Aberrant enhancer activation is a key mechanism driving oncogene expression in many cancers. While much is known about the regulation of larger chromosome domains in eukaryotes, the details of enhancer-promoter interactions remain poorly understood. Recent work suggests co-activators like BRD4 and Mediator have little impact on enhancer-promoter interactions. In leukemias controlled by the MLL-AF4 fusion protein, we use the ultra-high resolution technique Micro-Capture-C (MCC) to show that MLL-AF4 binding promotes broad, high-density regions of enhancer-promoter interactions at a subset of key targets. These enhancers are enriched for transcription elongation factors like PAF1C and FACT, and the loss of these factors abolishes enhancer-promoter contact. This work not only provides an additional model for how MLL-AF4 is able to drive high levels of transcription at key genes in leukemia but also suggests a more general model linking enhancer-promoter crosstalk and transcription elongation. Previous studies have reported MLL-AF4 binding at intragenic and intergenic enhancers, however, the role of MLL-AF4 in enhancer function remains to be investigated. Here, the authors show that MLL-AF4 cooperates with PAF1 and FACT at enhancers to promote high-density interactions with oncogene promoters in leukemia. Much is now known about the factors that govern higher-order 3D genome structure, but the mechanistic details that control enhancer-promoter crosstalk remain poorly understood3.There is a growing understanding that alterations in the three-dimensional structure of the genome  can have a profound impact on gene expression5. Epigenetic changes in cancer are also increasingly recognized as important drivers of gene expression changes6. Active enhancers commonly display characteristics that include an open chromatin conformation, close spatial proximity to the target promoter8, bidirectional transcription of unstable enhancer RNA (eRNA)9, and post-translational histone modifications such as histone H3 lysine-4 monomethylation (H3K4me1) and H3 lysine-27 acetylation (H3K27ac)10.Enhancers are key regulatory elements that contribute to gene expression. Aberrant enhancer activity is a major factor in many cancers, often involving specific DNA mutations or even large-scale DNA rearrangements1. Enhancers have been proposed to drive transcription at both initiation12 as well as promoter-proximal pause release14. Live imaging has suggested that strong enhancers impact the transcription cycle by increasing burst frequency rather than burst size16, arguing that one of the main roles of enhancers is to increase the probability of successful transcription events.Exactly how enhancers control gene expression remains unknown, although they are thought to function in part by acting as docking sites for transcription factors, which then activate appropriate target genes over long distances18, but this does not seem to be necessary for inducing gene expression during differentiation18. Transcription factor binding has also been thought to be essential for the initiation of enhancer-promoter crosstalk, but this has not been shown directly. Co-activators such as BRD4 and Mediator have been proposed to bridge the gap between transcription factor binding and enhancer-promoter contact25, although recent work from us and others suggests that these co-activators are not necessary for the maintenance of enhancer-promoter proximity or at least only have a subtle role27. However, we identified a subset of enhancers, H3K79me2/3-marked Enhancer Elements (KEEs), which are highly dependent on this methylation for the maintenance of gene expression and enhancer-promoter interactions28. What is not clear is whether other components of the transcriptional machinery might have similar crucial impacts on maintaining enhancer activity and whether they are necessary for the maintenance of enhancer-promoter crosstalk.The factors that initiate enhancer-promoter crosstalk may be different from those that maintain it. For instance, during embryonic stem cell differentiation, pre-binding of the Polycomb Repressive Complex (PRC) is required for initial enhancer-promoter contactsMixed Lineage Leukemia  gene (MLLr) cause aggressive, poor prognosis acute lymphoblastic (ALL) and acute myeloid (AML) leukemias in both children and adults31 and are associated with very few cooperating mutations36. Despite the relatively simple genetic landscape, MLLr leukemias exhibit a large number of epigenetic and transcriptional changes, suggesting that the MLL fusion protein (MLL-FP) drives oncogenesis via transcriptional reprogramming and therefore provides a good model to study aberrant activation of enhancers.Rearrangements of the 39. In many cases, MLL-FP binding spreads from the promoter into the gene body, associated with elevated levels of elongation factors and upregulated transcription40. Many target genes are also regulated by strong enhancers, and we and others have identified MLL-AF4 binding at intragenic and intergenic enhancers43, but the significance of this behavior and whether it is dependent on the same factors associated with MLL-AF4 at gene promoters has not been established.MLL-AF4, along with other MLL-FPs, is thought to drive leukemia by binding at the promoters of key oncogenes and upregulating their expression by the recruitment of a complex of elongation-associated factors, including the RNA Polymerase (RNAP) II-Associated Factor complex (PAF1C), Eleven Nineteen Leukemia , AF9, DOT1L, AFF4 and P-TEFb8, we show that MLL-AF4 binding is a major driver of aberrant enhancer activation, characterized by a high density of interactions between enhancers and promoters of key genes that drive leukemogenesis. Many of the proteins colocalizing at MLL-AF4-bound enhancers are the same elongation factors that associate with MLL-AF4 at target genes, including ENL and PAF1C. We identify here the histone chaperone Facilitates Chromatin Transcription (FACT) as an MLL-AF4 complex component found at enhancers, which is typically reported to be associated with transcription elongation. Chemical degradation of either PAF1 or the FACT component SSRP1 results in a drastic loss of enhancer-promoter interactions at many MLL-AF4-bound and -unbound enhancers, indicating that these factors are generally required for enhancer function. Together, this reveals an unexpected mechanism of oncogenicity for the MLL-AF4 fusion protein in maintaining enhancer-promoter contacts and argues that highly enriched transcription elongation activity can drive high levels of enhancer activity.In this study, we seek to understand the role of MLL-AF4 in enhancer function as a model to investigate mechanisms of aberrant enhancer activation in cancer. Using Micro-Capture-C (MCC), a highly sensitive 3C method44, optimizing it for analysis of more weakly-associated non-histone chromatin proteins, an approach we call Transcription factor-OPtimized ChIPmentation . Approximately 60% of MLL-AF4 bound genes (6021) were common between all patients, and very few promoters (≤184) were bound in only one sample , including three childhood  and one infant , combining gene expression and chromatin accessibility data with TOPmentation and ChIP-seq for H3K4me1, H3K4me3, H3K27ac, H3K27me3, H3K79me2 and the N-terminus of MLL-AF4 , a large proportion was found more than 10 kb from the nearest TSS Fig. . We obseFLT3, MYC, CDK6 , for example, at the FLT3 enhancer , for example, at TNFRSF14  were more highly expressed in MLLr ALL . Taken together, this suggests that MLL-AF4 binding to enhancers contributes to the unique gene expression pattern observed in MLL-AF4 leukemia.To further examine the specificity of the MLL-AF4-unique enhancers, we used a published patient RNA-seq datasetFLT3, MYC and CDK6 . In other cases, MLL-AF4 binds to pre-existing enhancers and may contribute to maintaining their activity in leukemia.Visual inspection of ATAC-seq data at key broad MLL-AF4-bound enhancers, for example, at the oci Fig. , revealeAD3 Fig. , suggest8 to look at a subset of key oncogenes. Although MCC does not allow genome-wide interaction analysis, the high sequencing depth permits precise mapping of DNA-DNA interactions at single base pair resolution8. The MCC interaction profiles for FLT3, ARID1B, CDK6 and MYC are markedly broad, showing extensive interactions aligning with the high density of ATAC-seq peaks (likely TF binding events)8 s)8 Fig. . These is)8 Fig. . This doD3, Fig. , MCC rev40. We intersected SEM enhancers and MLL-AF4 peaks, identifying enhancers enriched (bound) or depleted (not bound) for MLL-AF4  transcription, suggesting that MLL-AF4 is associated with high levels of enhancer activity Fig. . Surpris28. MLL-AF4 is associated with elevated H3K79me2/356, and both intragenic and intergenic enhancers bound by MLL-AF4 were enriched for H3K79me3 me3 Fig. . Indeed,me3 Fig. , and thime3 Fig. , suggestme3 Fig. , associame3 Fig. .59. We therefore asked whether MLL-AF4 recruits these components to enhancers to promote enhancer activity. The elevated levels of H3K79me3 at MLL-AF4 enhancers  and FACT (SSRP1) at both intergenic and intragenic MLL-AF4-bound enhancers, suggesting that MLL-AF4 may recruit PAF1C and FACT to enhancers , as well as TetR fusions of other complex components. Using ChIP-qPCR to assess the ability of these proteins to recruit other factors to the TetO array, we detected in vivo interactions between TetR-CXXC with PAF1 and SSRP1  at non-MLL-AF4-bound sites following PAF1 or SSRP1 degradation  occupancy across gene bodies, suggesting a role in suppressing promoter-proximal pause release90. However, other studies instead showed that loss of PAF1C impaired transcription elongation, indicating that PAF1 was essential for productive promoter-proximal pause release95, consistent with in vitro transcription and structural studies97. It is possible that PAF1C has different roles at different genes or in different cellular contexts; our results here are consistent with PAF1C promoting transcription elongation in both MLL-AF4 ALL and multiple myeloma cells.While PAF1 was originally identified as a transcriptional elongation factor, it has since been found to regulate multiple stages of the RNAPII transcription95. This is in contrast to the dramatic reduction in transcription produced by PAF1 degradation in SEM (MLL-AF4 ALL) cells but matches the much weaker effect we observed in MM1.S (multiple myeloma) cells. Thus, while PAF1 may have a particularly central role in transcription in MLL-AF4 leukemias, this is not universal and may be a consequence of the activity and multivalent interactions of the MLL-AF4 complex. A full answer to this question will be revealed by studying PAF1C function in a wider range of cell types and tissues.Recently, the use of degron technology has allowed the consequences of acute PAF1C degradation to be investigated. One study in K562 (chronic myelogenous leukemia) cells identified distinct requirements for different PAF1C components in stimulating RNAPII activity, with degradation of the PAF1 subunit only weakly affecting RNA synthesis100. Our finding that loss of PAF1C reduced enhancer activity, not only at MLL-AF4 bound enhancers but also at many non-MLL-AF4 bound enhancers, complements a recent study showing a strong correlation between PAF1C binding and enhancer activity99, arguing for a role in promoting transcription. In contrast, Chen et al.98 proposed that PAF1C