadows. Peaks separated by less than 100 bp were clustered. Adjacent peaks with <25 standard deviations of their positions in the same cluster were further subclustered. Low-intensity peaks with less than four counts were discarded to select enriched peaks. Peaks that were not present in either biological replicate were removed. The Z-score was an additional criterion for peak height enrichment . A high Transcription units (TUs) were determined by connecting TSS and TEP using a continuous RNA-seq profile. First, TSSs and TEPs were classified into two groups: coding and non-coding TUs. All possible combinations of TSS and TEP were tested for their association by calculating the normalized average RNA read count between 100 nt downstream region of the TSS and 100 nt upstream region of the TEP. If the distance between TSS and TEP was shorter than 200 nt, the normalized average RNA read count between TSS and TEP without the end criteria was calculated. The normalized average RNA read count of the 200 nt window region within the entire connected region was calculated and repeated for each +1 nt shifted window 200 nt upstream of the TEP. The connected region between TSS and TEP was classified as a TU when the average RNA read count of all windows was higher than 5% of the average RNA read count of the entire connected region for at least one growth phase. If the average raw RNA read count of the window was <5, it was excluded. Transcription unit clusters (TUCs) were defined as a group of connected TUs overlapping the TU region within at least 1 nt.10, and not duplicated to other functions were selectively represented. A total of 43 smBGCs were determined by antiSMASH 5.0 . GO_Biological_Process enrichment for each gene group was analyzed by a hypergeometric statistical test with Benjamini and Hochberg False Discovery Rate (FDR) correction using BiNGO . EnricheMASH 5.0 .To normalize the RNA read density, the maximum RNA read count between the -300 and +300 nt positions from TSS or TEP was normalized to 1, and RNA read counts at other positions were normalized relative to the maximum position. All RNA read counts for TSSs or TEPs at each position were then added and divided by the number of TSSs or TEPs. To determine the read-through fraction, the sum of normalized RNA counts at positions 0 to +300 nt was divided by the sum of normalized RNA read counts at positions −300 to 0 nt from the TEP. To calculate the RNA ratio of premature TUs to coding sequences (CDSs), the sum of normalized RNA read counts within each region was used.p < 0.05) (p < 0.05) among the extracted sequences from -40 to -25 nt relative position of the TSS. The obtained motif sequences were aligned relative to the middle position of the conserved motif, and the 8 nt extracted sequence was used as the input for Weblogo 3 (p < 0.01) (p < 0.05). The spacer length was calculated as the distance between the 3′-end of the −35 motif and the 5′-end of the −10 motif. Potential transcription factor binding motifs were searched by MEME (zoops) among the extracted sequences from −200 to +50 nt relative to the position of TSSs. The conserved TEP motif was searched using MEME (Zoops) among the extracted sequences from −40 to +20 nt relative to the position of TEP.The -10 motif of the promoter was searched by MEME   among theblogo 3 . For con < 0.01) , and MEMThe fold free energy of the sequence from −40 to +1 nt relative to the position of TEP was calculated using RNAfold software . The intThe ribosome pausing score of each base position within the CDS was calculated by the 3′-end position counts of RPF reads accumulated at the position shifted by 14 nt downstream from the base position, then dividing by the average 3′-end position counts of RPF reads within the CDS. The first and the las 10 codons in the CDS were excluded from the calculation to remove the effects on translation initiation and termination. In addition, only genes with more than one RPF count per nucleotide were included in the calculation to remove the high pausing score calculated using the low average count of CDS. The ribosome pausing score for each A site codon was the sum of the pausing scores at the −1, 0, and +1 positions of the P site codon. The relative frequency of the codon of the top 1,000 pausing sites was calculated by subtracting the ratio of specific codons from the total codons from the ratio of codons among the top 1,000 pausing sites.S. griseus, dynamic changes in gene expression were expected to precisely coordinate the metabolic shift from primary to secondary. To identify the differentially expressed genes at the transcription and translation levels, RNA-seq and ribosome profiling libraries were prepared from the cells collected at exponential (E), transition (T), and stationary (S) phases  E to T phase transition (ET) and 2) T to S phase transition (TS) were calculated (2 (FC)| > 1 and p < 0.05, a total of 1,086 upregulated and 568 downregulated genes and 1,312 upregulated, and 737 downregulated genes were selected from the ET and TS phase transitions at the transcription level, respectively. Using the same criteria, a total of 1,071 upregulated and 386 downregulated genes and 909 upregulated, and 669 downregulated genes were selected from the ET and TS phase transitions at the translation level, respectively. Overall, a total of 3,095 genes (44.5%) at the transcription level and 2,527 genes (36.3%) at the translation level were identified as differentially expressed genes (DEGs).During the growth of ) phases . RNA-seq) phases . RNA-seq) phases . RNA andlculated . Using tDifferentially transcribed and translated genes showed a higher proportion of upregulated genes than downregulated genes at both the ET and TS phase transitions. The genes involved in morphological differentiation and various secondary metabolic pathways contribute to this overall expression trend. A total of 43 smBGCs were determined, and the distribution of normalized RNA read counts of their genes was calculated, except for two isorenieratene BGCs at the terminal inverted repeat regions . Median 2 (FC)| > 1 and p < 0.05), and non-differential expression (N) genes include both the genes showing no significant difference in the transcription levels between the two phases, and the genes showing no significant expression. Functional enrichment of genes in each group was analyzed using the GO biological process. Overall, in the ET phase transition, the functions for cellular growth, including carbon source consumption, cell division by cell wall component recycling, and energy production, were upregulated (U). In contrast, the functions for translation, aromatic compound catabolism, and nitrogen utilization were downregulated (D). In the TS phase transition, the functions of carbon source consumption, nitrogen utilization, and transport were upregulated (U), whereas the functions in energy production, translation, and other biosynthetic processes were downregulated (D).To analyze the transcription patterns according to the gene functions, total genes were categorized into nine expression groups (groups I-IX) based on their transcription patterns, which are a combination of upregulated (U), downregulated (D), or non-differential expression (N) for ET and TS phase transitions . The criIndividual smBGCs showed different expression patterns, even though they were categorized into the same BGC types . For exastrR, streptomycin transporter genes strV and strW, and other genes with unknown functions were mostly in group IX (N-N). This result was consistent with the A-factor regulatory cascade, in which strR is first induced to activate streptomycin biosynthetic genes (afsA and bprA in groups VII (N-U) and IX (N-N), respectively. Other A-factor BGC genes also showed no change or upregulation during the T to S phase transition. In contrast, the A-factor responsive global regulator gene adpA was in group VIII (N-D); plausibly AdpA repressed its own transcription to maintain an appropriate level of AdpA to tightly regulate ordered differentiation and growth , particularly for the genes involved in streptomycin biosynthetic reactions . In contic genes . Althougd growth . In summDifferent regulatory elements can achieve differential gene expression at the transcriptional and translational levels. To elucidate the regulatory elements by determining genome-wide transcription start sites (TSSs) and transcript 3′-end positions (TEPs), dRNA-seq and Term-seq libraries were prepared from pooled samples of the three growth phases. A total of 1,887 TSSs were determined and subsequently classified into five categories based on their genomic positions relative to the adjacent genes . FurtherConsensus −35 and −10 motif searches resulted in a total of 1,887 promoters, which were 5′-TGAC-3′ for the −35 motif and 5′-ANNNT-3′ for the −10 motif, respectively . The -10As diverse sigma factors are expected to be differentially expressed in response to cellular status, their binding to specific motifs would be different during growth, resulting in differential transcription patterns of downstream genes. To identify the sigma factor binding motifs governing specific differential transcription patterns, the conserved −35 and −10 motifs and the spacer length of the promoters of the nine transcription pattern groups in To identify the binding motifs of potential transcription factors and their regulons that have a common transcription pattern, conserved motifs in the −200 to +50 nt sequences of the TSSs of the same transcription pattern group were searched (zoop parameters for MEME). Four potential transcription factor binding motifs were predicted with their regulon genes: one in group I (U-U), two in group III (U-N), and one in group VII (N-U) . The traFirst, a T-rich motif was found in group I (U-U) genes, including the WhiD transcription factor that governs sporulation  Jakimow. In addiSecond, the motif 5′-TCACCCGTnCGGCnGA-3′ was found in group III (U-N), which was almost identical to the BldM dimeric binding motif (5′-TCACcCgnncGgGTGA-3′)  Al-Bass. Indeed,Third, a CT-rich motif (5′-TTTTCnCTC-3′) was found in Group III (U-N) . Genes iStreptomyces  the transcription units (TUs) connecting the TSS and the TEP having continuous RNA reads between them, 2) the transcription unit clusters (TUCs) involving all TU isoforms that have overlapping regions among themselves, 3) non-terminal TEPs located at the middle of each TUC, and 4) terminal TEPs located at the end of each TUC were defined . A totalhopE was low in the E phase, but increased in the T and S phases. Although the upstream gene (hopD) and downstream gene (hopE) of the non-terminal TEP were both in group III (U-N), the fold change of hopD in the ET phase transition was 1.3, while that of hopE was 3.2, reflecting a sharp increase in hopE transcription. The hopE gene encodes a squalene-hopene cyclase, which is the key enzyme in hopanoid biosynthesis that cyclizes squalene to have ring structures, forming hopene  and dimethylallyl diphosphate (DMAPP), whereas downstream genes (SGR_RS04655 and SGR_RS04650) from hopE seemed to be involved in the additional modification of adenosylhopene formation. Thus, hopE and downstream genes govern more hopene-specific reactions, whereas hopA, hopB, hopC, and hopD govern more general terpene biosynthetic reactions. Therefore, the