Deregulation of Pol III products causes a range of diseases, including neural diseases and cancers. However, the factors and mechanisms that modulate Pol III-directed transcription remain to be found, although massive advances have been achieved. Here, we show that STAT3 positively regulates the activities of Pol III-dependent transcription and cancer cell growth. RNA-seq analysis revealed that STAT3 inhibits the expression of TP73, a member of the p53 family. We found that TP73 is not only required for the regulation of Pol III-directed transcription mediated by STAT3 but also independently suppresses the synthesis of Pol III products. Mechanistically, TP73 can disrupt the assembly of TFIIIB subunits and inhibit their occupancies at Pol III target loci by interacting with TFIIIB subunit TBP. MiR-106a-5p can activate Pol III-directed transcription by targeting the TP73 mRNA 3’ UTR to reduce TP 73 expression. We show that STAT3 activates the expression of miR-106a-5p by binding to the miRNA promoter, indicating that the miR-106a-5p links STAT3 with TP73 to regulate Pol III-directed transcription. Collectively, these findings indicate that STAT3 functions as a positive regulator in Pol III-directed transcription by controlling the miR-106a-5p/TP73 axis. Leu-CAA and tRNATyr-GTA are required for the regulation of senescence Brf1 and Gtf3c2 genes by binding to their promoters , RNA splicing (U6 snRNA), protein translocation (7SL RNA), Pol II transcription elongation (7SK RNA), and so on romoters . Despite3GAA). STAT3 has been shown to regulate the transcription of genes related to survival (BCL-XL and Mcl1), cell cycle (CyclinD1), cell migration , and angiogenesis  and immunosurveillance , an important effector in the IL-6/JAK/STAT3 signaling pathway, regulates diverse biological processes, including apoptosis, angiogenesis, immunosuppression, cell proliferation, and migration . During f1 genes . Due to f1 genes . Recentlf1 genes . Thus, iIn our recent study, we found that STAT3 can positively regulate RNA polymerase I-dependent transcription by controlling RPA34 expression (manuscript accepted). Since 5S rRNA, an important component of ribosomes, was synthesized by RNA polymerase III, we hypothesized that STAT3 can also regulate Pol III-dependent transcription to coordinate cellular ribosome biogenesis. Indeed, in this study, we found that STAT3 expression levels positively correlate with the synthesis of Pol III products and cell proliferation. Mechanistic analysis revealed that STAT3 modulates Pol III-directed transcription by controlling the activity of the miR-106a-5p-TP73 axis.RNA polymerase I and III (Pol I and Pol III) products play fundamental roles in several cellular processes, including ribosomal biogenesis, translation, development, and cell growth . In our It is well established that Pol III product levels correlate closely with cell growth or cell proliferation activity . WhetherSTAT3 has been shown to regulate both cell proliferation and Pol III-directed transcription. Thus, we next investigated whether changes in cell proliferation are caused by the alteration of Pol III products. To this end, cell proliferation assays were performed using the HepG2 cell lines with or without STAT3 overexpression in the presence and absence of Pol III transcription-specific inhibitor . We showed that STAT3 overexpression enhanced cell proliferation and Pol III-dependent transcription and that the presence of ML-60218 inhibited proliferative activity and pol III-dependent transcription for HepG2 control cells. Strikingly, the presence of ML-60218 reversed the activation of cell proliferation and Pol III-directed transcription induced by STAT3 overexpression , indicatTo validate whether STAT3 promotes liver cancer cell growth in vivo, we injected HepG2 cells into nude mice and measured tumor sizes regularly during tumor formation. Analysis of tumor sizes showed that STAT3 silencing inhibited the sizes of liver tumors during tumor formation . After rBCL2  assays using HepG2 cell lines stably expressing STAT3 shRNA or control shRNA and antibodies against components of the Pol III transcription machinery. The results of ChIP assays showed that STAT3 knockdown reduced the recruitment of TBP, BRF1, GTF3C2, GTF3C3, and POLR3A at several Pol III target loci, including DNA loci encoding 5S rRNA, 7SL RNA, and tRNA-Met . HoweverBCL2 , suggestBCL2 , suggestWe showed that STAT3 knockdown did not affect the expression of TBP, BRF1, GTF3C2, and GTF3C3 proteins. However, the number of Pol III transcription machinery components is far beyond the number of factors tested above. Thus, we sought to investigate the expression of all components of the Pol III