restrains activation of a subset of enhancers by inhibiting RNAPII promoter-proximal pause release. Another study found that PAF1 knockout resulted in increased eRNA levels, which the authors attributed to PAF1 recruitment of Integrator to terminate transcription100. In our work, we observe the opposite effect; PAF1 degradation significantly decreased eRNA transcription in SEM cells. One possibility is that the timing of PAF1 depletion is critical; our assays were conducted after 24 h, whereas both previous studies used a window of 72 h or longer100, which could allow for additional secondary effects on enhancer activity. Surprisingly, while we observed a strong effect of PAF1 depletion on enhancer function in SEM cells, the consequences were minimal on MM1.S enhancer function, despite the high frequency of PAF1 binding at enhancers. This may also explain why SEM cells displayed a clear reduction in transcription initiation, whereas MM1.S cells only showed an effect on elongation. Together, these observations support the notion that PAF1C activity may vary based on the cell type, perhaps dependent on the presence or absence of specific complex components or the chromatin context itself.While relatively understudied, roles for PAF1C at enhancers have also been proposed100, it is not known how it localizes at these sites. Here we show that MLL-AF4 localizes PAF1C to enhancers via multiple direct and indirect interactions in a co-dependent manner, consistent with a role in promoting the transcription of target genes. It is unclear how PAF1C is recruited to enhancers independent of MLL-AF4, perhaps via interaction with RNAPII, although there may also be a role for wild-type MLL61.Although PAF1C binding to enhancers has been previously observed101. FACT has also been proposed to function in sequestering the H2A/H2B dimer for efficient reincorporation into the nucleosome after RNAPII/PAF elongation-mediated disruption97. Consistent with the effects we observe following SSRP1 degradation, depletion of FACT in yeast reduces both transcription and RNAPII assembly at promoters102. However, the role of FACT at enhancers is unclear, with some studies arguing it may suppress the expression of target promoters103, although our data argue for a role in promoting gene activation. As has been proposed for PAF1C99, FACT may perform a similar function at enhancers as in the gene body, facilitating RNAPII transcription, in this case, for the production of eRNAs. FACT disruption of nucleosome structure at enhancers may also contribute by aiding the binding of TFs, which in turn drive enhancer features. Indeed, we found that MLL-AF4 enhancers show a high density of chromatin accessibility and TF binding.The FACT complex functions in transcription by displacing histone H2A/H2B dimers from nucleosomes, promoting access for RNAPII transcription104. However, this domain is also the site of interaction with the AF4 fusion partner, placing MLL-AF4 and DOT1L in mutually exclusive complexes69. Thus, ENL/AF9 alone cannot mediate the MLL-AF4/DOT1L interaction. Our data demonstrate that DOT1L also interacts with PAF1C, consistent with the fact that Paf1 is required for H3K79 methylation in yeast106. PAF1C also binds to MLL via the MLL CXXC domain61 and ENL via its YEATS domain107, indicating two mechanisms by which PAF1C could associate with the MLL-AF4 complex. The ability of both the MLL and AF4 portions of the fusion to interact with many of these proteins may explain the strong enrichment of factors at MLL-AF4 binding sites. Thus, a network of multivalent interactions may assemble and stabilize the complex of proteins associated with MLL-AF4 at chromatin without requiring continuous binding. We identified FACT as a component of this complex, interacting with MLL, ENL, PAF1C and DOT1L in vivo via similar multivalent contacts. FACT has previously been found to interact with Oct4109 and TIF1γ/TRIM33110, suggesting that this may be a common mechanism to promote transcription.To understand how MLL-AF4 assembles a transcriptional complex at promoters and enhancers, we used the TetR binding assay to identify in vivo interactions. DOT1L and H3K79me2/3 are known to be associated with MLL-AF4 target genes, but how they are recruited is unclear. ENL/AF9 can interact with DOT1L via their ANC homology domain (AHD)59, may be sufficient to bridge the interface. We have previously proposed that these interactions drive spreading of MLL-AF4 in cis from the promoter into the gene body40, and our data here suggest that they may also facilitate trans interactions between MLL-AF4-bound at the enhancer and promoter  under the Blood Cancer UK Childhood Leukaemia Cell Bank (now VIVO Biobank) ethical approval: NHS HRA: REC: 16/SW/0219. All patients gave informed consent via a specific CellBank consent form, as found at https://cellbank.org.uk/participants. Informed consent was obtained from all participants or those with parental responsibility, and participants did not receive any monetary compensation. The consent is held by CellBank and the treating hospital.The research in this paper complies with all relevant ethical regulations. More specifically, ethics oversight is governed by the UK Human Tissue Authority  and childhood (1–18 years old) ALL samples were obtained from Blood Cancer UK Childhood Leukaemia Cell Bank (now VIVO Biobank). There were three childhood ALL  primary samples and one infant (iALL #2) primary sample, one female and three males. The infant primograft was from previously published data on sample L826, a male iALL patient (iALL #1)121 were purchased from DSMZ (www.cell-lines.de) and cultured in Iscove’s Modified Dulbecco’s Medium (IMDM) with 10% fetal calf serum  and Glutamax (ThermoFisher Scientific). RS4;11 (CRL-1873) and MM1.S (CRL-2974) cells were purchased from ATCC (www.lgcstandards-atcc.org) and cultured in RPMI 1640 with 10% FCS and Glutamax. Cells were validated by the supplier by STR DNA typing. Mouse ES cells with a TetO-array (TOT2N mESC) were kindly provided by Prof. Rob Klose (University of Oxford) and were grown in DMEM supplemented by with 10% FCS, NEAA, Glutamax, LIF and β-mercaptoethanol. All cell lines were confirmed free from mycoplasma contamination.SEM cells (ACC 546)40, using the following sequences: NT siRNA: sense AAAAGCUGACCUUCUCCAAUG; antisense CAUUGGAGAAGGUCAGCUUUUCU. SEM MLL-AF4 knockdown siRNA: sense AAGAAAAGCAGACCUACUCCA; antisense UGGAGUAGGUCUGCUUUUCUUUU. RS4;11 MLL-AF4 knockdown siRNA: sense ACUUUAAGCAGACCUACUCCA; antisense UGGAGUAGGUCUGCUUAAAGUCC. Briefly, cells were resuspended at a density of 108/ml, and siRNA was added to a concentration of 250 nM. Cells were subjected to a 10 ms 330 V electroporation using a rectangle pulse EPI 2500 electroporator , after which they were diluted to 106/ml. Cells were either harvested at 48 h, or at this point, a second electroporation was performed, and cells were harvested after a further 48 h for a 96 h knockdown.siRNA knockdown of MLL-AF4 was conducted as previously described40. Briefly, 107 cells were single-fixed  for histone modifications or double-fixed  for transcription/chromatin factors, then lysed with 120 µl SDS lysis buffer  and sonicated using a Covaris ME220  to generate 200–300 bp fragments. Insoluble material was pelleted, then the supernatant was diluted 10× and pre-cleared for 30 min at 4 °C with rotation using 5 µl protein A- and G-coupled dynabeads (ThermoFisher Scientific). An input sample was taken, then 2 µg antibody was added to the sample before incubation overnight at 4 °C with rotation. The antibodies used are detailed in Supplementary Table Chromatin immunoprecipitation was conducted as previously described122, fixed Drosophila S2 cells were added to fixed SEM cells prior to sonication at a ratio of 1:4 S2:SEM. After sequencing, reads were adjusted based on the ratio of dm3:hg19 reads in the input and IP samples for each condition.For reference-normalized ChIP-seq4 live cells using Nextera Tn5 transposase (Illumina) as previously described123. Libraries were sequenced by paired-end sequencing with a 75-cycle high-output Nextseq 500 kit (Illumina).ATAC-seq was conducted on 5 × 1044. For TOPmentation, protein A-coupled magnetic beads (10 µl) were incubated with 1 µl of the appropriate antibody for 4 h with rotation at 4 °C in 150 µl Binding Buffer (PBS with 0.5% BSA and 1× protease inhibitor cocktail). The antibodies used are detailed in Supplementary Table HT-ChIPmentation was conducted as previously described2, 10% dimethylformamide) and adding 1 µl of transposase (Illumina). Samples were incubated at 37 °C for 10 min, and the reaction was terminated by adding 150 µl RIPA buffer. Beads were washed with 10 mM Tris-HCl pH 8.0 to remove any detergent and resuspended in 22.5 µl ddH2O. In order to amplify the tagmented chromatin, 25 µl NEBNext Ultra II Q5 Master Mix (NEB) and indexed amplification primers  were added, and libraries were prepared using the following thermal profile: 72 °C 5 min, 95 °C 5 min,  × 11 cycles, 72 °C 5 min. Library clean-up was performed using Agencourt AMPure XP beads at a 1:1 ratio. Samples were sequenced by paired-end sequencing on a NextSeq 500 (Illumina).Immunoprecipitated chromatin was washed three times with RIPA buffer . Beads were transferred to a second tube between the first and second wash to remove non-immunoprecipitated fragments that adhered to the tube. The beads were then washed once with Tris-EDTA and once with 10 mM Tris-HCl pH 8.0. The chromatin was then tagmented by resuspending beads in 29 µl Tagmentation Buffer , reads were trimmed using trim_galore with Cutadapt . Trimmed reads were then mapped to the hg19 reference genome using Bowtie 2 (v2.5.1)124. PCR duplicates were removed using picard MarkDuplicates . Problematic genomic regions present in the ENCODE Blacklist (10.1038/s41598-019-45839-z) were removed from the aligned files, and further QC of the aligned files was performed using samtools (v1.17)125. As many of the factors that were immunoprecipitated have a mix of both sharp and broad modalities, we used either HOMER (v4.11)126 or the deep learning-based peak caller LanceOtron (v1.0.8)127  to call peaks. Due to the limited material available, replicates were not performed and therefore an IDR-based method was not used. BigWigs were generated using the deepTools (v3.5.1) bamCoverage command128, with the flags –extendReads –normalizeUsing RPKM, and visualized in the UCSC genome browser129.Following QC of FASTQ files by FastQC  package131. Normalized counts were then Z-scored and clustered using the K-Means algorithm. Heatmaps were generated using ComplexHeatmap v3.17.For the comparative analysis of enhancer activity, read counts over the common MLL-AF4-bound enhancer set were extracted from H3K27ac TOPmentation or ATAC-seq BAM files for all samples using featureCounts v2.0.26 cells with the RNeasy Mini Kit (Qiagen). For qPCR, reverse transcription was conducted using Superscript III (ThermoFisher Scientific) with random hexamer primers (ThermoFisher Scientific), and cDNA was analyzed by Taqman qPCR, using the housekeeping gene YWHAZ for gene expression normalization.RNA was extracted from 1 × 10For patient RNA-seq, mRNA was isolated from bulk RNA using the NEBNext Poly(A) mRNA magnetic isolation module (mRNA) and used to generate a strand-specific library using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (NEB).76. Briefly, thiouridine-labeled spike-in RNA was generated by