non-terminal TEP between them was predicted to be a potential regulatory element of transcription termination or post-transcriptional processing for the efficient regulation of hopanoid BGC genes during growth.A total of 637 non-terminal TEPs without any downstream TSSs within TUC were selected to investigate the sole effect of non-terminal TEPs on the transcript abundance of the downstream gene. The non-terminal TEP of the structured non-motif group in the hopanoid BGC is shown as an example . The RNAg hopene . UpstreaIn addition to the non-terminal TEP, 79 premature TUs could have regulatory elements affecting the differential transcription of the downstream gene. The RNA read count ratio of the premature TUs to the CDS indicated premature termination intensity, and their average and standard deviation at the three growth phases were diverse . Among tS. coelicolor and S. clavuligerus; the increase in the translation level of the secondary metabolism genes from early to later growth phases was not sufficient compared to the increase in their transcription levels  > 4 (2 (TE_FC) > 1) and RNA decrease (log2 (RNA_FC) < 1) during the ET phase transition (ET_RNA-down_TE-high group) have functional enrichment in the regulation of cellular biosynthetic processes. These regulator genes showed translational induction specifically at the T phase, which was previously observed for four clustered-situated regulators of representative antibiotics in S. coelicolor (2 (TE_FC) > 0] with both RNA and RPF decrease [log2 (RNA_FC) < 1 and log2 (RPF_FC) < 1] in the TS phase transition (TS_RNA-down_RPF-down_TE-up group) have functional enrichment in translation and cellular biosynthetic processes. This may be because these functions should be tightly regulated during translation to maintain a certain level, even if their transcription was less. Many ribosomal protein genes are involved in this group and are regulated through translational feedback by r-proteins  > 4 . For exaelicolor . This reproteins .We speculated that differential TE changes for different genes in different growth phases would be affected by the regulation of translation initiation levels. Thus, the 5′-UTR length distribution, which contains translation initiation regulatory elements, including the ribosome binding site, was investigated . AccordiManual investigation of RPF profiles of the regulator genes in the ET_RNA-down_TE-high group  revealedFurther, the relative frequency of codons and amino acids was investigated for the top 1,000 pausing score sites compared to the total number of sites in each growth phase. The GAC, CTG, and CCG codons showed the highest frequency for the top 1,000 pausing sites encoding Asp, Leu, and Pro, respectively . Indeed,Ribosome pausing could also occur by mRNA stem-loop structures inhibiting translocation or hindering A-site tRNA binding . To inveafsR  suggests that this A-factor cascade had already started before the E phase (afsA transcription in the later growth phase was observed in this study, which was also reported in a previous study (In a previous study, the metabolic shift by the cascade from A-factor was predicted to occur rapidly, and the addition of the critical concentration of A-factor at the 12 h growth phase in YMPD medium activated the AdpA regulon genes within 30 min . However E phase . In addius study , but thein vitro binding assays would validate the predicted regulons to understand the complex transcriptional regulatory network.Next, to elucidate the regulatory elements in the promoter affecting differential transcription patterns at the transcription initiation level, the binding motifs of sigma factors and transcription factors were searched. As a result, the conserved binding motifs of sigma and transcription factors differed in each differential transcription pattern group. In addition, the binding motifs and regulons of one potential sigma factor and four potential TFs were elucidated. Further, with RNA-seq and dRNA-seq under various conditions, potential binding motifs of other sigma factors and transcription factors could be efficiently predicted. Additional ChIP-Seq and S. lividans (S. clavuligerus (By determining genome-wide TEPs by Term-seq, the potential regulatory elements of transcription termination and post-transcriptional processing at the 3′-end of the transcripts were investigated. Motif TEPs show features of intrinsic terminators, while Structured non-motif TEPs are likely I-shaped intrinsic terminators of the GC-rich eubacterial genome , or enrilividans  and S. culigerus .hopE gene and its downstream modification genes showed differential transcription patterns in the ET phase transition that were considered to be regulated by non-terminal TEP. The biological functions of hopanoids include the control of cell membrane fluidity and integrity; however, many structural variants of hopanoids have also been reported to have other functions, including low pH tolerance, antimicrobial resistance, alignment of extracellular cellulose microfibrils, and plant–bacteria interactions (In addition, TUs and TUCs were determined from TSSs and TEPs to provide genome-wide information on the TU isoforms during growth. Moreover, transcript abundance within TUC was differentially changed in non-terminal TEPs according to the features of the non-terminal TEP. For example, the squalene cyclase ractions . Furtherractions . Althougractions , no cobaE. coli rpoS gene, which is the regulatory element playing a positive role in its expression (The translation level regulation in genes with differential transcript patterns was analyzed in terms of the TE fold change during growth. For translation initiation, 5′-UTR length did not show a significant correlation with the TE fold change during growth. However, specific functional enrichments of lmRNA and emRNA suggested that lmRNAs may play important roles in other conditions, such as stress conditions, rather than different growth phases . Translapression . In addiS. griseus growth was investigated in terms of transcriptome and translatome. A pipeline for systematic analysis of differentially expressed genes during growth was provided. The potential regulatory elements found in this study may facilitate this rational engineering design for enhancing secondary metabolite production.In conclusion, the metabolic shift during


Human T-cell leukemia virus type 1 (HTLV-1) is a retrovirus that causes an aggressive T-cell malignancy and a variety of inflammatory conditions. The integrated provirus includes a single binding site for the epigenomic insulator, CCCTC-binding protein (CTCF), but its function remains unclear. In the current study, a mutant virus was examined that eliminates the CTCF-binding site. The mutation did not disrupt the kinetics and levels of virus gene expression, or establishment of or reactivation from latency. However, the mutation disrupted the epigenetic barrier function, resulting in enhanced DNA CpG methylation downstream of the CTCF binding site on both strands of the integrated provirus and H3K4Me3, H3K36Me3, and H3K27Me3 chromatin modifications both up- and downstream of the site. A majority of clonal cell lines infected with wild type HTLV-1 exhibited increased plus strand gene expression with CTCF knockdown, while expression in mutant HTLV-1 clonal lines was unaffected. These findings indicate that CTCF binding regulates HTLV-1 gene expression, DNA and histone methylation in an integration site dependent fashion. Human T-cell leukemia virus type 1 (HTLV-1) is a cause of leukemia and lymphoma as well as several inflammatory medical disorders. The virus integrates in the host cell DNA, and it has a single binding site for a protein designated CTCF. This protein is important in the regulation of many DNA viruses, as well as many properties of normal and malignant cells. In order to define the role of CTCF binding to HTLV, we analyzed a mutant virus lacking the binding site. We found that this mutation variably affected gene expression, DNA and histone modification, suggesting a key role in regulation of virus replication in infected cells. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Bryan R. CullenAssociate EditorPLOS PathogensRichard KoupSection EditorPLOS PathogensKasturi HaldarEditor-in-ChiefPLOS Pathogensorcid.org/0000-0001-5065-158XMichael MalimEditor-in-ChiefPLOS Pathogensorcid.org/0000-0002-7699-2064***********************Reviewer Comments :Reviewer's Responses to QuestionsPart I - SummaryPlease use this section to discuss strengths/weaknesses of study, novelty/significance, general execution and scholarship.Reviewer #1: In this manuscript, Cheng et al. investigate potential functions of the CTCF binding site in the HTLV-1 genome. The authors generated a CTCF mutant proviral clone that disrupts CTCF binding, which was then used to infect PBMCs and JET cells. Since the CTCF mutations also result in p12 mutations, the authors introduced a stop codon in p12 upstream of the CTCF mutations. A proviral clone with only the p12 deletion was used as a control. The authors found increased DNA methylation in the genomes of delta CTCF viruses in both PBMCs and JET cells, mainly downstream of the CTCF binding site. There was also an increase in both activating (H3K4me3) and repressive (H3K27me3) histone marks in delta CTCF viruses in PBMCs and JET cells. Finally, mutation of the CTCF binding site did not impact proviral integration site preferences, but did influence proviral gene expression. Knockdown of CTCF with shRNA enhanced viral gene expression in JET cells infected with wild type HTLV-1, but not delta CTCF viruses.Overall, the results suggest that the CTCF binding site serves as an epigenetic barrier to limit the spread of methylation to the 3’ LTR and thus protect HBZ from inactivation, however it is difficult to understand why there were no noted changes in HBZ expression associated with loss of the CTCF binding site. Furthermore, the results from this study are directly opposed to those from a recent study , and thus additional experiments are warranted to rigorously substantiate the conclusions. The presented data convincingly implicate CTCF in the suppression of plus strand HTLV-1 gene expression; however, it remains unclear if the underlying mechanism is due to epigenetic changes (i.e. DNA and histone methylation) in the HTLV-1 genome caused by loss of the CTCF binding site.Reviewer #2: Recent work demonstrated the existence of a CTCF binding site in the HTLV-1 provirus . This is a nice paper reporting the effects on DNA methylation and expression of HTLV-1 proviral DNAs, upon mutating the CTCF binding site. The usual switch from highly methylated upstream (in the “sense” direction), to poorly methylated downstream, is mostly lost in the mutant. This is a very intriguing result (predicted by early studies and here confirmed to be true). Histone marks were also affected.All the work here is done with cell lines infected in vitro, so we are looking at integration sites established by recent infection. The work was done in pooled JET cells and immortalized clones of PBMC cells.The authors next looked effects of KD of CTCF on transcription, using infected JET clones that are initially silent, and then activated with PMA and ionomycin, plus/minus KD of CTCF. The KD gave increased viral gene expression in a subset