transcription machinery by performing RNA-seq using the STAT3-depleted HepG2 cells and the corresponding control cells. Analysis of mRNA transcriptome showed that STAT3 silencing caused expression upregulation of 356 genes and expression downregulation of 590 genes significantly . Next, wSTAT3 has been shown to inhibit TP73 mRNA expression; thus, we determined whether TP73 protein expression is affected by STAT3 expression changes. Western blot analysis showed that STAT3 depletion enhanced TP73 protein expression, whereas STAT3 overexpression reduced its protein levels in HepG2 cells . ConsistTP73 has been shown to negatively regulate Pol III-directed transcription. To uncover the mechanism by which TP73 modulates Pol III-directed transcription, we performed ChIP assays using HepG2 cell lines with TP73 depletion and antibodies against components of the Pol III transcription machinery. Interestingly, TP73 silencing enhanced the occupancies of Pol III transcription machinery components at DNA loci encoding 5S rRNA, 7SL RNA, and tRNA-Met . For theTP73 gene transcription. To support this hypothesis, we searched for potential miRNA molecules targeting TP73 mRNA from the Database of TargetScanHuman: Prediction of miRNA Targets  was inserted into the same vector in parallel , and the resulting fragments were inserted downstream of a parallel . The resparallel . These rpression  but signpression , indicatpression . AnalysimiR-106a-5p gene. As expected, the miR-106a-5p gene promoter contains several STAT3 binding motifs . In addition, STAT3 has been shown to promote cell proliferative activity in several types of cancer stem cells  inhibits Pol III-dependent transcription and cell proliferation , suggestem cells . The inhem cells . TherefoTaken together, in this study, we found that STAT3 positively modulate Pol III-directed transcription and cell proliferation. STAT3 inhibits TP73 expression and activates miR-106–5a expression. Both miR-106a-5p and TP73 are not only required for the regulation of Pol III-directed transcription mediated by STAT3 but also independently regulate Pol III-dependent transcription. Transfection of miR-106a-5p mimics was able to reduce TP73 expression by targeting TP73 mRNA 3’UTR and activate Pol III-dependent transcription. Therefore, STAT3 modulates Pol III-directed transcription by controlling the miR-106a-5p/TP73 axis. The findings from this study shed light on the regulatory mechanism of Pol III-directed transcription and lay a foundation for developing novel drugs for cancer therapy.mCherry gene in the protein-expressing lentiviral plasmid pLV-EF1α-mCherry-puro. HepG2 , HuH-7 , and 293T  cell lines were cultured in their respective media supplied with 10% fetal bovine serum (Gibcol Co.) and 1×penicillin/streptomycin (Hyclone Co). These cell lines have been authenticated by STR profiling test and proved without contamination before used in various assays in the present study. Biological reagents, including transfection reagents, qPCR Master Mix, ECL western blot detection solution, and others, were purchased from Thermo Scientific. Chemical reagents were obtained from Sinopharm Chemical Reagent Co .The shRNA-expressing lentiviral plasmid pLV-U6-EGFP-Puro was obtained from Inovogen Tech Co . Three dsDNA fragments encoding distinct STAT3 shRNAs were inserted downstream of the U6 promoter within the pLV-U6-EGFP-Puro plasmid. STAT3 cDNA was synthesized using the total RNA extracted from 293T cells and reverse transcriptase , amplified by PCR, and inserted immediately downstream of a For the generation of cell lines stably expressing shRNA or fusion protein, lentiviral vectors expressing STAT3 or TP73 shRNA or expressing mCherry-STAT3 were transiently transfected into 293T cells along with the packaging plasmids pH1 and pH2. After 48 hr, the culture medium containing lentiviral particles was collected and filtered with a sterilized filter (0.45 μm). Meanwhile, HepG2, HuH-7, and 293T cells were grown in 12-well plates; at 70% confluence, cells were incubated with the lentiviral particle-containing medium. The cell lines stably expressing shRNA or exogenous protein were obtained by selecting with puromycin (5 μg/mL) and screening using 96-well plates. The colonies with shRNA or fusion protein stable expression were verified by western blot using antibodies against STAT3, TP73, and mCherry.2O. For one qPCR reaction, 1 μL of cDNA samples was mixed with 0.5 μL of 5 μM forward and reverse primers and 10 μL of 2×SYBRGreen PCR Mastermix within a 20 μL reaction system. qPCR reactions were performed in a Bio-Rad real-time detection system as described previously . When the reaction finished, the resulting products were diluted to 100 μL