in vitro transcription of exogenous plasmid sequences in the presence of 4S-UTP (Jena Bioscience) using the MEGAscript kit (ThermoFisher Scientific). Then, 5 × 107 SEM or MM1.S cells were treated with 500 µM 4-thiouridine for 5 min before RNA was isolated by Trizol extraction (ThermoFisher Scientific) with the addition of 60 ng spike-in RNA, then purified and DNase I-treated. Labeled nascent RNA was fragmented briefly by sonication (Covaris), then biotinylated with EZ-link biotin-HPDP (ThermoFisher Scientific) and purified by Streptavidin bead pull-down (Miltenyi). Strand-specific libraries were prepared using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (NEB). Libraries were sequenced by paired-end sequencing with a 75 (patient samples) or 150 (TT-seq) cycle high-output Nextseq 500 kit (Illumina).TT-seq was conducted as previously described132 in paired-end mode using default parameters. Gene expression levels were quantified as read counts using the featureCounts function from the Subread package (v2.0.2)133 with default parameters. The read counts were used to identify differential gene expression between conditions using the DESeq2 (v3.12)134 package. For comparative expression analysis of MLLr vs MLLwt samples, featureCounts generated expression counts were normalized using the pyDEseq2 variance stabilizing transformation131 and normalized counts were compared between the enhancer-associated groups. For TT-seq, spike-in RNA levels were quantified by mapping to a custom genome using featureCounts and used to normalize the output of DESeq2. For metagene analysis, each transcript was scaled by binning the normalized coverage into 600 bins. Upstream and downstream flanking regions (2 kb) were split into 200 equal-length bins. Transcripts were split into quartiles based on length, and median coverage within each bin for each quartile was determined. The median of all replicates for all conditions was determined and log2 transformed after adding a pseudocount of 1.Reads were subjected to quality checking by fastQC (v0.12.1) and trimming using trim_galore (v0.6.10) to remove contaminating sequencing adapters, poor quality reads and reads shorter than 21 bp. Reads were then aligned to hg19 using STAR (v2.4.2a)GSE34861) cohort: 191 total samples comprising 78 BCR-ABL1 patients, 6 E2A-PBX1 patients, 25 MLLr patients : 17, other MLLr: 8), and 82 other B-ALL patients48; the Children’s Oncology Group (COG) Clinical Trial P9906 (GSE28460) cohort: 207 total samples, 23 E2A-PBX1 patients, 21 MLLr patients, 3 RUNX1-ETV6 patients, 155 other B-ALL patients (trisomy 4 or 10 patients)49; the St. Jude Research Hospital pediatric ALL clinical trial cohort 2003 (http://www.stjuderesearch.org/site/data/ALL3/): 118 total samples, 15 BCR-ABL1 patients, 18 E2A- PBX1 patients, 20 MLLr patients, 20 RUNX1-ETV6 patients, 17 hyperdiploid patients, 28 other B-ALL patients, 14 T-ALL patients50; and the St. Jude Research Hospital pediatric ALL clinical trial cohort 2013 (GSE26281): 127 total samples, 18 BCR-ABL1 patients, 8 E2A- PBX1 patients, 15 MLLr patients, 24 RUNX1-ETV6 patients, 26 hyperdiploid patients, 11 CRLF2 patients, 11 ERG patients, 14 other B-ALL patients, and 27 T-ALL patients51. Microarray data was normalized with RMA method using Expression ConsoleTM software  for the Affymetrix arrays HG-U133 plus2  or NimbleScan software  for the NimbleGen arrays HG18 60mer expression 385K platform . Downstream microarray analysis was performed using R version 2.14.0. Heatmaps were generated with Cluster/TreeView3.0 (http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm).Microarray gene expression data from four large cohorts of patients with ALL were analyzed. These cohorts included the Eastern Cooperative Oncology Group (ECOG) Clinical Trial E2993  were added using Herculase II (Agilent) for the final PCR. Indexing was performed in duplicate to maintain library complexity, with libraries pooled after indexing. Enrichment was performed using previously designed Capture-C probes , bead washes and PCR amplification using the HyperCapture Target Enrichment Kit (Roche). Samples were sequenced by paired-end sequencing with a 300-cycle high-output Nextseq 500 kit (Illumina). Data analysis was performed using CapCruncher v0.2.082 (10.5281/zenodo.6326102), and statistical analysis was performed as described28.Capture-C was conducted as described previously135. Briefly, 107 SEM cells were fixed with 2% formaldehyde for 10 min, then quenched with glycine and washed with PBS. Cells were permeabilized with 0.005% digitonin (Sigma Aldrich) for 15 min, then snap-frozen. Thawed cells were pelleted and resuspended in reduced-calcium MNase buffer , then split into three aliquots. Cells were treated with a titration of different micrococcal nuclease (NEB) concentrations  for 1 h at 37 °C in a Thermomixer (Eppendorf) at 550 rpm. The reaction was stopped with the addition of ethylene glycol-bis(2-aminoethylether)-N,N,N′,N′-tetraacetic acid (EGTA) to 5 mM and 200 µl was removed to assess the extent of digestion. The remaining cells were pelleted, washed with PBS/EGTA, then processed for end repair, phosphorylation and ligation. Cell pellets were resuspended in DNA ligase buffer, supplemented with 400 µM dNTPs and 5 mM EDTA, before addition of DNA Polymerase I large (Klenow) fragment (NEB) to 100 U/µl, T4 polynucleotide kinase (NEB) to 200 U/µl and T4 DNA ligase (Thermo Scientific) to 300 U/µl. The reaction was incubated for 2 h at 37 °C, then for 8 h at 20 °C, at 550 rpm, then cooled to 4 °C overnight. Both the digested and ligated chromatin were decrosslinked at 65 °C in the presence of proteinase K, then DNA was purified using the Qiagen DNeasy Blood and Tissue Kit. Digestion and ligation efficiencies were assessed by TapeStation (Agilent D1000). Library preparation, indexing and capture were performed as described for NG Capture-C. The probes used for capture are given in Supplementary Table 8 (https://github.com/jojdavies/Micro-Capture-C).Micro-Capture-C was performed as describedNuclear extracts from HEK-293 cells expressing a FLAG- and HA-tagged fragment of the MLL protein  were incubated with anti-FLAG M2 beads (Sigma Aldrich M8823) in 50 mM Tris-HCl pH 7.5, 300 mM KCl, and 20% glycerol. Beads were washed in 50 mM Tris-HCl pH 7.5, 200 mM KCl, 0.05% NP-40, and 20% glycerol, and then three times with the same again but with either 150 mM KCl (lanes 1 and 2) or 300 mM KCl (lanes 3 and 4). Bound proteins were eluted with FLAG peptide. Nuclear extracts from normal HEK-293 cells with no expression construct were used as a control. The eluted material was run on a 4–12% NuPAGE gel and stained with colloidal blue. Bands were separately cut out of the FLAG-C-PHD and control lanes and submitted for mass spectrometry.60. Spectra were searched using the Comet (version 2022.01 rev. 0)136. Searches were done with the Uniprot Human reference proteome (UP000005640) with reversed sequences appended as decoys 137. Each search was done using the following parameters: parent mass error ±3 Da; fragment bin ion tolerance ±1.0005; tryptic digestion with up to 1 missed cleavage; variable modification of +16 Da on methionine and a fixed modification of +57 Da on cysteine. Search results were then processed using the Trans-proteomic Pipeline (TPP) tools PeptideProphet and ProteinProphet140. These tools collapse proteins with shared peptide evidence into protein groups.The protein samples were processed and analyzed at the Mass Spectrometry Facility of the Department of Pathology at the University of Michigan. Each lane was cut into 12 equal slices, and in-gel digestion with trypsin followed by LC-MS/MS analysis was performed as described elsewhereNuclear extracts from HEK-293 cells expressing either FLAG-tagged CXXC or PHD constructs were incubated with anti-FLAG M2 beads (Sigma Aldrich M8823) in 50 mM Tris-HCl pH 7.5, 300 mM KCl, and 20% glycerol. Beads were washed in 50 mM Tris-HCl pH 7.5, 150 mM KCl, 0.05% NP-40, and 20% glycerol. Bound proteins were eluted with FLAG peptide. For GST pull-down assays, 500 ng of GST-fused proteins and 200 ng of purified factors were mixed with glutathione-sepharose 4B beads in binding buffer  and washed in the same buffer. Bound proteins were analyzed by western blotting . The antibodies used are detailed in Supplementary Table For western blotting, proteins were extracted from 1 × 10Tet Operator (TetO) mESC line66. The MLL CXXC domain, ENL, PAF1 and DOT1L cDNA sequences were cloned into the original pCAGFS2TetR vector downstream of the FS2-TetR open reading frame. Plasmids were transfected into mESC using Lipofectamine-2000, and puromycin (1 µg/ml) was used to select for clones stably expressing the TetR construct (confirmed by western blotting). As a negative control, cells were treated with 1 µg/ml doxycycline for 6 h to disrupt TetR binding at the TetO prior to fixation for ChIP.For the TetR recruitment assay, we used the previously engineered PAF1 or SSRP1 gene to FKBP12F36V-P2A-mNeonGreen, immediately prior to the stop codon. This was achieved by CRISPR/Cas9-mediated homology-directed repair (HDR), as previously described119. Cells were electroporated with two plasmids: pX458, encoding Cas9, an sgRNA sequence targeting the end of PAF1 or SSRP1 . Cell lines are available from the corresponding authors upon completion of a Materials Transfer Agreement.SEM or MM1.S cell lines were edited to fuse both alleles of the Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting SummarySource Data


Mecp2 mice. Genes are dysregulated by changing transcription rate or half-life and are buffered when both change. We utilized classifier models to predict the direction of transcription rate changes and find that combined frequencies of three dinucleotides are better predictors than CA and CG. MicroRNA and RNA-binding Protein (RBP) motifs are enriched in 3ʹUTRs of genes with half-life changes. Nuclear RBP motifs are enriched on buffered genes with increased transcription rate. We identify post-transcriptional mechanisms in humans and mice that alter half-life or buffer transcription rate changes when a transcriptional modulator gene is mutated in a neurodevelopmental disorder.Transcriptional changes in Rett syndrome (RTT) are assumed to directly correlate with steady-state mRNA levels, but limited evidence in mice suggests that changes in transcription can be compensated by post-transcriptional regulation. We measure transcription rate and mRNA half-life changes in RTT patient neurons using RATEseq, and re-interpret nuclear and whole-cell RNAseq from  Rett syndrome (RTT) is a neurodevelopmental disorder caused by mutations in the transcriptional modulator MECP2. Here, the authors measured transcription rate and mRNA half-life changes in RTT patient-derived neurons to show transcription rate buffered by mRNA half-life changes. MECP2)1. Most evidence indicates that MECP2 regulates transcription globally after binding to methylated (m)CG dinucleotides in immature neurons and mCA dinucleotides in adult neurons8. Specifically, recent models support a role for MECP2 binding to mCA/mCGs in the gene-body that can: 1) slow RNA polymerase II elongation5 2) inhibit transcription initiation by looping interactions between the promoter and high-density MECP2-bound gene-bodies9 or 3) inhibit transcription initiation by microsatellite interruptions of nucleosome binding that impact intragenic enhancer activation10.Rett syndrome (RTT) is a