of the clones .There was no effect of the mutation on the frequency of establishment of latently infected clones or the reactivation from latency. Similar results to this were recently reported by some of the authors . There were no changes in integration site preferences.Somewhat conflicting results on the effects of CTCF BS mutation on viral chromatin were recently reported  in a different setting.This is a generally nice story on an interesting site in the HTLV-1 genome. There’s a lot of work presented, and the findings are generally convincing and properly interpreted. There are limitations to the settings  that are not ideal but unavoidable, and just should be admitted firmly somewhere in discussion.Reviewer #3: In this manuscript, Cheng and Joseph et al. generated and examined an HTLV-1 mutant virus that eliminates the binding site for the epigenomic insulator CCCTC-binding protein (CTCF) located in the pX region of the viral genome. Using a variety of state-of-the-art techniques and clonally infected T-cells, the authors clearly show that mutation of the CTCF-binding site (CTCF-BS) does not disrupt viral gene expression. Moreover, establishment of latency and reactivation from latency do not seem to depend on the CTCF-BS. However, DNA deep sequencing of bisulfite treated DNA revealed that the viral genome is heavily methylated upstream of the CTCF-BS, while it is only weakly methylated downstream of the CTCF-BS. Interestingly, disruption of the CTCF-BS leads to a dramatically enhanced methylation in the region downstream of the CTCF-BS. ChIP sequencing data underline the insulating role of the viral CTCF-BS since disruption of CTCF-BS results in differentially increased histone methylation of the HTLV-1 provirus. Using next generation sequencing, the authors show that disruption of the CTCF-B does not impact proviral integration site preferences of HTLV-1. However, analysis of a series of clonally infected cells by qPCR and live cell imaging revealed that inhibition of CTCF binding affects viral gene expression in ca. 2/3 of all investigated clones, leading to increased viral gene expression. Finally, the authors show that cells infected with delta-CTCF-BS virus do not differ in establishment of viral latency nor do they differ in viral reactivation from latency, suggesting that control of latency may be independent of CTCF-binding.This is a very interesting manuscript which clearly elucidates the relevance of the CTCF-BS in HTLV-1 proviral DNA using a broad panel of up-to-date methods. The manuscript is well-written and the experiments are clearly described. The authors comprehensively analyzed the newly established proviral clones and the respective controls. The experimental results strongly support the conclusions of the authors. Due to the importance of the data showing that mutation of the CTCF-BS does not impact latency establishment and reactivation , these data should be shown in the main part of the manuscript.**********Part II – Major Issues: Key Experiments Required for Acceptanceabsolutely required to validate study conclusions.Please use this section to detail the key new experiments or modifications of existing experiments that should be Generally, there should be no more than 3 such required experiments or major modifications for a "Major Revision" recommendation. If more than 3 experiments are necessary to validate the study conclusions, then you are encouraged to recommend "Reject".Reviewer #1: 1) The authors need to characterize the HTLV-1 immortalized PBMCs used in the study. Are these cells CD4+, IL-2 dependent, etc.? What is the baseline expression of Tax, HBZ and Gag mRNA and protein in PBMCs immortalized with WT, p12Stop and delta CTCF viruses? Based on the authors’ model, differences in viral gene expression should be apparent in the absence of a functional CTCF binding site in the viral genome.2) It is unclear if the methylation studies presented in Figs. 2 and 3 represent single cell clones or the bulk population of PBMCs and JET cells. This needs to be clarified by the authors. If these experiments were performed with the bulk population of cells, they should be repeated with single cell clones with defined viral integration sites. Why is the overall histone methylation so low in JET cells ?3) The data in Fig. 5 suggest that CTCF binding to the proviral genome suppresses Tax expression. However, what is the molecular basis of the increased plus strand viral gene expression in these particular JET cell clones? Is it due to epigenetic changes? Is there increased H3K4me3 marks at the 5’ LTR? Do viral integration sites play a role? Why is HBZ expression unaltered in the different clones?4) PMA/Ionomycin is not a physiologically relevant approach to induce viral gene expression . Crosslinking of anti-CD3 and anti-CD28 with agonistic antibodies more accurately mimics T cell activation. Recent studies  have identified stress stimuli as transcriptional activators of the HTLV-1 plus strand. Do stress stimuli (e.g. hypoxia or oxidative stress) also reactivate HTLV-1 gene expression in the latent JET cell clones?Reviewer #2: Some issues:(ll 226 ff.):It would have been nice to have looked at one more silencing mark, which is H3K9me3.ll 345 ff: The discussion of the border phenomenon is important, but should really start out with an explanation of the fact that we do not yet know the point of origin of the methylation area, and that this would be important to identify. Then the discussion of where it might be is more clear. (Also we need some statement of the premise that it can spread.) I don’t think the data really rule out the idea that the 5’LTR might be the source of the methylation domain, though Taniguchi may be right.ll 244: While the DNA methylation looks like a border issue, the histone modification does not. The summary statement that “loss of the CTCF binding site in HTLV-1 results in increased histone methylation 5’ and 3’ of the CTCF site, suggesting that CTCF binding may act as a boundary element to epigenetic modification” does not quite make sense. This does not seem like a boundary element. Since the effect is the same on both sides, it just seems like a site working in both directions.It would be nice to have more discussion of the magnitude of the various effects. For example, Fig 2 is impressive. But Fig 5 (expression) is showing generally more modest effects . No point hiding this.The results of the Bangham paper probably need some more discussion, including how the setting is different than here.Reviewer #3: No additonal experiments required, but Fig S10 should be presented in the main manuscript and not in the supplement.**********Part III – Minor Issues: Editorial and Data Presentation ModificationsPlease use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity.Reviewer #1: 1) JET cells should be defined when first introduced in the Results section. It is not common knowledge that these cells express tdTomato controlled by Tax responsive elements.Reviewer #2: Small points:The reportage of the changes in CpG methylation up- and down-stream of the CTCF BS in the text is someone long-winded and unnecessarily detailed. The numbers are appropriately in the table, and a more general description would be better. Summary (ll. 180 area) is good.And the format of the fold changes given as “2-31 fold” is somewhat confusing – maybe it needs to be written as “in a range of 2-fold to 31-fold” or something similar. But we don’t really need all the numbers in the text.(ll. 192 ff) Scoring the 5’LTR vs. 3’LTR for methylation needs explanation – if the assay is by PCR after bisulfite, we need to know how the reads are assigned to either one or the other LTR. (Maybe the reads are long enough to discriminate?)(ll 199 ff) We need a note as to how strand-specific methylation is read out – presumably seeing C-to-T changes on the appropriate strand (i.e. CpG to TpG as opposed to seeing CpG to CpA change).(ll 240 ff) It needs to be stated here that (very curiously) both repressive and activating histone marks are increased In the mutant. This is hard to interpret, but it should be laid out here.Reviewer #3: Line 127: Electrophoretic mobility shift data is not shown. Either show it in the supplement or do not mention it.Line 174: please cite studies that „commonly used“ JET cells.Line 238: typo, HTLV-1A should read HTLV-1Line 254: please provide a reference for the Gini index.Figures:All Figures should be formatted according to the journal guidelines. Some figures are too wide.Fig.2: The left and the right part of the Figure should be adjusted to each other, or the Figure should be arranged in upright format. It would be helpful for the reader if the authors create subpanels, e.g. A) percentage of methylation, and B) change of methylation.Fig.4A: Why did the authors not analyze HTLV-1 wildtype? Since integration sites of HTLV-1-Delta-CTCF were analyzed, the legend should also read „Delta-CTCF“ instead of CTCF.Fig. 4B: It is not clear why the authors show the inset. Labeling of the axes is lacking in the inset. The authors state FOXP3 in the legend, but not in the text.Fig S3: The labeling of the luciferase assay and the western blot should be the same , otherwise, it is confusing for the reader.Fig. S4: Copy numbers of HBZ are very low (0.008 per million GAPDH). Could the authors please comment if these low copy numbers are typical for HBZ expression analysis of proviral clones?Fig. S5: The labeling of subpanels in the legend is mixed up. It should read „in (A) 5’LTR, (D) 3’LTR and HBZ“ and „in (B) PBMCs and (C) JET“; Typo: THLV-1; * and ** should also be explained in the legendFig. S7: Why is #15 shown twice? Line 549: Typo „).“**********what does this mean?). 