with ddHeviously . An ActiCell proliferative activity was detected using several distinct methods, including cell counting, cell counting kit-8 (CC-8), and EdU assays. For cell counting assays, HepG2, HuH-7, and 293T cell lines with STAT3 silencing or overexpression and their corresponding control cell lines were cultured in 12-well plates. Cell counting was performed every 24 hr, and the resulting data were subjected to statistical analysis. For CCK-8 assays, cells were seeded in 96-well plates, and cell proliferation was monitored based on the protocol as described previously . For EdU3 HepG2 cells with STAT3 silencing or overexpression and their corresponding control cells were seeded in 6-well plates, and the culture medium was replaced every 2 days. After growing for 12 days, cells were fixed with ice-cold 100% methanol and stained with 0.3% crystal violet. After washing three times with deionized distilled H2O, cell colonies were pictured with a camera.Colony formation assays were performed using HepG2 cells cultured in 6-well plates. Briefly, around 1×107 HepG2 cells expressing STAT3 shRNA or control shRNA. After growing 7 days, tumors within nude mice appeared and were measured with a vernier caliper every 7 days. Tumor volumes were calculated using the following formula: V = 2. At the end of the fifth week, nude mice were euthanized, and tumors within mice were removed, weighed, and photographed. The resulting data were subjected to statistical analysis. Tumor samples randomly picked from controls or treatments were used for immunohistochemistry analysis performed by the Analysis Center at Wuhan University of Science and Technology. Animal experiments were approved by the Animal and Medical Ethics Committee of the School of Life Science and Health at the Wuhan University of Science and Technology. The animal protocols abided by the Animal Welfare Guidelines (China), and the approval with a stamp and reference number (WKDLL-2020–019) is available on request.12 of the 5-week-old female BALB/C nude mice were purchased from the Vital River Laboratory Animal Technology Co . These nude mice were nurtured under a sterile condition with controlled temperature, humidity, and light. After inhabiting for 1 week, nude mice (n=6 for each group) were injected subcutaneously with 1×102O. 1 μL of ChIP DNA sample (1/40) was used for a qPCR reaction, while 0.02% input DNA (0.1 ng genomic DNA) was used in a qPCR reaction for positive control. Relative enrichment was obtained by comparing the quantity of target DNA in 1 μL of ChIP DNA sample to that in 0.02% input DNA.HepG2 cells with STAT3 or TP73 silencing and their control cells were cultured in 10 cm dishes. At 85% confluence, cells were fixed for 10 min using 10 mL of 1% formaldehyde solution freshly prepared with 1×PBS and subsequently quenched with 1 mL of 2.5 M glycine solution. After washing three times with 1×PBS solution, cell samples were harvested with cell scrappers and were sheared with an ultrasonication machine . After sonicating for 20 min  with 25% power, cell lysate was centrifuged for 10 min at 12,000 rpm, and the resulting supernatant was retained for ChIP assays as described previously , where aHepG2, HuH-7, and 293T cells were cultured in 6-well plates. At 90% confluence, cells were harvested and lyzed with 1×SDS loading buffer. After heating at 100°C for 10 min, 20 μL cell lysate was loaded for SDS-PAGE electrophoresis and western blot analysis. Western blot was performed using antibodies against STAT3 , mCherry , GAPDH , BRF1 , GTF3C2 , GTF3C3 , TBP , and TP73 .HepG2 cells were cultured in 10 cm dishes. At 90% confluence, cell samples were harvested, and nuclei were isolated using a hypotonic buffer  and a NP-40 buffer . After isolation, nuclei were suspended using a modified nuclear extraction buffer  and subjected to vortexes for 30 min. The nuclear suspension was centrifuged for 10 min at 13,000 rpm, and the supernatant (nuclear extract) was retained for IP assays. IP reactions were performed using 200 μL nuclear extract and 5 μg of antibodies, respectively, against TP73 , TBP , BRF1 , GTF3C2 , and GTF3C3 . Antibody-bound proteins were precipitated by incubating with protein A/G agarose and washed with a modified RIPA buffer . IP samples were eventually eluted with 40 μL of 1×SDS loading buffer . Western blot was performed using 10 μL of the IP samples, where 5% input was loaded as a positive control. The antibodies used for western blot were the same as those used for IP assays.A HepG2 cell line expressing STAT3 shRNA and its control cell line established above were cultured in 10 cm dishes in triplicates. At 85% confluence, cells were harvested, and total RNA was