neurodevelopmental disorder in girls caused by damaging mutations in the methyl CpG-binding protein 2 . In particular, they examined the enrichment of 12 RBP binding sites in subsets of mRNAs and identified HuR-(ELAVL1) cis-acting elements in the 3ʹUTR with extended mRNA stability, or AGO2 cis-acting elements with reduced stability to implicate the action of unknown micro(mi)RNAs17. Their model of post-transcriptional regulation is similar to transcription buffering where RBPs present in the nucleus tag nascent mRNAs and shuttle with them to the cytoplasm. The RBPs then modify half-life to buffer transcription rate changes that preserve steady-state levels . These results have not been independently tested in mouse and it is unknown whether the mechanism is conserved in human RTT neurons. Therefore, it is important to experimentally measure the direction and magnitude of half-life changes in human MECP2-null neurons. Moreover, the post-transcriptional mechanism has not been studied by systematic enrichment analysis of all known miRNA and RBP cis-acting elements of the post-transcriptionally regulated mRNAs.Genome-wide analyses of steady-state mRNA levels and transcription rate changes in RTT models have demonstrated a global dysregulation of gene expressionMecp2 mutant mouse brains9. We find that transcription rate changes in both human and mouse datasets are best predicted by combinations of three dinucleotide frequencies in gene-bodies that include the expected CA/CG motifs, but are most accurate if they also include other dinucleotides. We discover extensive half-life changes that identify: 1) a gene set with exclusive mRNA stability dysregulation  and no associated transcription rate changes; and 2) a larger buffered gene set in which half-life regulation compensates for transcription rate changes that fully offset or minimize mRNA steady-state changes. We demonstrate a global absolute downregulation of miRNA levels, that corresponds with a global absolute half-life increase in RTT neurons. We find individual enriched miRNA binding-sites in the half-life only gene set but very few in the buffered gene set. RBP-binding sites were enriched in the 3ʹUTRs of half-life only genes, and distinct sites were also enriched in buffered genes with increased transcription rate. Overall, we propose that transcription rate increases in MECP2 neurons are subject to surveillance by RBPs that post-transcriptionally regulate RNA half-life. We find that the buffering of transcription rate changes by half-life changes is a conserved feature of RTT models which minimize the steady-state changes in mRNA levels.Here, we simultaneously investigate the potential role of sequence features mediating transcriptional dysregulation in RTT and expand on the post-transcriptional findings of Johnson et al. in human isogenic induced Pluripotent Stem Cell (iPSC)-derived RTT neurons. We use RNA-approach to equilibrium-sequencing (RATEseq) to measure transcription rate and half-life changes and employed machine learning to uncover sequence features underlying these changes in human and mouse RTT models. In parallel, we compare our human neuron findings to the high-confidence RNA-seq from subcellular fractionations of WT) and MECP2-Null (NEURTT) isogenic patient-derived iPSCs21 . Cellular viability was consistently high as expected24  by calculating the number of newly synthesized mRNAs over 30 mins. Importantly, the measurement of transcription rate fold-changes between NEUWT and NEURTT were highly comparable using only the 0.5-hour replicates or the 0.5 plus 1-hour time-point pseudoreplicates   or 957 (0.5 h time-point only) genes  with significantly increased or decreased mRNA half-life but with unchanged transcription rate, termed half-life only (HL-only), leading directly to changes in the steady-state levels  Figs. . These mT) Figs. . Other ols Figs. . The claRTT were observed when calculations used the 0.5 h time point only on genes whose transcription rates were altered by at least 4-fold  with altered steady-state expression to be exclusively dysregulated at the transcription rate level . By applying the proxy value in the ratio method, we calculated relative half-life fold-changes. From the 5032 genes reported by Boxer et al. to be differentially regulated in Mecp2 y/- mice, we found a similar pattern of widespread fold-changes in half-life  with transcription rate only dysregulation as measured in the nuclear fraction. Altogether, our findings demonstrate a pattern of half-life shifts and transcription buffering that is conserved in RTT mouse and human models.Despite the conserved relationship that half-life and transcription rate or their proxies have on steady-state gene expression in human and mouse RTT models, we found minimal overlap in the identities of genes dysregulated in each species Fig. . Furtheral. Fig. . Overallcis-acting elements in the 3ʹUTR between buffered and non-buffered genes. Initially we investigated the contribution of mRNA isoforms by mapping and quantification of 3ʹUTR alternative poly-Adenylation events that showed no difference in the frequency of poly-Adenylation site usage in NEURTT  and alternatively-spliced mRNA isoforms  indicated that all mRNA isoforms display the half-life buffering effect to the same degree  alternative polyadenylation; 2) alternative-splicing; 3) codon usage integrating translation elongation to RNA stability; 4) sequence composition of 3ʹUTR and gene-bodies; and 5) enrichment of miRNA binding-sites and RBP RTT Fig. . Moreoveree Fig. . These aRTT. Dinucleotide frequencies in the 3ʹUTR offered significant prediction accuracy on whether the half-life was increased or decreased. Increasing the size of the tested k-mers from dinucleotides to 4-mers and 6-mers to encompass potential cis-acting elements improved the prediction accuracy of half-life  splicing factors that have been shown to participate in transcriptional buffering36 were enriched in >150 genes. Further support for a directional role by NOVA2 and SRSFs is that they were depleted in the buffered genes with decreased transcription rate and increased half-life  and the Boxer et al. (mouse) datasets originate from the analysis of different cell types (in vitro-derived human neurons vs. mouse whole brains), or from the maturation stage differences between the studies. A high degree of variability between the dysregulated genes in the RTT mouse models has also been observed previouslycis-acting elements that are relevant for transcription rate or half-life regulation. At the DNA level, our classifier model discovered that the direction of transcription rate shifts in human and mouse RTT models are best predicted by combinations of three dinucleotides that include one or both of the canonical MECP2-binding sites CA and CG, together with other dinucleotides such as AT. The MECP2 AT-hook domain contributes to low-affinity transient interactions with AT-rich DNA that influence the dynamics of MECP2 binding to local high affinity methylated DNA sites39. Tellingly, the classifier predictions are low when modelling only CA/CG dinucleotide frequencies without accounting for low-affinity sites. These unbiased findings from machine learning algorithms indicate that the gene-body frequencies of other dinucleotides like AT are important and are a conserved mechanism defining the direction of transcription rate changes in RTT neuronal models. We speculate they may act by transient recruitment of MECP2 influencing its local binding dynamics to nearby methylated DNA. We confirmed a role of gene-body length in transcription rate regulation but only in the adult mouse neuron resource of Boxer et al. However, neither our classifier model on the human neuron RATEseq dataset nor the adult mouse dataset from Johnson et al. support this observation. It is possible that gene-body length contributes less to transcription rate regulation in fetal stage neurons derived from iPSC in which only mCG modifications are expected to be present40, or that it requires the power of ten replicate samples used in the Boxer et al. resource to be detected.Our computational methods were focused on defining RTT. These results confirm the reported miRNA changes in RTT mouse models, although the RATEseq dataset shows that intergenic pri-miRNA transcription rate is altered in RTT adding another dimension to the known miRNA processing alterations in mouse30 and human neurons31. Through the use of spike-in scaling in the miRNA dataset, we deduced a global absolute downregulation of miRNAs in NEURTT that account for the global absolute increase in mRNA half-life of ~0.5 hours. Superimposed on the increased global half-life effect were individual HL-only genes which our classifier models revealed strong enrichment of miRNA-binding sites consistent with their known role in mRNA instability. Many RBP cis-acting elements including ELAVL1 (HuR) were enriched in the HL-only gene set. While Johnson et al. reported HuR and AGO2 cis-acting element enrichments in buffered gene sets in the mouse using the subcellular fractionation approach, our RATEseq results and unbiased search of RBP cis-acting elements point to a role for miRNAs and ELAVL1 in mRNA stability in human neurons rather than in the buffering mechanism itself.With regards to half-life only regulation, to define which miRNA-binding sites to investigate we first used our human neuron miRNA dataset to identify the miRNAs that are altered in NEU41. We speculate that these mechanisms could also participate in the buffering mechanisms, particularly in the gene set with decreased transcription rate and increased half-life that we found was not associated with enrichment of either miRNA-binding sites or RBP cis-acting elements. The simplest mechanism for buffered genes with increased transcription rate is that the RBPs bind nascent mRNA in the nucleus and are transported to the cytoplasm where they tag the transcript for stabilization. We speculate that the nuclear RBPs are limiting in neurons, and if transcription rate increases then the proportion of tagged mRNA falls, leading to relatively more degradation in the cytoplasm and decreased half-life. A more complex variation on this model is that the concentration of some RBPs themselves may also change in RTT, and this may increase or decrease their ability to stabilize their target mRNAs. To distinguish these models, it will be necessary to determine which RBPs are changed at the protein level in RTT using proteomics of neuronal nuclei and then individually testing their impact through gain- or loss-of-function assays on the mRNA targets.While we eliminated several possible buffering mechanisms, one limitation is that we were unable to test a potential role of mRNA methylation modifications or poly(A)-tail length regulation on the targeted mRNAs with altered transcription rateNOVA235. The impact of these RBPs on buffering could be established using existing or new iPSC or mouse models. In fact, global transcription rate and RBP concentrations will inevitably be altered during the course of neurodevelopment, suggesting that it would be valuable to define the buffered gene sets in iPSC and their progeny Neural Progenitor Cells (NPC) relative to the final neurons described here. We and others23 have previously reported translational regulation changes in RTT neurons in both ribosomal loading and protein stability implemented through alterations of E3-ubiquitin ligase protein