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ACT1 intron into different positions within IMD4 and INO1 genes. Transcription Run-On (TRO) analysis revealed that the transcription of both IMD4 and INO1 was maximal in constructs with a promoter-proximal intron and decreased with the increase in distance of the intron from the promoter. However, activation was partially restored when the intron was placed close to the terminator. We previously demonstrated that the promoter-proximal intron stimulates transcription by affecting promoter directionality through gene looping-mediated recruitment of termination factors in the vicinity of the promoter region. Here we show that the terminator-proximal intron also enhances promoter directionality and results in compact gene architecture with the promoter and terminator regions in close physical proximity. Furthermore, we show that both the promoter and terminator-proximal introns facilitate assembly or stabilization of the preinitiation complex (PIC) on the promoter. On the basis of these findings, we propose that proximity to both the promoter and the terminator regions affects the transcription regulatory potential of an intron, and the terminator-proximal intron enhances transcription by affecting both the assembly of preinitiation complex and promoter directionality.An evolutionarily conserved feature of introns is their ability to enhance expression of genes that harbor them. Introns have been shown to regulate gene expression at the transcription and post-transcription level. The general perception is that a promoter-proximal intron is most efficient in enhancing gene expression and the effect diminishes with the increase in distance from the promoter. Here we show that the intron regains its positive influence on gene expression when in proximity to the terminator. We inserted  Candida glabrata to 98% in capsular basidiomycete yeast Cryptococcus neoformans  to appropriate volume and grown to AIMD4 gene were Alu1 and Dra1. Each experiment was performed with at least four independently grown cultures. The P1T1 PCR signals obtained using P1 and T1 primers flanking the promoter and terminator regions respectively were normalized with respect to F1-R1 PCR signal that amplifies an uncut region within the gene as shown in 3C experiments were performed as described previously in . The priChIP experiments  were performed as described previously . DiffereTranscription analysis was performed by RT-PCR approach as described previously . The priThe strand-specific “Transcription Run-On” (TRO) assay was performed as described in . The prip-values were calculated by two-tailed student t-test , a second one at 750 bp position within the coding region (middle), and a third one in 3′ UTR at the position 20 bp downstream of the coding region  as shown in INO1, we have previously demonstrated that ACT1 intron enhances transcription of IMD4 in a splicing-dependent manner as mutation of either 5′ or 3′ splice sites completely abolished transcription enhancement potential of the intron    on the promoter . WhetherIMD4 as shown in p-values . Thus, unlike the promoter-proximal intron, the middle intron does not have a significant effect on the promoter occupancy of the general transcription factors. These results are in accordance with the effect of middle intron on transcription and directionality.Since we have shown that the position of an intron in a gene affects promoter directionality, we next examined if the position of the intron also affects its ability to recruit or stabilize binding of PIC components on the promoter region. We therefore checked occupancy of TFIIB and TFIIH subunit Ccl1 in the presence of the intron in the middle region and the terminator-proximal region of p-value 0.005)   assay as described in A comparison of the active RNAPII molecules present on naturally intron-less and intron-containing genes was done by nuclear run on, enabling us to gauge the direct effect of introns on transcription. RNAPII density was almost doubled on intron containing genes and the transcription rate of intron-containing genes was 2.5 times more than that of intron-less genes . This inINO1 and IMD4 in a splicing-dependent manner (INO1 or IMD4. The strains with the mutated intron near the terminator region were not viable. Instead we show here that the terminator-proximal intron of both INO1 or IMD4 is spliced efficiently (Studies with cultured mammalian cells have found that the 3′ splice-site of the terminator-proximal intron had an adverse effect on trimethylation of H3K36, a chromatin mark normally associated with transcription elongation that must be removed to facilitate termination . A mutatt manner , we thouiciently .In budding yeast, most of the intronic genes contain a single intron located in the vicinity of the promoter. The contribution of the position of an intron, especially the terminator-linked intron, on gene expression therefore remained largely unexplored. This is the first systematic study analyzing the role of the position of an intron within a gene on its transcription in budding yeast. Our results reaffirm the view emerging from studies with higher eukaryotes that the position of an intron within a gene has a strong bearing on its transcription. Unlike in higher eukaryotes, however, we found that the terminator-linked intron in yeast affects promoter transcription in a manner similar to the promoter-proximal intron. The terminator intron inhibits uaRNA transcription in the vicinity of the promoter region, thereby enhancing promoter directionality. The terminator intron also affects the occupancy of TFIIB on the promoter in a manner similar to a promoter-proximal intron. The occupancy of TFIIH, a general transcription factor that is recruited last during assembly of PIC, however, is not significantly enhanced by the terminal intron. This may explain why a terminator-linked intron is not as efficient in enhancing transcription as the promoter-proximal intron. Unlike in budding yeast, higher eukaryotes contain multiple introns distributed all over the genes. This study will serve as a paradigm for investigating the role of terminator-proximal introns on gene expression in higher eukaryotes.


Conserved ATP-dependent chromatin remodelers establish and maintain genome-wide chromatin architectures of regulatory DNA during cellular lifespan, but the temporal interactions between remodelers and chromatin targets have been obscure. We performed live-cell single-molecule tracking for RSC, SWI/SNF, CHD1, ISW1, ISW2, and INO80 remodeling complexes in budding yeast and detected hyperkinetic behaviors for chromatin-bound molecules that frequently transition to the free state for all complexes. Chromatin-bound remodelers display notably higher diffusion than nucleosomal histones, and strikingly fast dissociation kinetics with 4–7 s mean residence times. These enhanced dynamics require ATP binding or hydrolysis by the catalytic ATPase, uncovering an additional function to its established role in nucleosome remodeling. Kinetic simulations show that multiple remodelers can repeatedly occupy the same promoter region on a timescale of minutes, implicating an unending ‘tug-of-war’ that controls a temporally shifting window of accessibility for the transcription initiation machinery. Saccharomyces cerevisiae, gene promoters contain nucleosome-depleted regions (NDRs), approximately 150 base-pair stretches of DNA that are depleted of nucleosomes  to directly observe and characterize the chromatin-binding kinetics of ATP-dependent chromatin remodelers in living cells . We inveWe tagged the catalytic subunits of six major chromatin remodeling complexes, RSC, SWI/SNF, CHD1, ISW1 , ISW2, and INO80 at the C-terminus with the self-labeling HaloTag by engineering the endogenous loci and expressed the fusion proteins as the sole source under natural promoter control. The fusion proteins were localized in the nucleus and did not display detectable cleavage of the tag by SDS-PAGE . FurtherIn order to quantify a broad range of kinetic behaviors displayed by remodelers, two imaging regimes were applied. ‘Fast-tracking’ acquires 10 ms frame-rate movies to directly measure a range of single-molecule diffusivities from ‘slow’ (chromatin-bound) to ‘fast’ (chromatin-free) and determine fractional representation . However2s−1 (2s−1)  based on their mean squared displacements (MSD), and present frequency histograms based on the log(D) values of each trajectory. The histograms were initially fit to two Gaussian distributions, approximating slow and fast populations . For mor2s−1  consiste1 (2s−1) .bound fraction (average 0.036 ± 0.007 to 0.067 ± 0.004 µm2s−1) as would be expected for molecules associated with largely immobile chromatin  are ~10-fold higher, but distinctly lower than the Dfree for Halo-NLS, indicating that our imaging regime captures essentially the full range of potential diffusive behaviors for this family. In addition, the Dfree values show an inverse correlation with the estimated total molecular weights of chromatin remodeling complexes, consistent with expectations that the tagged catalytic subunits are associated within larger complexes   complex  (CHD1 and ISW1 act primarily on nucleosomes located in the gene body . The two ± 1.1%) . However ± 1.1%) , in addi ± 1.1%) . The ISW ± 1.1%) . Since I ± 0.0%) .Unlike the two well-separated Gaussian distributions for H2B histones and HaloTag protein , the LogTo distinguish between these possibilities, we analyzed single-particle trajectories using vbSPT, a variational Bayesian Hidden Markov Model (HMM) algorithm, which models state kinetics and assigns diffusive states to each displacement . We clasIt is striking that the population of transitioning trajectories is more prominent for remodelers (from 20.9 ± 2.5% to 30.2 ± 1.5%) compared to free HaloTag (10.4 ± 0.7%) and H2B histone (7.1 ± 2.1%), . We obsesb, τtb) and fractions  of stable-binding and transient-binding species were extracted (The chromatin-bound remodeler population measured by fast tracking consists of both transiently and stably bound molecules. We acquired long-exposure movies [250 ms/frame] under slow tracking  to generxtracted . All τ vsb) of RSC (27 ± 2%) and SWI/SNF (24 ± 6%) display strikingly short lifetimes   and similarly short residence times  (The stable-binding subpopulations (f± 1.2 s) , consist± 1.2 s) . Similarctively) . Hence, ctively) .sb 15 ± 3% and 19 ± 2%, respectively) and short dwell times   with comparable stable-binding fractions (tb< 0.65 s). Hence, the majority of chromatin binding events by remodelers is transient, and stable binding, on the order of several seconds, is notably short-lived.For gene body-acting remodelers, CHD1 and ISW1 complexes exhibit stable-binding fractions (fctively) . Interesractions . These rTo examine whether the measured dissociation kinetics are intrinsic to chromatin remodeling complexes or functionally related to their ATP-dependent remodeling activities, we made strains harboring a point mutation in the ATPase domains of Isw2, Isw1, and Chd1; these mutations have previously been shown to abolish their ATPase activities  . We thenWe found that the stable-binding average residence time increased by ~2-fold (from 4.9 ± 2.2 to 9.7 ± 3.1 s) for the Isw2K215R mutant . Similarc [radius of confinement] were calculated to characterize its diffusivity and the confined domain encompassing the observed trajectory, respectively , but not for ATP binding  formation and TSS fidelity . RSC and-binding . Here, w-binding .molecules) and number of the target sites in the genome (Ntargets) , many studies have investigated the genome-wide specificities of chromatin remodelers using multiple approaches including ChIP-Seq , SWI/SNF binds only a small subset (Ntargets = 466), and INO80 (Ntargets = 1646) and ISW2 (Ntargets = 1802) each bind to approximately a third of all yeast promoters  . We relyrevisiae , with thChIP-Seq , MNase-CChIP-Seq , ChIP-exChIP-Seq , Native-ChIP-Seq , CUT andChIP-Seq , and ChEChIP-Seq . For thiromoters . Becauseromoters , the totsearch (search time), the time it takes for a molecule to go from one stable target site to the next [i.e. time bound non-specifically plus time in free diffusion], the SI  [i.e. the time between initial binding of one molecule and binding of the second molecule], and the estimated values for Nmolecules and Ntargets per cell (sb) of 5.0 ± 0.7 s. Thus, rather than individual RSC molecules residing for long periods of time, the high occupancy rate can be attributed to the short τsearch (15 ± 2.6 s) and comparable SI (17 ± 3.3 s) values coupled to high Nmolecules (>4000)  . SWI/SNF (>4000) . Strikin (>4000) .search (23 ± 6.0 s) and SI (15 ± 4.7 s) values, while ISW2 has a relatively longer τsearch (61 ± 30 s) and SI (44 ± 21 s) values  at its targets while ISW2 displays a lower occupancy (11 ± 7.2%). INO80 exhibits short τ) values . Average) values  show tha) values , the occ) values .    , and wit 73–87%) . The hig 73–87%) , By slow 73–87%) . Compara 73–87%) , all ima 73–87%)  results ± 0.7 s) . Unlike ± 0.7 s) . Indeed,Importantly, the fast dissociation rates of remodelers are facilitated by ATP hydrolysis. Five tested ATPase-dead mutants  show twofold or greater increase in their stable-binding residence times , highligDrosophila ISWI remodelers undergo ATP hydrolysis-dependent translocation on ssDNA and dsDNA in vitro  and varying sampling intervals (5–44 s) provides a dynamic temporal window of accessibility for promoter chromatin.In a related study , the aveDrosophila Brahma (BRM) remodeler also displays dependence on ATP utilization by the catalytic ATPase for dynamic mobility and remodeler dissociation in live Drosophila cells. For INO80 and ISW2, it would be of interest to distinguish their diffusive behaviors specific to DNA replication in cells undergoing S phase, and to DNA repair in the presence of DNA damaging agents. Outstanding questions also include determining the kinetic parameters for other chromatin regulators such as histone acetyltransferases, methyltransferases, histone de-modification enzymes, and histone chaperones, to gain a comprehensive view of the overall competition for engagement of promoter-proximal nucleosomes, their effects on nucleosome positioning and the dynamics of transcription complexes. Our findings in live cells provide a temporal framework for further testing of proposed models and should facilitate development of in vitro single-molecule assays that allow direct observation of physical and functional interactions between transcription regulators, chromatin, and the transcription machinery.Taken together, our SMT study elucidates the dynamic behaviors of this family of nuclear proteins and offers insights into additional kinetic functions for the remodeling ATPase and the timescales that govern nucleosome repositioning in relation to transcription events. It is notable that an independent study from the Verrijzer laboratory  found that the Saccharomyces cerevisiae strains used in this study are isogenic derivatives of W303 strain carrying pdr5△ for efficient JF ligand labeling, and are listed in All 4) were serially diluted (5-fold dilutions) on YPAD plates. Plates were imaged after 48 or 72 hr growing at three different temperatures .The cell growth of strains carrying HaloTag fusion constructs were compared to their derived parental genotype. The strains grown to saturation in YPAD (Yeast Extract-Peptone-Dextrose + 40 mg/L Ade-SOYeast cultures growing at early log phase (OD600 0.2) were treated with JF646 dye at a saturating dye concentration of 20 nM JF646  was usedYeast cultures growing in Synthetic Complete Medium  dextrose; 40 mg/L Adenine hemisulfate) were treated with dyes at early log phase (D600 0.2–0.3) for 2 hr. For fast-tracking, saturating dye concentrations ranging from 10 to 20 nM JF552  were useAll yeast imagings were performed using a custom-built Zeiss widefield microscope (Axio Observer Z1) with a 150X glycerin immersion objective (NA 1.35) as previously described . Data waAfter yeast immobilization, the asynchronous cells representing all phases of the cell cycle were imaged for around 2 hr at room temperature.2. A field of view of 128 x 128 pixels was used to capture 4–6 yeast nuclei. Single-molecule imaging was performed using dSTORM  (Movies with 10 ms exposure/frame were recorded with continuous 555 nm laser irradiation at ~1 kW/cmroscopy) . Each moroscopy) .This conUnder fast-tracking conditions, cells displayed no detectable cellular damage and underwent normal bud growth and cell division when examined every 30 min up to 3 hr . Further2 (5% of fast-tracking power) for sufficient signal-to-noise while minimizing photobleaching. A focal plane of 256 x 256 pixels was used to capture 15–20 yeast nuclei. In the beginning of each movie, the 639 nm excitation channel was briefly used to fine-tune the focus, and then immediately switched to 555 nm excitation to start data acquisition. Upon 555 nm laser illumination, we record ~5 min movies starting with fluorescent JF552 pushed to the dark state for > 30 s, followed by single-molecule imaging of at least 750 frames at 250 ms/frame. Fifteen to twenty movies were taken per imaging session, with two or three biological replicates for wildtype and two to four biological replicates for mutant strains.Movies with 250 ms exposure/frame were acquired using continuous 555 nm laser irradiation at 0.05 kW/cmhttp://www.diatrack.org/index.html) to localize the centroid of PSF (point spread function) by Gaussian fitting over the fluorescence intensity to sub-pixel resolution  and 750 frames (3.125 min) were selected for fast and slow tracking movies, respectively, using ImageJ (1.52 p) custom-written script. The substacks were then applied to the Diatrack software . From the slope, the diffusion coefficient was calculated as :MSD-based diffusion coefficient histograms: All ‘masked’ trajectories with at least five displacements were analyzed, using the lab custom-written R package, Sojourner (bound (µm2/s): 0.0005–0.1, Dfree (µm2/s): 0.15–25, Fbound: 0–1, Localization error (µm): Fit from data (0.01–0.1), dZ (µm): 0.6, Use Z correction, Model Fit: CDF, Iterations: 3.Spot-On : All ‘mahttps://gitlab.com/anders.sejr.hansen/anisotropy): timestep = 0.01; dim = 2; trjLmin = 2; runs = 3; maxHidden = 2; stateEstimate = 1; bootstrapNum=10; fullBootstrap = 0; init_D = ; init_tD = *timestep; and default prior choices according to vbSPT  HMM : All ‘maThen each trajectory was sub-classified as ‘Bound only’ if all displacements are classified as bound state; ‘Free only’ if all displacements are classified as free state; and ‘Transitioning’ if the trajectory contains both bound and free displacements with at least two consecutive displacements in each state. To validate that the transitioning trajectories consist of bound and free states, we calculated and compared the displacement length between ‘bound only’ and bound segments of transitioning trajectories, and between ‘free only’ and free segments of transitioning trajectories. Finally, the sub-classified trajectories were used to regenerate the diffusion coefficient histograms.2 ≥ 0.8 filter, we used all trajectories whose MSD plots passed the more lenient R2 ≥ 0.1 filtering. The MSD plot was then fit to the circular confined diffusion model:−5 and Rc higher than 300 nm were discarded.Radius of confinement: All ‘masked’ trajectories with at least four displacements were analyzed, as described previously . To detesb and ktb correspond to dissociation rates for stable- and transient-binding events, respectively, and Residence times: Using Sojourner package, the apparent lifetimes  were determined for all ‘masked’ trajectories lasting at least three frames. To account for blinking or mislocalizations, we allowed for gaps up to two frames between two localizations and linked them as one trajectory if they were less than three pixels apart. 1-CDF curves were generated and fit to a double exponential decay model:sb and ktb values are affected by technical and imaging limitations such as photobleaching and chromatin movements. To correct for this bias, we used apparent dissociation rates of H2B imaged under same conditions as described previously (sb) and transient binding (τtb) were calculated as follows:The apparent keviously . The cor2 < 0.8 plots). From the slope, the diffusion coefficient was calculated as :Apparent diffusion coefficient values for stably bound trajectories: All ‘masked’ trajectories lasting at least five frames  were analyzed, using Sojourner package. For each trajectory, MSD plot for time lags from 2 to 5 △t (△t = 10 ms) were generated, then fit to linear regression  is the average time it takes for a molecule to go from one specific site to its next specific site. The two specific binding events (lasting for τsb) are interspersed by a number of trials  binding to non-specific sites (lasting for τtb). τfree is the average free time between two binding events. Assuming equal probability of binding to all specific and non-specific sites, the search time is calculated as follows:Search time  to specific sites (Ns), or rs:Ns can be determined based on two assumed scenarios for bound molecules observed during slow tracking  is the time interval between two specific binding events at a given site as described previously :Samplingtargets values presented by molecules was determined as the median and standard error values  values were used to simulate sequential promoter-occupied and vacant states over the time trace (500 s). The duration for each occupied and vacant state was randomly chosen from exponential distributions of the average τsb and (SI- τsb) values, respectively. For promoter regions targeted by multiple remodelers, each remodeler was independently subject to the occupancy simulation, and the number of any single or multiple remodeler(s) co-occupying each timepoint was calculated throughout the time trace.Remodeler occupancy at a target promoter region was simulated as described previously . Briefly public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Acceptance summary:In this study, Kim and co-workers track the dynamics of a large set of different ATP-dependent chromatin remodelers in living cells by utilizing state-of-the-art single-molecule imaging. They report that the remodelers exhibit very high turnover rates at target loci/nucleosomes, find evidence for cooperativity among the remodelers, and reveal the role of ATP hydrolysis in those interactions. These observations allow the authors to put forward a model for tug-of-war activities that modulate the accessibility of promoter regions for transcriptional activity. This manuscript brings important new information to the remodeler and chromatin dynamics field.