levels. These findings emphasize that buffering in RTT and potentially other disorders likely operates at both the mRNA and protein levels.Equivalent loss-of-function experiments have already been described in humans with neurodevelopmental disorders caused by mutations in RBP genes such as MECP2-null) from a female patient were previously described21. Both cell lines were generated and cultured under the approval of the SickKids Research Ethics Board and the Canadian Institutes of Health Research Stem Cell Oversight Committee. iPSC lines were cultured in 5% CO2 on BD hESC-qualified matrigel (BD) in mTeSR medium (STEMCELL Technologies). Cultures were passaged using ReLeSR (STEMCELL Technologies) following the manufacture’s instruction every 6-7 days. For neuronal induction, iPSCs were aggregated as Embryoid Bodies (EBs) in low-attachment dishes in N2 media containing laminin (1 ml/ml) with 10 mM SB431542, 2 mM DSM, and 1x penicillin-streptomycin changed daily. After 7 days, EBs were plated on poly-L-ornithine + laminin-coated dishes and grown in N2 media + laminin (1 ml/ml). After 7 days, neural rosettes were manually picked and transferred to poly-L-ornithine + laminin-coated wells. After 7 days, neural rosettes were picked a second time, digested with Accutase and plated on poly-L-ornithine + laminin-coated wells. Resulting NPCs were grown as a monolayer and split every 5-7 days in NPC media containing DMEM/F12, N2, B27, 1x non-essential amino acid (NEAA), 2 mg/ml Heparin, 1 mg/ml laminin. To generate neurons, NPCs were plated on poly-L-ornithine + laminin-coated plates at a density of 106 cells per 10 cm dish and cultured for 3 weeks in neural differentiation medium .iPSC lines #37 (WT) and #20  and CD184  recognizing NPCs, glial progenitors and astrocytes43 using magnetic-activated cell sorting (MACS® - Miltenyi Biotec). After enrichment, neurons were re-seeded onto Matrigel-coated 6-well plates, cultured in neural differentiation medium, and allowed to recover for one extra week, for a total of 4 weeks neuronal differentiation.Neuronal cultures were enriched for all experiments to exclude contaminating glia and NPCs present after differentiation. Enrichment of 3-week old neuronal cultures was made as described earlierCells were washed in ice-cold PBS and total protein extracted in radioimmune precipitation assay (RIPA) buffer . Equivalent protein mass was loaded on SDS-PAGE and transferred to Hybond ECL  nitrocellulose membrane. Antibodies MECP2 , and Beta Actin  were used. Near-Infra Red-conjugated secondary antibodies  were used and membranes scanned using LI-COR Odyssey CLx scanner according to manufacturer’s instructions. Acquired images were analyzed using ImageStudio v5.2.5.MECP2-null line where the time-point 1 h was omitted from both replicates due to low differentiation yields). Metabolic labeling was designed such that all time points were collected together. After incorporation, cells were quickly washed twice with ice-cold 1× PBS Total RNA and scraped into ice-cold 1.5 ml Eppendorf tubes. Cells were collected by spinning at 1000 g for 5 min at 4 °C and cell pellets were resuspended in 1 mL of Trizol (Thermo Fisher Scientific). Total RNA was extracted according to manufacturer instructions. The steady-state sample was prepared from a 5 μg aliquot of the 24 h time-point added with 0.5 μg of both 4sU labeled and unlabeled spike-in RNAs. Neuronal viability in the presence of 100 μM 4sU was monitored up to 24 h of treatment on parallel cultures by using Trypan blue staining and live/dead cell counting.When enriched neuronal cultures reached 4-week of differentiation, media was replaced with neuronal differentiation media supplemented with 100 μM 4sU (Sigma-Aldrich) reconstituted in DMSO. Neurons were harvested at 0.5, 1, 4, 8, and 24 h after the addition of 4sU  was reconstituted in dimethylformamide by shaking at 37 °C for 30 min at 300 RPM. 120 µL of 2.5× citrate buffer  and 60 µL of 1 mg/mL HPDP-biotin were added to the premixed RNA sample for each time point. The solution was incubated at 37 °C for 2 h at 300 RPM on an Eppendorf ThermoMixer F1.5 in the dark. Samples were extracted twice with acid phenol, pH 4.5, and once with chloroform. RNA was precipitated with 18 µL 5 M NaCl, 750 µL 100% ethanol, and 2 µL GlycoBlue (Invitrogen) overnight at −20 °C. Precipitated RNA was pelleted for 30 min at 21,000 g at 4 °C. The RNA pellet was resuspended in 200 µL of 1× wash buffer . Biotinylated RNA was purified using the µMACS Streptavidin microbeads system (Miltenyi Biotec). 50 µL Miltenyi beads per sample were pre-blocked with 48 µL 1× wash buffer and 2 µL yeast tRNA (Invitrogen), rotating for 20 min at room temperature. µMACS microcolumns were washed 1× with 100 µL nucleic acid equilibration buffer (Miltenyi Biotec), followed by 5× washes with 100 µL 1× wash buffer. Beads were applied to microcolumns in 100 µL aliquots and again washed 5× with 100 µL 1× wash buffer. Beads were demagnetized and eluted off the column with 2× 100 µL 1× wash buffer, and columns were placed back on the magnetic stand. A total of 200 µL beads was mixed with each sample of biotinylated RNA and rotated at room temperature for 20 min. Samples were applied to the microcolumns in 100 µL aliquots, washed 3× with 400 µL wash A buffer  prewarmed to 65 °C, and washed 3× with 400 µL wash B buffer . RNA was eluted with 5× 100 µL of 1× wash buffer supplemented with 0.1 M DTT, and flow-through was collected in a tube. Purified RNA was precipitated with 30 µL 5 M NaCl, 2 µL GlycoBlue, and 1 mL 100% ethanol, incubated at −20 °C overnight. Samples were spun at 21,000 g at 4 °C for 30 min and resuspended in 20 µL water. RNA quality was assessed by running 3 µL of samples on a 1.5% agarose gel.50 µg of total neuronal RNA was mixed with 5 µg unlabeled WT and NEURTT were incubated with 0.5 mM 5-EU (ThermoFisher) for 30 min. Total RNA was extracted and processed using Click-iT Nascent RNA Capture Kit (ThermoFisher) according to the manufacturer’s instructions. The captured RNAs were used as a template for cDNA synthesis, followed by qRT-PCR to quantify mRNA level . Genes were chosen to cover a wide range of transcription rate changes determined by RATE-seq.Transcription rate measurements were validated by an alternative method using the metabolic incorporation of 5-ethynyl uridine (5-EU) followed by quantifying mRNA levels by qRT-PCR. NEUWT and NEURTT. RNAs were isolated at 1 h, 3 h, and 6 h time points using the RNeasy Plus kit (QIAGEN). The RNAs were used as a template for cDNA synthesis followed by qRT-PCR to quantify mRNA levels . Genes were chosen to cover a wide range of half-life changes as determined by RATE-seq. Fold-changes for all time points were calculated relative to the 0-hour time point. Then, data was fit with lm function from R  ~ Time + 0). The coefficient of “Time” term measures degradation rate confint function (stats package).Half-life measurements were validated by an alternative method using transcription inhibition followed by quantifying mRNA levels by qRT-PCR. 10 μg/mL actinomycin D (Sigma-Aldrich) was added to NEUcDNAs were synthesized using SuperScript III reverse transcriptase (ThermoFisher) with random hexamer primers according to the manufacturer’s instructions. For qRT-PCR, we used SYBR Select PCR Master Mix (ThermoFisher). Fold-changes were calculated by the ΔΔCt methods using Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and 18 S as housekeeping genes, averaged between technical and subsequently biological replicates to achieve an average fold difference .WT and NEURTT, small RNAs were extracted from two replicates of both lines using the same number of cells followed by the addition of a set of spike-in RNAs. Small RNAs were extracted from 500,000 neurons of each line using the SPLIT RNA extraction Kit (Lexogen) according to the manufacturer’s instructions. A set of 52 RNA spike-ins (QIAseq miRNA Library QC Spike-Ins – Qiagen) that spanned a wide range of concentrations were added to the recovered RNAs according to the manufacturer’s instructions. Sequencing libraries were made using the Small RNA library preparation kit NEBNext (NEB) according to the manufacturer’s instructions. Sequencing was performed on the Illumina HiSeq 2500 using the Rapid Run mode. Datasets can be accessed from GEO using the access number GSE191168.To calculate relative and absolute differences in the miRNA population in NEUGSE191168.RNA-seq libraries were prepared for each time-point and steady-state sample using the QuantSeq 3’ mRNA-Seq Library Prep Kit FWD for Illumina (Lexogen) automated on the NGS WorkStation (Agilent) at The Centre for Applied Genomics (TCAG) according to the manufacturer’s instructions. PCR cycle numbers were determined using the PCR Add-on Kit for Illumina (Lexogen). All steady-state samples were processed with 250 ng of total RNA input. To minimize variability between time-points within a batch, RNA samples were processed with the same total RNA input with a minimum of 100 ng of total RNA used. Each sample was spiked-in with ERCC RNA Spike-In Control Mix 1 (Ambion) according to the manufacturer’s instructions prior to the start of library preparation. Library quality and quantity were measured at TCAG prior to sequencing with Bioanalyzer (Agilent) and KAPA qPCR (Roche). Sequencing was also performed at TCAG on the Illumina HiSeq 2500 with single-end 100 bp read length yielding 40 to 50 million reads. Datasets can be accessed from GEO using the access number 44. First, we removed adapters exactly at the 3‘-end of the reads (-a AGATCGGAAGAGCACACGTCTGAACTCCAGTCAX -O 4 -e 0.1 --minimum-length 25). Second, we removed internal or long stretches of adapter (-a AGATCGGAAGAGCACACGTCTGAACTCCAGTCA -O 30 -e 0.18 --minimum-length 25). Third, we trimmed low-quality bases at the 3‘-end of the reads (-q 20 -O 4). Finally, we removed poly-A tail at the 3‘-end of the reads (-a AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAX -O 4 -e 0 –minimum-length 25).Processing starts with trimming of reads in 4 steps using cutadapt version 1.1045.We generated a custom genome index to accommodate the quantification of yeast, fly, and ERCC spike-in RNA. Annotations  and genomes  for all species and ERCC were combined and then processed with STAR version 2.6.0c (--sjdbOverhang 100). Finally, reads are aligned to hybrid genome with STAR version 2.6.0c (default settings)liftOver (UCSC)47. Reads with MAPQ < 2 are filtered out. Finally, usage of poly-A sites was defined as a sum of reads whose 3‘-ends are falling within 20 bp upstream and 10 bp downstream of the poly-A sites. The sum was counted with a custom Python script using pybedtools, pysam, pypiper50. Annotation of pri-miRNA transcripts structures was downloaded from Mendel lab51. Each transcript was matched to miRNA gene based on overlaps with GENCODE annotated pre-miRNA coordinates52. Then, pri-miRNA poly-A sites overlapping mRNA or lncRNA poly-A sites from PolyA_DB were removed. Finally, usage of pri-miRNA poly-A sites was quantified with featureCounts 53. The abundance of mature miRNAs was quantified with mirdeep2 pipeline54. Reads were preprocessed and collapsed with mapper.pl script (-e -h -j -k AGATCGGAAGAGCACA -l 18 -m -v) and quantified with quantifier.pl script, using hairpin and mature sequences obtained from miRbase55.Poly-A sites were obtained from PolyA_DB version 3 and converted to hg38 coordinates with 56.Counts of each replicate are normalized relative to the median of the sample and log-transformed. Transformed values are visualized for the NPC and Glia markers in addition to a list of neuron-specific genes obtained from Zaslavsky et al. First, reads are assigned as originating from either human, fly, yeast or ERCC, based on alignment to hybrid genome. Then, human raw counts are divided by the sum of all fly spike-in raw counts. Since both human and fly RNAs are 4sU-labeled, this normalization to fly spike-ins reconstructs the fraction of 4sU-labeled human RNA at each time point.Fly spike-ins were used to normalize human counts for all time points (0.5-hour to 24-hour) to assist with transcription rate and absolute half-life calculations. Yeast spike-ins are only used as quality control for contamination in the pull-downs as presented in Fig. SSS be human normalized counts at steady-state, Let Y(t) be human normalized counts at time t, YThen, 4sU incorporation kinetics is a solution to above equation, when Y(t)=0 at time t = 0:In general, cell division dilutes 4sU labeled transcripts. This effect can be accounted for by using Note that 2FC estimated with DESeq2. To compare the transcription rate between cell types using DESeq2, human normalized counts are further quantile normalized between replicates of the same cell type with normalize.quantiles (preprocessCore package)58. Half-life was estimated in 2 separate ways: fit of the 4sU saturation curve and the ratio of steady-state to transcription rate. For the 4sU saturation curve method, the half-life is estimated in a 2-step procedure. First, normalized counts are fit with nls (nonlinear least squares from stats package) to approximate the true number of counts Y at each timepoint. Then, in a second pass, normalized counts are fit again with nls, but now correcting for the increase in variance using weights set as 1/Y. Confidence intervals are estimated with confint function (stats package). For the ratio method, the half-life is estimated with DESeq2 using raw human counts from 0.5-hour, 1-hour and steady-state samples (design = ~ assay). The assay is a 2-level factor one for transcription rate and one for steady-state. 0.5-hour and 1-hour samples correspond to the transcription rate.The transcription rate was estimated from 0.5-hour and 1-hour time points for WT neurons and only from 0.5-hour time point for RTT neurons. This estimate assumes that RNA degradation is negligible for most genes at time points before 1 hour . Assay factor encodes whole-cell vs nuclear or chromatin samples. Coefficient of the celltype:assay term is used to measure the log2 fold-change in half-life between cell types. Before the DESeq2 run, we filter out genes with a sum of counts in replicates less than 20 in a pair of compared cell types.Mouse data for whole-cell, nuclear and chromatin RNA-seq was downloaded from GSE128178. Mouse Fold-changes in human half-life in log scale DESeq2 as a ratio from steady-state and transcription rate replicates. Z-score of a fold-change There, oligonucleotideFrequency (Biostrings package)60. In addition, the methylation status of CA and CG in the gene-body was added for mouse analysis from GSE139509. Predicted variable denotes genes that are either up or down-regulated in transcription rate or half-life. Thresholds for the human data were:up: TRdown: TRup: HLdown: HLFeatures of the classifier are frequencies of k-mers in 3ʹUTR, coding sequence or gene-body, calculated using up: TRdown: TRnot: TRup: HLdown: HLThe half-life for the mouse was either from nuclear or chromatin. Thresholds for the mouse data were:randomForest (randomForest package)61. Precision-recall and receiver operator characteristic curves were obtained with evalmod (precrec package)62.Data was split into 75% and 25% for training and test sets. The classifier is trained using run_kmer_tsma (transite package)63. Groups of compared genes:down and HLup Background: TRdownForeground: TRup and HLdown Background: TRupForeground: TRnot and HLdown Background: TRnotForeground: TRGenes were split into up- or down-regulated according to their transcription rate and half-life fold-change. Then, we performed multiple comparisons of 3ʹUTR sequences between groups of genes using 32. TargetScan analysis includes 439 human miRNA-seed sequences with family conservation scores of 0,1,2 that were selected from miR_Family_Info.txt (TargetScan website). Definitions of up- and down-regulated genes:up: TRdown: TRnot: TRup: HLdown: HLThese comparisons were performed for all transite RNA binding protein motifs and for TargetScan seed sequencestransite analysis were further processed with a custom script for multiple hypothesis correction. Motifs with a low number of sites detected in both background and foreground were removed from the analysis. A separate threshold for the number of sites was chosen for each transite analysis. A threshold was determined from a requirement for p-values distribution to be unimodal and enriching at p = 0. The distribution of p-values with unfiltered sites is bimodal with peaks at both p = 0 and p = 1.The results of Further information on research design is available in the Supplmentary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6fSupplementary Data 7Reporting Summary


Mycobacterium tuberculosis (Mtb) employ transcription factors to adapt their physiology to the diverse environments within their host. CarD is a conserved bacterial transcription factor that is essential for viability in Mtb. Unlike classical transcription factors that recognize promoters by binding to specific DNA sequence motifs, CarD binds directly to the RNA polymerase to stabilize the open complex intermediate (RPo) during transcription initiation. We previously showed using RNA-sequencing that CarD is capable of both activating and repressing transcription in vivo. However, it is unknown how CarD achieves promoter-specific regulatory outcomes in Mtb despite binding indiscriminate of DNA sequence. We propose a model where CarD’s regulatory outcome depends on the promoter’s basal RPo stability and test this model using in vitro transcription from a panel of promoters with varying levels of RPo stability. We show that CarD directly activates full-length transcript production from the Mtb ribosomal RNA promoter rrnAP3 (AP3) and that the degree of transcription activation by CarD is negatively correlated with RPo stability. Using targeted mutations in the extended −10 and discriminator region of AP3, we show that CarD directly represses transcription from promoters that form relatively stable RPo. DNA supercoiling also influenced RPo stability and affected the direction of CarD regulation, indicating that the outcome of CarD activity can be regulated by factors beyond promoter sequence. Our results provide experimental evidence for how RNA polymerase–binding transcription factors like CarD can exert specific regulatory outcomes based on the kinetic properties of a promoter.Bacterial pathogens like  Mycobacterium tuberculosis (Mtb), which face threats from both the host immune system and antibiotic treatment. Regulation of transcription initiation is a major mechanism by which bacteria adapt their gene expression in response to environmental stimuli. Transcription in bacteria is performed by a single RNA polymerase (RNAP) enzyme, which consists of a multisubunit core enzyme that can bind to different sigma factors (σ) to form a holoenzyme and initiate promoter-specific transcription. Mtb devotes a significant fraction of its genome toward encoding numerous transcription factors that can regulate transcription initiation by altering the promoter specificity and recruitment of RNAP  formed by the mycobacterial RNAP during transcription initiation (c) to increase the rate of DNA melting while also slowing the rate of bubble collapse   , 20, chrl genome , leavingMtb strains expressing mutants of CarD that either impair or enhance its ability to stabilize RPoin vitro  on a set of in vitro . We discposition  and reprposition . Howeverromoters , making promoter . SpecifiM. smegmatis, where CarD is present at almost all RNAP-σA transcription initiation complexes , predicted weakened affinity for DNA (CarDK125A), or increased affinity for RNAP (CarDI27F and CarDI27W). By collecting RNA from Mtb strains with mutations that target different domains of CarD, we were able to dissect how the respective interactions of CarD’s functional domains contributed to its role in regulating the Mtb transcriptome. To replicate this experimental design in M. smegmatis, we collected RNA from four strains of M. smegmatis with the native copy of carD deleted and expressing one of four different alleles of Mtb CarD: wildtype (WT) CarD (CarDWT), CarDR25E (a RID mutant with weakened affinity for RNAP), CarDK125E (a DBD mutant with weakened affinity for DNA), or CarDI27W (a RID mutant with increased affinity to RNAP) as the only carD allele  impair CarD’s ability to stabilize RPoin vitro (o stabilization at lower concentrations (R25E-1 and CarDK125E-4) were identified as outliers following principal component analysis (PCA) and were discarded from downstream analysis  in comparison to the CarDWT strain  and CarDK125E (2901 genes) M. smegmatis strains is similar to the number of differentially expressed genes in the CarDR47E (2877 genes) and CarDK125A (2690 genes) Mtb strains , following the same pattern as the Mtb CarD mutant strains , suggesting that the transcript abundance differences measured in the CarD mutant strains represent local changes in transcription at specific genes rather than a global decrease in RNA production within the cell that would be expected if CarD functioned strictly as a transcriptional activator.In all three strains with mutations in arDR47E 277 genes  strains , suggestarDR47E 277 genes M. smegmatis was also consistent with the relationships we observed in our Mtb dataset (M. smegmatis sample replicates clustered tightly with each other based on CarD genotype and samples from CarD mutant strains with impaired RPo-stabilizing activity (CarDR25E and CarDK125E) separated from the strain with enhanced RPo-stabilizing activity (CarDI27W) along the first principal component (C), demonstrating consistency between replicates from the same genotype and suggesting that altered CarD RPo-stabilizing activity contributes to transcript abundance changes in the mutant bacteria. In addition, the CarDR25E and CarDI27W strains, which encode CarD RID mutants with impaired or enhanced RPo-stabilization in vitro, respectively, displayed largely opposite transcriptomic changes (A), similar to the RID mutants in Mtb  and the direction of transcript abundance changes in the DBD mutant CarDK125E samples correlated poorly with the transcript abundance changes in the RID mutant CarDR25E samples (R2 = 0.351) (B and C). This is in contrast to the tight correlation between CarD RID and DBD mutants in Mtb  , B and Cs in Mtb  and may A is also found, supporting a model that CarD is present at the promoters of both upregulated and downregulated genes. However, our data are also compatible with an alternative model in which CarD acts directly as a monotonic transcriptional activator, and genes that appear to be transcriptionally “repressed” by CarD are expressed at lower levels in WT bacteria due to decreased RNAP occupancy at non-CarD-activated promoters. If this alternative model were true, then we would expect to find RNAP-σA/CarD binding sites overlapping with