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by Sebastian Deindl as the Reviewing Editor and Kevin Struhl as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Xiaojun Ren (Reviewer #1); Bradley R Cairns (Reviewer #2).Thank you for submitting your article "Single-molecule imaging of chromatin remodelers reveals role of ATPase in promoting fast kinetics of target search and dissociation from chromatin" for 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.Essential revisions:1) The impact of the paper would be increased if the model presented in Figure 8 could be extended to include all six remodelers studied, thereby highlighting their kinetics similarities and the resulting tug-of-war at promoters.2) The authors should comment on the perceived discrepancy in the relative abundance of the remodelers. The authors should consider adding the proposed quantification via Western Blot if feasible.3) The authors should comment on several important technical points raised by the reviewers, in particular in terms of the imaging conditions  and data analysis :1) It appears to this reviewer that the Log D and jump distance histograms in Figures 1 and 2 should be decomposed into three components: chromatin-bound, intermediate diffusive, and free diffusive, since the authors have identified an intermediate diffusive population that can transition between bound and free states, which are shown in Figure 3.2) The authors use the number of molecules of remodelers from published results  to calculate target-search kinetics. It appears to this reviewer that the published data does not match the data in Figure S1A. For instance, Figure 7A shows that INO80 is more abundance than ISW2; however, Figure S1A shows the opposite results. Given that these remodelers have been endogenously tagged, it would be possible to quantify the number of molecules of remodelers through western blotting by comparing to known concentrations of HaloTag.3) P27L603: It should be 10 ms exposure time.4) P27L605: Please explain why each video lasts 1.5 min.5) P27L607: It should be 250 ms exposure time.6) P27L612: Please explain again why each video lasts 7-9 min.7) P28L638: Please check the equation.8) P32L716: Please check the reference.Reviewer #2 (Recommendations for the authors):The model presented in figure 8 restricts itself to two of the remodelers (RSC and INO80) addressed, and does not reflect the full scope of the results obtained in this work. It would be helpful if changes could be made to the model figure in order to embrace all six remodelers studied, highlighting their kinetics similarities, and the resulting tug-of-war at promoters. Notably, it would be helpful to consolidate and clarify the ATPase state, as ATP and ADP+Pi are written concomitantly at all stages of each remodeler presented. Moreover, the model would benefit from a more clear depiction of ATP binding in the enhancement of nucleosome browsing, and of ATP hydrolysis in the enhancement of dissociation.Reviewer #3 (Recommendations for the authors):The paper is a technical tour-de-force, but I have a number of technical questions that need clarification. These arise partially from my ignorance, but also from the fact that there seem to be details missing in the methods section. Given that the paper should be read by a maximal number of chromatin biologists, I think it would greatly enhance the impact of the paper to include explanations concerning the following points, either in the methods section or in the text itself.1. How do they know that they are actually imaging SINGLE chromatin remodelers in every case, and not clusters of a given remodeler, for example, bound at multiple nucleosomes at a promoter (or cluster of promoters) ? I understand that they use a pulsed laser to activate the Halo tag fluorochrome, but it seems to me that they need to quantify the number of molecules each signal might represent. Hansen and Darzacq have worked out means to do this. The authors could also estimate the number of total molecules of each remodeler this way, which would be useful to know, especially to see if this influences occupancy rates.2. The pixel size (over 100 nm) is quite large – certainly large enough to contain multiple protein complexes. How large are the signals they monitor ? Did the try to reduce pixel size (it is possible with the LSM 710 and 100x objective)?3. The authors use an imaging scheme worked out by Hansen et al. for "rapid" and "slower" movements. In one they image at 10ms intervals for 1.5 min, but for the slower one they image at 250ms intervals for 7-9 minutes. This latter scheme implies a lot of exposure to laser light. Did they check that there was no impact on cell division rate after the slower, longer imaging protocol ? It is a massive dose, and the time needed for cell division should be monitored under these conditions.4. The authors say that they image for 2 h after the immobilization of cells on a cover slide. Are the cells continually bathed in medium during imaging ? this should be stated, if so. During 2 hours without glucose yeast rapidly deplete their ATP levels and this strongly influences chromatin mobility and remodeler activity . It leads one to wonder if the slower moving remodeler tracks were taken at the end of the 2h period and the faster ones at the early time points ? Or did they keep a flow of fresh media onto the cells ? This is a very important technical point that should be explained in the methods.5. For the longer (but not shorter) tracking and subsequent calculation of diffusion coefficients the authors should be conscious that nuclear movement  can alter the track, if not normalized out. I guess the fact that they subtract H2B movement does normalize for cellular or instrumental drift, but perhaps they should state that. Other means exist, e.g. to track the center of the nucleus interpolated from a ring of nuclear pores or background nuclear fluorescence – and subtract that movement from the movement of the SPT. Some discussion of removal of such background drift should be mentioned in methods, or if this has already been ruled out elsewhere, the reference should be cited.6. Finally, the authors assume that the nucleosome remodelers are almost exclusively involved in transcription, and based on this they make the tug-of-war argument that INO80 and ISWI2 are counteracting RSC and Swi/SNF action. However, the two remodelers that have slightly different diffusion histograms (INO80 and ISWI2) are both implicated in DNA replication , as well as transcription. The Tsukiyama lab has clearly demonstrated their roles in replication, and various authors provided convincing evidence that INO80 is important for paused fork restart and other repair events. DNA repair may be too rare an event (compared to transcription) to bias the movement observed, but replication could definitely bias the mobility/diffusion or Spot-on profiles, if a fraction of the cells imaged were in S phase.This raises a number of questions:a) Were the cells imaged exclusively in G1 phase ? That is, were only cells without buds monitored ? This should be stated if so.b) If not, did they monitor INO80 or ISWi2 diffusion specifically in S phase cells and did this lead to the same or a different profile of chromatin-bound/-free fractions and diffusion co-efficients?c) The diffusion histograms are fit to two Gaussian curves, but they would also be compatible with the superposition of multiple populations. Given that INO80 (and probably ISWI2) respond to phosphorylation, and may be regulated by cell cycle phase – it might be that there are more than simply a "bound" and an "unbound" population. This should – at the very least be discussed.I think it should be mentioned that these specific two remodelers are involved in replication – and what that might imply – unless all cells imaged were in G1 phase. If they were all G1 phase cells, then it should have been mentioned (sorry if I missed it).d) There could also be multiple variant complexes, containing additional subunits or being post-translationally modified, that might account for the complexity of Diffusion coefficients. This should at least be mentioned somewhere in the discussion.With clarity on these issues, I think this paper definitely brings important new information to the remodeler and chromatin dynamics field. Essential revisions:1) The impact of the paper would be increased if the model presented in Figure 8 could be extended to include all six remodelers studied, thereby highlighting their kinetics similarities and the resulting tug-of-war at promoters.We agree that the impact of the paper could be increased by extending Figure 8 model to include the main remodelers performing ‘tug-of-war’ at promoters – RSC, SWI/SNF, INO80, ISW2, and have revised the model and text [P24L523 ~ P25L550] accordingly. . As requested, we have also relabeled the figure to distinguish between ATP-binding and ATP-hydrolysis at the steps of 1D diffusion and nucleosome remodeling/remodeler dissociation. We thank the reviewer for suggesting these changes, which improves the model’s general accessibility.2) The authors should comment on the perceived discrepancy in the relative abundance of the remodelers. The authors should consider adding the proposed quantification via Western Blot if feasible.We appreciate the reviewer’s perceptive comment on the discrepancy between the in-gel JF646 intensities as shown in Figure 1—figure supplement 1A and the # molecules/cell presented by Ho et al., 2018, shown in Figure 7A. The discrepancy can be attributed to the protein extraction method  we used which does not provide quantitative protein extraction due to the yeast cell wall barrier. For example, SDS-PAGE in-gel JF646 analysis of RSC lysates shows substantial signal retained in the insoluble pellet in addition to the supernatant . Even with pellet analysis, we are unsure that there are no additional losses incurred during extraction. The purpose of our original presentation was simply to show the integrity of the HaloTagged factors at their expected sizes. We rely on Ho et al.’s extensive meta-analysis of twenty-one yeast proteomic studies for the best available quantitative estimate of remodeler abundance, with the assumption, now explicitly stated in the revised text [P18L371], that the functional fusions are similar in abundance to the untagged proteins.molecules values for the median, which more accurately represents the central value when datasets have substantial outliers).As a rough test for consistency with Ho et al., we quantified the live-cell JF intensities by measuring the initial nuclear fluorescence upon laser illumination in our fast-tracking videos. We found that INO80 and ISW2 abundances are substantially lower than RSC but similar to each other , compara3) The authors should comment on several important technical points raised by the reviewers, in particular in terms of the imaging conditions  and data analysis :1) It appears to this reviewer that the Log D and jump distance histograms in Figures 1 and 2 should be decomposed into three components: chromatin-bound, intermediate diffusive, and free diffusive, since the authors have identified an intermediate diffusive population that can transition between bound and free states, which are shown in Figure 3.Thank you for the recommendation. We understand that Figures 1 and 2 LogD histograms may confuse readers by our presentation of 2-component fits when in fact there is an intermediate population described in Figure 3. Hence, we revised our original figures 1 and 2 to show the Spot-On values for fraction bound, and moved the LogD histograms to Figure 1—figure supplement 4. The vbSPT analysis shown in Figure 3 reveals that in addition to two main diffusive populations there is a third population composed of trajectories containing both bound and free states.2) The authors use the number of molecules of remodelers from published results  to calculate target-search kinetics. It appears to this reviewer that the published data does not match the data in Figure S1A. For instance, Figure 7A shows that INO80 is more abundance than ISW2; however, Figure S1A shows the opposite results. Given that these remodelers have been endogenously tagged, it would be possible to quantify the number of molecules of remodelers through western blotting by comparing to known concentrations of HaloTag.Please see above response to Editor’s Essential revisions.3) P27L603: It should be 10 ms exposure time.We have corrected the text accordingly [P30L654].4) P27L605: Please explain why each video lasts 1.5 min.We have added this paragraph to methods for clarification [P30L656]:Single-molecule imaging was performed using dSTORM  . Each video begins with global nuclear fluorescence on laser excitation of essentially all JF552-labelled molecules