transcription start sites (TSSs) ascribed to the transcriptionally “activated” promoters but absent from TSSs ascribed to transcriptionally “repressed” promoters. To examine the overlap between CarD binding sites and CarD-regulated transcripts, we used our RNA-seq dataset to identify a list of M. smegmatis genes whose transcript abundance was likely directly responsive to altered CarD-mediated RPo stabilization activity based on having opposite expression patterns in CarDR25Eversus CarDI27W. To avoid internal genes within operons, we focused our analysis on 2917 M. smegmatis genes directly downstream of a primary TSS (Padj < 0.05) in CarDR25E and significantly upregulated in CarDI27W were classified as ‘Activated’ by CarD (n = 117), while TSSs associated with genes that were significantly upregulated in CarDR25E and significantly downregulated in CarDI27W were classified as ‘Repressed’ by CarD (n = 153). TSSs associated with genes that were significantly differentially expressed in both CarDR25E and CarDI27W but in the same direction relative to wildtype were classified as ‘Uncategorized’ (n = 222) because their expression profile does not reflect the divergent expression pattern expected between CarD mutants with opposing effects on RPo-stabilization in vitro. Lastly, any TSSs that were not significantly differentially expressed in both CarDR25E and CarDI27W were categorized as ‘Not Significant’ (n = 2425).The ChIP-seq dataset shows that CarD is present when RNAP-σmary TSS  and catemary TSS . TSSs asM. smegmatis and 1129 sites (62.8%) overlapped with a primary TSS associated with a protein-encoding gene (R25E and CarDI27W and found that 57.9% (285/492) of these TSSs were associated with CarD binding   driving transcription of a 164 nucleotide RNA product. The addition of a saturating concentration of WT CarD (25:1 M ratio CarD:RNAP (A). To investigate how CarD’s RPo-stabilizing activity relates to transcriptional activation, we repeated the multiround in vitro transcription assays with CarD mutants impaired in their ability to stabilize RPoin vitro  (A), suggesting that CarD’s RPo-stabilizing activity underlies its ability to activate transcription from AP3. In addition, the degree to which each CarD mutant attenuated transcript production correlated with how severe the impact was on the CarD macromolecular interactions with RNAP  (B), further demonstrating the association between CarD's RPo-stabilizing activity and activation of transcript production. Collectively, these results demonstrate that CarD activates full-length RNA production in vitro from AP3 and this transcription activation is dependent on the RPo-stabilizing activity of CarD.To test our model that the outcome of CarD’s RPn assays , 20 and n assays , 15 thatarD:RNAP A. To invrDK125E) , 15. AllrDK125E) A, suggesith RNAP  or DNA (ith RNAP . In contrD:RNAP) B, furtheo stability of a promoter, we explored CarD’s direct regulatory effect on transcription from a set of promoters with varying levels of basal RPo stability. Transcription initiation kinetics and RPo lifetime are highly dependent on promoter DNA sequence  promoter form a relatively unstable RPo  with higher levels of basal RPo stability by making targeted sequence mutations that would add or optimize predicted DNA–RNAP interactions in RPo (A). AP3WT contains near consensus sequence motifs in the −35 and −10 elements . The most stable variant AP3Stable displayed 8-fold higher basal RPo stability relative to AP3WT. To quantify the effect of CarD on RPo stability from these promoter variants, we also performed three-nucleotide transcription assays in the presence of WT CarD protein (B). On AP3WT, CarD increased the amount of three nucleotide product by roughly 4-fold over reactions with no factor. As the basal RPo stability of promoter variants increased, the degree of RPo stabilization by CarD decreased to the point that on AP3Stable, the addition of CarD resulted in no detectable difference in the amount of three nucleotide product.To measure the basal RPn assays , 20 in tto AP3WT B. The mo protein B. On AP3o stability levels that range over nearly one order of magnitude, we performed multiround in vitro transcription reactions using these AP3 promoter variants in the presence or absence of CarD to investigate the relationship between the basal RPo stability of a promoter and transcriptional regulation by CarD (C). We discovered that across the AP3 promoter variants, basal RPo stability positively correlated with full-length transcript production in the absence of CarD but negatively correlated with transcriptional activation by CarD. Indeed, the two promoters with the highest levels of basal RPo stability (AP3EcoExt and AP3Stable) were transcriptionally repressed by CarD, consistent with the predictions of our model and providing the first in vitro evidence of direct transcription repression by Mtb CarD.Having established a set of promoters with different basal RP by CarD C. We diso stability by affecting the chemical properties of the DNA molecule. For example, guanosine + cytosine base pairs in the discriminator region impose a kinetic barrier to DNA untwisting and unwinding during the formation of the transcription bubble due to their greater base-pairing and base-stacking stability compared to adenosine + thymine base pairs  in which the discriminator region G + C% is titrated from 100% (AP3Discr1) to 16.7% (AP3Discr5) (A). We observed a negative correlation between discriminator G + C% and basal RPo stability as measured by three-nucleotide transcription assays (B). CarD increased three nucleotide RNA production from all promoter variants tested, but the magnitude of RPo stabilization by CarD displayed a negative correlation with basal RPo stability across AP3 variants as the discriminator G + C% was titrated (B).In addition to forming direct interactions with the polymerase, promoter DNA sequences can also influence RPse pairs , 41. Distability  and has tability , 43. To 3Discr5) A. We obsn assays B. CarD ititrated B.Figure C). On the AP3 variant with the lowest discriminator G + C% and highest basal RPo stability (AP3Discr5), CarD decreased transcript production, further supporting that promoters with high basal RPo stability can be transcriptionally repressed by CarD. Collectively, our experiments show that promoter sequence motifs that increase basal RPo stability decrease the magnitude of transcriptional activation by CarD and can lead to transcriptional repression in the most stable RPo contexts.Discriminator G + C% of the AP3 variants was also negatively correlated with basal transcript production in the multiround transcription assay and the magnitude of transcription activation by CarD decreased as discriminator G + C% decreased C. On theo stability could explain the outcomes in transcript production and CarD regulation, we performed a linear regression analysis across all of our promoter templates . In contrast, the log2 ratio of transcript production in multiround transcription reactions ± CarD shows a roughly linear inverse correlation with increasing RPo stability, with the most stable promoter variants  being transcriptionally repressed by CarD (B). The robust relationship across multiple promoter variants suggests that RPo stability is a fundamental determinant of full-length transcript production and CarD regulatory outcome. Collectively, our experiments illustrate a relationship between RPo stability, transcription strength, and CarD regulation and demonstrate that transcription factors like CarD can discriminate promoters based on their basal kinetic features to potentiate bidirectional outcomes in transcription regulation via a single kinetic mechanism.We show that base substitutions in the spacer region, extended −10 region , and disemplates . For thitability A. In con by CarD B. The roin vitro transcription experiments, we have identified multiple promoter sequence motifs associated with high RPo stability in vitro. If our model is generally applicable to transcription from mycobacterial promoters throughout the genome, then we would expect to find an association between DNA sequence motifs associated with RPo stability and transcriptional repression by CarD. To interrogate this prediction, we examined the prevalence of a consensus extended −10 motif (T-15G-14N-13) and discriminator GC% in promoters that were differentially expressed in our Mtb and M. smegmatis RNA-seq datasets . A similar trend was true of the proportion of promoters containing extended −10 elements in M. smegmatis, but the difference in proportions between CarD-regulated promoters and the genome-wide distribution was not statistically significant (B). In both species, promoters that were predicted to be repressed by CarD contained significantly more GC-rich discriminator regions than promoters predicted to be activated by CarD (C and D). The association of stable RPo DNA sequence signatures with genes that are inferred to be repressed by CarD in vivo support that the regulatory mechanisms that we demonstrate in vitro could be relevant to gene expression in vivo.Through our datasets . Since a element , 44, 45,megmatis , respect feature A. A siminificant B. In bot by CarD , C and Din vivo, as positive or negative supercoiling can inhibit or enhance RPo formation, respectively  (A). We performed in vitro three-nucleotide transcription assays using the topologically distinct DNA templates and found that negative supercoiling contributes to a ∼6-fold increase in basal RPo stability compared to a linear “cut” DNA template containing the same promoter sequence (B). In addition, the “nicked” DNA template exhibited a similar basal RPo stability to the “cut” DNA template, indicating that the higher RPo stability observed in the “mock” template is a result of supercoiling and not the circular shape of the molecule. The addition of CarD decreased the amount of three nucleotide transcript produced with the supercoiled “mock” DNA template. This result could indicate that CarD inhibits progression from RPo toward an initial transcribing complex intermediate (RPitc) that synthesizes the three nucleotide product quantified in these assays . Furthermore, CarD activated transcription from the “cut” and “nicked” DNA templates but repressed transcription from the supercoiled “mock” DNA template, which has a higher basal RPo stability relative to the “cut” and “nicked” molecules. These data demonstrate a single promoter DNA sequence can exhibit varying levels of basal RPo stability based on DNA supercoiling, and this supercoiling-dependent change in RPo stability can change the regulatory outcome of CarD on transcription. While the DNA sequence of a given promoter is constant within the genome, the topology of the DNA molecule can change over the lifetime of the cell. Thus, our findings reveal an additional layer of complexity in CarD’s regulatory mechanism and could help explain how CarD expression in vivo could lead to differential gene expression outcomes in different conditions.In mycobacteria, CarD transcript levels increase in response to double-stranded DNA breaks and genotoxic stress , suggestectively ,47. Thustreated) A. We persequence B. In adde assays . The basoduction C. FurtheMtb that affects the expression of over two-thirds of the genome  being transcriptionally repressed by CarD. CarD’s effect on mycobacterial transcription in vivo also reflects the observations from our in vitro experiments