before photoconversion to a dark, non-fluorescent state. The duration of the initial excitation which pushes fluorescent JF552 to the dark state is ~10 s, depending on the density of labeled Halo-proteins, which is a function of protein expression and dye labeling concentration adjusted for the specific protein. Once in the dark state, JF552 spontaneously and stochastically converts back to the fluorescent state .This conversion, a characteristic of fluorophore photochemistry, is sufficiently slow such that mostly one JF552 molecule per yeast nucleus is fluorescent at any given time during data acquisition, allowing for an unambiguous spatio-temporal record of single JF552-labeled proteins. To ensure that we can harvest a ‘substack’ of at least 5000 frames (10 ms/frame) , we routinely record videos of ~1.5 min.5) P27L607: It should be 250 ms exposure time.We have corrected the text accordingly, thank you for catching the error [P31L676].6) P27L612: Please explain again why each video lasts 7-9 min.Upon 555 nm laser illumination, we record ~ 5 min [correction of 7-9 min] videos starting with fluorescent JF552 pushed to the dark state for > 30 s, followed by single-molecule imaging of at least 750 frames at 250 ms/frame. This correction is included in revised methods [P31L681].7) P28L638: Please check the equation.8) P32L716: Please check the reference.The reference is now in press, and we have changed accordingly .Reviewer #2 (Recommendations for the authors):The model presented in figure 8 restricts itself to two of the remodelers (RSC and INO80) addressed, and does not reflect the full scope of the results obtained in this work. It would be helpful if changes could be made to the model figure in order to embrace all six remodelers studied, highlighting their kinetics similarities, and the resulting tug-of-war at promoters. Notably, it would be helpful to consolidate and clarify the ATPase state, as ATP and ADP+Pi are written concomitantly at all stages of each remodeler presented. Moreover, the model would benefit from a more clear depiction of ATP binding in the enhancement of nucleosome browsing, and of ATP hydrolysis in the enhancement of dissociation.Thank you for the comment. Please see detailed response to Essential revisions above.Reviewer #3 (Recommendations for the authors):The paper is a technical tour-de-force, but I have a number of technical questions that need clarification. These arise partially from my ignorance, but also from the fact that there seem to be details missing in the methods section. Given that the paper should be read by a maximal number of chromatin biologists, I think it would greatly enhance the impact of the paper to include explanations concerning the following points, either in the methods section or in the text itself.1. How do they know that they are actually imaging SINGLE chromatin remodelers in every case, and not clusters of a given remodeler, for example, bound at multiple nucleosomes at a promoter (or cluster of promoters) ? I understand that they use a pulsed laser to activate the Halo tag fluorochrome, but it seems to me that they need to quantify the number of molecules each signal might represent. Hansen and Darzacq have worked out means to do this. The authors could also estimate the number of total molecules of each remodeler this way, which would be useful to know, especially to see if this influences occupancy rates.Please see our response to Reviewer 1’s Comment #4 above. In addition, to further illustrate the point, we refer the reviewer to a sequence of frames in Regarding the number of molecules per cell, see our response to Essential revisions above.2. The pixel size (over 100 nm) is quite large – certainly large enough to contain multiple protein complexes. How large are the signals they monitor ? Did the try to reduce pixel size (it is possible with the LSM 710 and 100x objective)?Pixel size is a fixed number based on camera resolution and microscope objective. Despite the 107 nm square pixel size and the spread of signal over several pixels, the ability to separately image individual molecules allows localization of the centroid by Gaussian fitting the signal distribution over multiple pixels to the center of the PSF (point spread function) with a precision of ~30 nm . [P31L693]3. The authors use an imaging scheme worked out by Hansen et al. for "rapid" and "slower" movements. In one they image at 10ms intervals for 1.5 min, but for the slower one they image at 250ms intervals for 7-9 minutes. This latter scheme implies a lot of exposure to laser light. Did they check that there was no impact on cell division rate after the slower, longer imaging protocol ? It is a massive dose, and the time needed for cell division should be monitored under these conditions.2 continuous laser and image a 128x128 pixel field of view (containing 4-6 yeast cells) for ~1.5 minutes at 10 millisecond camera integration time. Under these conditions, illuminated cells, monitored every 30 minutes for up to 3 hours, exhibited no observable cellular damage and underwent bud growth and cell division similar to unexposed cells. This finding is provided by Nguyen et al., 2021 from our lab  and re-stated in our methods with the citation for the readers [P31L671]. . As a further check, we separately analyzed the first and second halves of our fast-tracking videos and found no substantial differences, indicating that there are little or no progressive changes in diffusive behaviors that we can detect over 1.5 min [P31L673]. This is now documented in Figure 1—figure supplement 3B. Importantly, for slow-tracking, low laser illumination requiring only 5% of the previous laser power [P31L677] provides sufficient signal over the ~5 min of imaging. These details are given in methods.We share the reviewer’s concern about photodamage. For fast tracking, we excite the sample with ~1 kW/cm4. The authors say that they image for 2 h after the immobilization of cells on a cover slide. Are the cells continually bathed in medium during imaging ? this should be stated, if so. During 2 hours without glucose yeast rapidly deplete their ATP levels and this strongly influences chromatin mobility and remodeler activity . It leads one to wonder if the slower moving remodeler tracks were taken at the end of the 2h period and the faster ones at the early time points ? Or did they keep a flow of fresh media onto the cells ? This is a very important technical point that should be explained in the methods.We share the concern over general yeast health during imaging. After harvesting log-phase cells and 3x washing with Synthetic Complete Medium (with glucose) to remove excess dye, yeasts are immobilized on a coverslip for microscopy (in imaging chamber Attofluor Cell Chamber A7816). After immobilization and additional washing, ~1.5 mL of fresh medium is added to the chamber in which the cells are bathed continually for the duration of the session (~2 h). We include these details in the Methods section . Furthermore, comparison of results from the first and second halves of an imaging session shows no substantial differences, suggesting that cells remain in good health  [P30L634].5. For the longer (but not shorter) tracking and subsequent calculation of diffusion coefficients the authors should be conscious that nuclear movement  can alter the track, if not normalized out. I guess the fact that they subtract H2B movement does normalize for cellular or instrumental drift, but perhaps they should state that. Other means exist, e.g. to track the center of the nucleus interpolated from a ring of nuclear pores or background nuclear fluorescence – and subtract that movement from the movement of the SPT. Some discussion of removal of such background drift should be mentioned in methods, or if this has already been ruled out elsewhere, the reference should be cited.apparent diffusion coefficients, but provide H2B as a reference standard for global chromatin and nuclear motions and instrument drift. We added a statement in the text to be clear about this [P15L317].For stably-bound remodelers measured by slow-tracking, we present uncorrected, 6. Finally, the authors assume that the nucleosome remodelers are almost exclusively involved in transcription, and based on this they make the tug-of-war argument that INO80 and ISWI2 are counteracting RSC and Swi/SNF action. However, the two remodelers that have slightly different diffusion histograms (INO80 and ISWI2) are both implicated in DNA replication , as well as transcription. The Tsukiyama lab has clearly demonstrated their roles in replication, and various authors provided convincing evidence that INO80 is important for paused fork restart and other repair events. DNA repair may be too rare an event (compared to transcription) to bias the movement observed, but replication could definitely bias the mobility/diffusion or Spot-on profiles, if a fraction of the cells imaged were in S phase.This raises a number of questions:a) Were the cells imaged exclusively in G1 phase ? That is, were only cells without buds monitored ? This should be stated if so.We imaged asynchronous log phase cells without selecting specific stages of the cell cycle. Thus, cells undergoing S-phase are included in our analysis, and the measured dynamics would include remodeler participation in replication as well as transcription. We thank the reviewer for the reminder and have revised the text accordingly [P5L144] and [P30L652].b) If not, did they monitor INO80 or ISWi2 diffusion specifically in S phase cells and did this lead to the same or a different profile of chromatin-bound/-free fractions and diffusion co-efficients?We have not selected or synchronized for cells in S-phase in this study, and plan to investigate the interesting question of remodeler participation in replication and repair, particularly for INO80, in the near future.c) The diffusion histograms are fit to two Gaussian curves, but they would also be compatible with the superposition of multiple populations. Given that INO80 (and probably ISWI2) respond to phosphorylation, and may be regulated by cell cycle phase – it might be that there are more than simply a "bound" and an "unbound" population. This should – at the very least be discussed.I think it should be mentioned that these specific two remodelers are involved in replication – and what that might imply – unless all cells imaged were in G1 phase. If they were all G1 phase cells, then it should have been mentioned (sorry if I missed it).d) There could also be multiple variant complexes, containing additional subunits or being post-translationally modified, that might account for the complexity of Diffusion coefficients. This should at least be mentioned somewhere in the discussion.Response to comments (c and d):We thank the reviewer for the important reminder that remodelers are not exclusive to transcription, and have revised the text to include this in [P4L96], [P19L390], and [P20L421]. Indeed, INO80 and ISW2 are prime candidates for dynamic analysis under specific conditions of replication and repair and will be subjects of future analysis. We also agree that there could be various sub-populations within our two observable diffusive populations which are beyond dissection by SMT. We have included this in the Results [P8L186] and Discussion [P26L573].