where promoters predicted to be repressed by CarD are associated with sequence features that correlate with high RPo stability  and promoters predicted to be activated by CarD are associated with an absence of these features. Collectively, these data support our model in which the specific outcome of CarD-mediated RPo stabilization is dependent on the kinetic properties of a given promoter and not on sequence-specific binding, which could explain the observed differential gene expression effects in CarD mutants in vivo.CarD is an essential transcriptional regulator in e genome  and whose genome , 13, 48.loenzyme , 15, 16,o stability in a stepwise manner , the effects of each mutation are likely more complex. Minor base substitutions in a promoter sequence can result in large-scale allosteric effects on other RNAP–DNA interactions  that is positioned to interact with a T at the −12 position of the nontemplate DNA strand at the upstream fork of the bubble  that were within 100 bp of a CarD binding site were significantly underenriched (hypergeometric test p = 5.66e-06) for promoters lacking a T-12  compared to the genome-wide proportion    and S5. o and decreasing transcript flux by inhibiting promoter escape ppGpp binds RNAP and destabilizes a kinetic intermediate preceding RPo, resulting in transcriptional repression at ribosomal RNA promoters that form unstable RPo and transcriptional activation at promoters of amino acid biosynthesis genes that form relatively stable RPo ppGpp , 61, Traable RPo , 69, 70.M. smegmatis strains used in this study were derived from mc2155 and grown in LB medium supplemented with 0.5% dextrose, 0.5% glycerol, and 0.05% Tween-80 at 37 °C. M. smegmatis strains expressing CarDWT, CarDR25E, CarDK125E, or CarDI27W were engineered so that the native copy of carD is deleted, and the respective CarD allele is expressed from a constitutive Pmyc1-tetO promoter integrated into the genome. The construction of these strains has been previously described  by bead-beating. RNA was isolated by TRIzol–chloroform extraction followed by isopropanol precipitation and finally resuspended in nuclease-free water (Invitrogen).All escribed , 20. Forfree Kit (Invitrogen) and submitted to the Washington University Genome Technology Access Center for paired-end Illumina sequencing (NovaSeq 6000 XP). Ribosomal RNA was depleted prior to sequencing using the Qiagen FastSelect system. Illumina reads were preprocessed using FastQC, and adapter sequences were removed using trimmomatic (HiSat2 (M. smegmatis mc2155 reference genome (assembly ASM1500v1) from the Ensembl database (featureCounts (DESeq2 (RNA samples were DNase treated using the TURBO DNA-mmomatic . Sequenc (HiSat2  to the Mdatabase . Reads mreCounts . Differe (DESeq2 . DownstrM. tuberculosis H37Rv genomic DNA encoding the different Mtb RNAP holoenzyme subunits were a gift from Jayanta Mukhopadhyay (Bose Institute). MtbRNAP-σA holoenzyme was purified as previously described (Mtb MtbRNAP-σA holoenzyme protein was expressed in E. coli BL21 cells containing the plasmids pET-Duet-rpoB-rpoC (encoding the β and β′ subunits), pAcYc-Duet-sigA-rpoA , and pCDF-rpoZ (encoding the ω subunit). Holoenzyme protein was isolated from E. coli cell lysate by affinity chromatography using a 2× 5 ml HisTrap HP Ni2+ affinity columns (Cytiva) and further purified by size-exclusion chromatography using a Sephacryl S-300 HiPrep column (Cytiva) to select for associated holoenzyme. Purified MtbRNAP-σA holoenzyme was flash frozen in storage buffer  and stored at −80 °C. CarD proteins were expressed in BL21 E. coli cells using the pET SUMO vector system described previously  containing the WT or variant AP3 promoter sequence from positions −39 to +4 relative to the transcription start site to create a linear double-stranded DNA fragment that was ligated into the pMSG434 plasmid. Linear DNA templates used for in vitro transcription were prepared PCR amplifying a 437 bp fragment from the pMSG434 plasmid. Plasmid DNA templates for in vitro transcription were constructed by inserting an intrinsic transcription termination sequence (5′-TTTAT-3′) into the pMSG434 plasmid 70 bp downstream of the cloned AP3 transcription start site. Negatively supercoiled plasmids were grown in E. coli and then isolated using a QIAGEN Plasmid Midi Kit. To generate cut or nicked plasmid templates, plasmid DNA was incubated with XhoI restriction endonuclease (NEB) at 37 °C or Bt.NstNBI nicking endonuclease (NEB) at 55 °C for 1 h, respectively. All DNA templates were purified by extracting with buffer-saturated phenol pH >7.4 (Invitrogen) followed by isopropanol precipitation before being used in in vitro transcription reactions. A full list of the primers used to construct the DNA templates can be found in Promoter fragments used for in vitro transcription assays were performed by combining MtbRNAP-σA holoenzyme, template DNA, and NTPs in a 20 μl reaction volume. Multiround reactions contained final concentrations of 40 nM RNAP holoenzyme, 0.8 nM DNA template, 0.1 mg/ml BSA, 1 mM DTT, 400 μM GTP, 200 μM ATP, 200 μM CTP, 200 μM UTP, 20 μCi/ml [ɑ-32P]-UTP (PerkinElmer), 10 mM Tris-HCl pH 7.9, 10 mM MgCl2, and 40 mM NaCl. Reactions were initiated with the addition of NTPs and incubated at 37 °C for 1 h before being terminated with the addition of 20 μl ‘stop buffer’ (95% formamide and <0.1% bromophenol blue and xylene cyanol). Three nucleotide in vitro transcription reactions were performed in the same manner, except with final reaction concentrations of 100 nM RNAP holoenzyme, 10 nM DNA template, 0.1 mg/ml BSA, 1 mM DTT, 20 μM GpU, 10 μM UTP, 62.5 μCi/ml [ɑ-32P]-UTP, 10 mM Tris-HCl pH 7.9, 10 mM MgCl2, and 40 mM NaCl. Multiround and three nucleotide in vitro transcription reaction products were separated by gel electrophoresis on denaturing (7M urea) 8% or 22% polyacrylamide gels, respectively, which were vacuum dried and visualized using a phosphorimager screen. Reactions with CarD contained 25:1 M ratio CarD:RNAP (1 μM CarD for the multiround in vitro transcription reactions or 2.5 μM CarD for the three nucleotide transcription reactions) unless otherwise noted. All original unprocessed gel images can be found in Multiround GSE222815.Raw RNA-seq data have been deposited in the GEO repository under accession code This article contains The authors declare that they have no conflict of interest with the contents of this article.


RNA polymerase II transcription can efficiently occur when mouse embryonic stem cells lack TBP and TBP-like proteins, confirming that this initiation factor may not be as essential as once thought. Related research article Kwan JZJ, Nguyen TF, Uzozie AC, Budzynski MA, Cui J, Lee JMC, Van Petegem F, Lange PF, Teves SS. 2023. RNA polymerase II transcription independent of TBP in murine embryonic stem cells. eLife12:e83810. doi: 10.7554/eLife.83810.Transcription requires a large number of proteins to come together in a precise order to read a particular segment of the genome and copy it into RNA. Switching on a protein-coding gene, for instance, involves a set of highly evolutionary conserved transcription factors to assemble at the site where transcription begins . TogetheThe first protein to bind to DNA is usually TBP (short for TATA-box binding protein), which then promotes the remaining components of the preinitiation complex to attach to the transcription start site. While TBP can initiate this process on its own, it normally carries out this role as one of the many subunits of transcription factor TFIID, which also contains 13 TBP-associated factors (TAFs). TBP became a central focus of the transcription field when it was discovered that all three nuclear RNA polymerases found in eukaryotes  require TBP, elevating it to the status of ‘universal transcription factor’ .However, the genomes of multi-cellular eukaryotes also encode TBP-like proteins, known as TBP-related factor 2 (TRF2) and TBP-related factor 3 (TRF3), which may replace TBP to ensure RNA polymerase II transcription in specific developmental processes. So far, the role of TRF2 and TRF3 has been respectively restricted to male and female germ cells in mammals, and to the transcription of developmentally regulated genes in other animals . Yet, ovSubsequent studies then revealed that RNA polymerase II – but not RNA polymerase I and III – could in fact transcribe DNA without TBP or similar proteins . HoweverThe team  used an auxin-based system which utilizes the cell’s own processes to rapidly and synchronously degrade TBP proteins. The amount of TBP and RNA polymerase II bound to the promoter sites where transcription begins was then quantified together with the level of newly generated RNA molecules.Kwan, Nguyen et al. found that although the level of TBP was reduced by almost 90%, this did not compromise the integrity of TFIID. However, the recruitment of another TFIID subunit called TAF4, as well as the transcription factor TFIIA, was reduced. The level of RNA polymerase II bound to promoter sites and involved in transcription (measured by nascent RNA production) remained essentially the same, whereas RNA polymerase III transcription was strongly inhibited. Importantly, TBP degradation in embryonic stem cells where the gene for TRF2 was inactivated also did not reduce RNA polymerase II transcription, suggesting that TRF2 does not compensate for the loss of TBP. In these conditions, RNA polymerase II transcription occurs without TBP and TBP-like proteins (as TRF3 is not expressed in embryonic stem cells and therefore cannot play a compensating role).Further experiments revealed that TBP depletion did not affect the activation of previously unexpressed genes induced by heat shock or retinoic acid treatment. This suggests that preinitiation complexes do not need TBP to switch on previously inactive genes or to maintain the transcription of the ones which are already turned on.This work represents one of the most comprehensive mechanistic studies on how TBP depletion affects RNA polymerase II transcription in mammalian cells, but many questions remain. The findings of Kwan, Nguyen et al. suggest that there is no linear relationship between TBP levels and RNA polymerase II transcription; however, it is possible that the residual 10% of TBP left behind after degradation could be playing some yet undefined role. If so, it would be clearly at odds with established ideas of the role of TBP in transcription initiation, requiring a serious revision of current models. Furthermore, it remains to be seen if the DNA bending normally induced by TBP still happens in its absence, and if so how this occurs. It is possible that TBP loss is compensated for by contacts made by the other subunits of TFIID with the DNA and other proteins of the preinitiation complex, as Kwan, Nguyen et al. showed that the integrity and recruitment of these components were not strongly affected by TBP depletion.If TBP is not required to form new preinitiation complexes or reinitiate transcription, then what is its role? One possibility is that mouse embryonic stem cells do not need TBP for transcription as they have very open chromatin environments, making it easier for RNA polymerase enzymes to access and bind to DNA – a situation that would not apply to most other cell types. Another suggested role for TBP is in mitotic book marking, as it remains bound to DNA during mitosis so certain genes can be quickly reactivated after division . Overall