Retroviruses are obligate intracellular parasites that must integrate a copy of the viral genome into the host DNA. The integration reaction is performed by the viral enzyme integrase in complex with the two ends of the viral cDNA genome and yields an integrated provirus. Retroviral vector particles are attractive gene therapy delivery tools due to their stable integration. However, some retroviral integration events may dysregulate host oncogenes leading to cancer in gene therapy patients. Multiple strategies to target retroviral integration, particularly to genetic safe harbors, have been tested with limited success. Attempts to target integration may be limited by the multimerization of integrase or the presence of host co-factors for integration. Several retroviral integration complexes have evolved a mechanism of tethering to chromatin via a host protein. Integration host co-factors bind chromatin, anchoring the complex and allowing integration. The tethering factor allows for both close proximity to the target DNA and specificity of targeting. Each retrovirus appears to have distinct preferences for DNA sequence and chromatin features at the integration site. Tethering factors determine the preference for chromatin features, but do not affect the subtle sequence preference at the integration site. The sequence preference is likely intrinsic to the integrase protein. New developments may uncouple the requirement for a tethering factor and increase the ability to redirect retroviral integration. By stably inserting a transgene into a patient’s genome, retroviral gene therapy vectors offer the possibility of curing monogenic diseases . RetroviThere are several consequences of stable retroviral integration in patients. In the cases of human pathogens HTLV-1 and HIV-1, it has been impossible to cure patients with the notable exception of two HIV-1 patients . During ex vivo, followed by successful engraftment and permanent functional cure of monogenic disorders. These disorders include X-linked chronic granulomatous disease (X-CGD), Wiskott-Aldrich syndrome (WAS), X-linked adrenoleukodystrophy, and X-linked severe combined immune deficiency (X-SCID)  . Despite(X-SCID) . GeneticTargeting retroviral integration to genetic “safe harbors” in the host genome that will not lead to cancer has become an imperative for the use of retroviral gene therapy vectors . IntegraAdeno-associated virus (AAV) vectors, which are not retroviruses, are also used as gene therapy vectors and may sometimes integrate at a specific locus in the human genome. AAV vectors are beyond the scope of this manuscript and have been extensively reviewed elsewhere .Retroviral gene therapy vectors that have been used in humans and animals have been derived from MLV, HIV-1, avian sarcoma leukosis virus (ASLV), and PFV. All retroviral INs have a zinc coordinating amino terminal domain (NTD), a catalytic core domain (CCD) with the enzymatic DD (35)E motif, and a carboxyl terminal domain (CTD) that is the least conserved between retroviruses . Some reRetroviral IN catalyzes two reactions during infection. Following reverse transcription of the viral genomic RNA to a linear double stranded cDNA IN removes a GT dinucleotide from the 3′-terminus of each viral DNA end, termed 3′-processing. In the nucleus IN covalently joins hydroxyls at both 3′ ends of the viral cDNA to the host DNA in independent single step transesterification reactions . These tEach retrovirus appears to have a distinct preference for integration site selection in cells . These pIn addition to genomic elements most retroviruses appear to have a unique subtle sequence preference at the points of joining . The preMany retrovirus families have host proteins that act as integration co-factors . These pPSIP1) is a transcriptional co-activator which interacts with a variety of cellular proteins including menin, mixed-lineage leukemia histone methyltransferase (MLL), and pogo transposable element with ZNF domain (PogZ)  . It consn (PogZ) . The PWWn (PogZ) . H3K36men (PogZ) . The IBDn (PogZ) . The IBDn (PogZ) . Taken tn (PogZ) .PSIP1 reduced HIV-1 infection 10-fold and reduced integration into actively transcribed genes . Alpharetrovirus ASLV IN has been shown to bind the heterodimeric facilitates chromatin transcription (FACT) complex which stimulates integration activity in vitro . Furtherin vitro . While tin vitro . Deltarein vitro . HoweverAdditional cellular factors have been shown to be involved in targeting retroviral integration to select genomic features. While not the focus of this review, a brief discussion is warranted. These factors have been studied in the context of their interaction with the HIV-1 capsid (CA) core and include cleavage and polyadenylation specificity factor subunit 6 (CPSF6), nucleoporin protein 153 (Nup153), and E3 SUMO-protein ligase (RANBP2 or Nup358). CPSF6 is a chromatin associated protein and a member of several nuclear complexes such as cleavage factor Im (CFIm) complex, paraspeckles, and nuclear speckles . It integp91phoxex vivo and engrafted into mice with X-CGD. The mice displayed partial reconstitution of superoxide production, increased phagocytosis, and significantly increased survival after challenge with B. cepacia . While d IL-2RG . These tcription . Patientcription . While tMKL2 and BACH2 oncogenes  do have higher incidence of non-AIDS-defining malignancies (NADM) such as Hodgkin’s lymphoma, oropharyngeal cancer, anal cancer, hepatocellular carcinoma, and non–small cell lung cancer . Increasncogenes . A seconncogenes . While tncogenes .HMGA2 gene was observed in a single patient  and stably up to 18 months , such as PFV, are not known to cause disease in animal infections or xenotropic human infections. The FV life cycle differs from other retroviruses and lentiviruses which precludes them from being efficiently pseudotyped (reviewed in ). Howeve animals . Four of animals . There w animals . Similarystrophy .ex vivo, FV has also been directly administered intravenously to dogs  and AMD3100 to mobilize hematopoietic stem and progenitor cells, followed by injection of the FV gene therapy vector, and showed the greatest survival of 2.5 years , octamers , and hexadecamer  . SeveralRetroviral integration site selection may be redirected by altering the chromatin binding domain of the respective cellular co-factors. Perhaps the best studied integration cofactor is LEDGF/p75. There have been a variety of reported alternative chromatin binding domains fused to the IBD of LEDGF/p75. The first successful attempt involved fusing the first 31 amino acids of kaposi’s sarcoma-associated herpesvirus (KSHV) latency-associated nuclear antigen (LANA) to LEDGF/p75 (93–530) lacking the PWWP domain . KSHV isMost of the re-targeting studies of LEDGF/p75 fusions have been performed in LEDGF/p75 knockdown or depleted cells. This is an impractical approach for patients due to the necessity of removing endogenous LEDGF/p75 prior to gene therapy. There is one reported attempt of using a LEDGF/p75 fusion in a wild type background . Cells wPFV IN is not known to require a host co-factor that tethers integration complexes to chromatin. However, the PFV IN CTD appears to interact with the amino terminus of nucleosome protein H2A . In addiin vitro or infection of cells (Another avenue for tether independent targeting is to generate retroviral vectors that no longer need their respective co-factor. This is not possible with HIV-1 IN as LEDGF/p75 not only plays a critical role in tethering but is also essential for IN catalytic activity . Howeverof cells . Biochemof cells . Truncatof cells . Studiesof cells . These sThe first goal of safer retroviral gene therapy vectors is to remove the possibility of cellular transformation and oncogenesis. This must be coupled with sufficient functional rescue and sustained gene expression. Recent evidence suggests that lentiviral-based gene therapy vectors are closer to achieving these goals . FunctioOne question in retroviral gene therapy is to what extent will directed integration be possible: sequence specific sites, unique histone PTMs, or genomic regions? Retroviral INs may impose some subtle preference for sequence at the integration site, but the preference is not stringent . Directi