prepared using a Axygen RNA minprep kit. After quantification, the total RNA was sent for mRNA-seq analysis. The clean data were aligned to the human reference genome (Hg38) using Hisat2 software . DEGs were analyzed using the DESeq Bioconductor package as described previously . p ValueDNA fragments that encode the wild-type TP73 mRNA 3’UTR recognized by miR-106A-5p were synthesized and inserted downstream of a luciferase gene in the reporter vector pmiRGLO. Meanwhile, a mutant disrupting miR-106A-5p binding was generated in parallel. The resulting vectors and miR-106a-5p mimics were co-transfected into 293T and HepG2 cells cultured in 12-well plates. After 48 hr, cells were harvested and lysed with 100 μL of lysis buffer provided by a luciferase detection kit (Promega). After centrifugation, the supernatant was retained, and 5 μL of the supernatant was used to analyze the activities of luciferase. Relative luciferase activity was obtained by comparing the luciferase activity of the cells co-transfected with both miRNA mimic and the reporter vectors containing the wild-type TP73 3'UTR to that from the cells transfected with the reporter vector containing the mutated TP73 3’ UTR.https://www.kmplot.com), which is based on the RNA-seq data of LIHC and kidney renal carcinoma deposited at the TCGA. The experiments in this study were performed with minimal three biological replicates (n≥3). The mean and SD for the data from gene expression analysis, cell proliferation assays, ChIP assays, and luciferase assays were calculated with the GraphPad Prism 8.0 software. Each bar/point in the histograms represents the mean ± SD (n≥3). p Values were obtained by Student’s t test or two-way ANOVA.Kaplan-Meier plotting showing the relationship between miR-106a-5p and survival probability and time was performed using the Kaplan-Meier Plotter online tool  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by Michael Green as a Reviewing Editor and James Manley as the Senior Editor. The following individuals involved in the review of your submission have agreed to reveal their identity: Martin Teichmann (Reviewer #1); Robert J. White (Reviewer #2).Thank you for submitting your article "STAT3 promotes RNA polymerase III-directed transcription by controlling the miR-106a-5p/TP73 axis" for consideration by eLife. The reviewers have specified two revisions as essential for publication in eLife, along with several other recommended revisions, as detailed in the individual reviews.The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission. Overall the reviewers were quite positive about the suitability of this submission for publication in Essential revisions:1. The authors need to complete the partial characterization of U6 transcription as a function of STAT3 expression. These studies would only concern the ChIP and Western blot experiments of figures 3C and J, as well as 6C and I. The authors should include BRF2 in these experiments.With these experiments, the authors could show that not only U6 transcription is affected by STAT3 and TP73 expression  but also that the association of BRF2 with the U6 gene is (or is not) affected. This would result in the presentation of comparable results to those they showed to characterize Pol III genes with gene-internal promoters. Without these experiments, the analysis of Pol III transcribed genes with promoters upstream of the transcription initiation site remains incomplete.2. Petrie et al. (2019) demonstrated that pol III activity promotes the ability of cancer stem cells (CSC) to initiate tumours . Given the well-established role of STAT3 in CSC generation, it would be worth discussing if pol III control contributes to the stimulatory effects of STAT3 on CSC.Reviewer #1 (Recommendations for the authors):To fully investigate the effects of STAT3 on Pol III transcription, the authors should determine whether TP73 also affects Pol III transcription of genes controlled by promoters located 5' of the transcription initiation site. To this end, they should monitor the expression of these genes and analyze the assembly of the BRF2-containing form of TFIIIB in response to STAT3 expression. U6 data were used as a negative control for Pol-III genes not regulated by BRF1-containing TFIIIB, but direct data on STAT3-dependent regulation of the U6 gene or other SNAPc-dependent Pol-III genes are lacking.In addition, co-IP of recombinant TP73 and recombinant TFIIIB subunits  should be performed to gain better mechanistic insight into which TFIIIB subunit TP73 interacts with in regulating Pol III transcription (presumably BRF1 and BRF2).eLife.If the authors are able to address these questions, I am in favor of publication in Reviewer #2 (Recommendations for the authors):The Abstract states that "…the pathways and factors that control Pol III-directed transcription have yet to be elucidated." This fails to acknowledge the considerable amount of published work investigating many aspects of pol III transcriptional regulation. The current manuscript makes a very valuable addition, but it is building on a well-established field.P8. The authors should explain what is measured by the CCK8 approach.P11. Figure 3I presents ChIP data in which binding of STAT3 is not detected at several pol III-transcribed loci. The conclusion that STAT3 does not act directly at these sites would be greatly strengthened by a positive control confirming that STAT3 binding can clearly be detected at other loci in this assay.P16. Figure 6I seems to show some change in levels of BRF1, GTF3C2, and GTF3C3 when TP73 is depleted, contrary to the authors' statement (lines 340-341).Petrie et al. (2019) demonstrated that pol III activity promotes the ability of cancer stem cells (CSC) to initiate tumours . Given the well-established role of STAT3 in CSC generation, it would be worth discussing if pol III control contributes to the stimulatory effects of STAT3 on CSC.Line 362:….STAT3 was originally identified as a positive regulator in RNA polymerase II-directed…should be rephrased into:….STAT3 was originally identified to be a positive regulator in RNA polymerase II-directed…Line 405:Change:….To suppoort this probability…..into….To support this probability….. Essential revisions:1. The authors need to complete the partial characterization of U6 transcription as a function of STAT3 expression. These studies would only concern the ChIP and Western blot experiments of figures 3C and J, as well as 6C and I. The authors should include BRF2 in these experiments.With these experiments, the authors could show that not only U6 transcription is affected by STAT3 and TP73 expression  but also that the association of BRF2 with the U6 gene is (or is not) affected. This would result in the presentation of comparable results to those they showed to characterize Pol III genes with gene-internal promoters. Without these experiments, the analysis of Pol III transcribed genes with promoters upstream of the transcription initiation site remains incomplete.We totally agree with this comment. We have analyzed the effect of STAT3 expression alteration on BRF2 expression by Western blot and examined BRF2 binding at the U6 snRNA promoter by performing ChIP assays with BRF2 antibody, and we added the data into Figure 3C, J and K. Likewise, we analyzed the effect of TP73 expression alteration on BRF2 expression and performed CHIP assays using the BRF2 antibody, and the data obtained were added into Fig6 C and I. We very much thank the reviewers for the constructive comment.2. Petrie et al. (2019) demonstrated that pol III activity promotes the ability of cancer stem cells (CSC) to initiate tumours . Given the well-established role of STAT3 in CSC generation, it would be worth discussing if pol III control contributes to the stimulatory effects of STAT3 on CSC.Yes, it is a good suggestion. We have discussed our findings by referring previous publications about the roles of STAT3 and Pol III products in cell stem cells growth and survival in the Discussion section of the revised manuscript.Reviewer #1 (Recommendations for the authors):To fully investigate the effects of STAT3 on Pol III transcription, the authors should determine whether TP73 also affects Pol III transcription of genes controlled by promoters located 5' of the transcription initiation site. To this end, they should monitor the expression of these genes and analyze the assembly of the BRF2-containing form of TFIIIB in response to STAT3 expression. U6 data were used as a negative control for Pol-III genes not regulated by BRF1-containing TFIIIB, but direct data on STAT3-dependent regulation of the U6 gene or other SNAPc-dependent Pol-III genes are lacking.These are good comments. Yes, we have analyzed the effect of STAT3 or TP73 expression alteration on BRF2 expression by Western blot, and investigating the effect of STAT3 or TP73 depletion on BRF2 assembly at the U6 promoter by performing ChIP assays using the BRF2 antibody. We added these data into Figures3  and Figures6 (C and I). Thanks very much for the helpful comments.In addition, co-IP of recombinant TP73 and recombinant TFIIIB subunits  should be performed to gain better mechanistic insight into which TFIIIB subunit TP73 interacts with in regulating Pol III transcription (presumably BRF1 and BRF2).If the authors are able to address these questions, I am in favor of publication in eLife.. It would be good to look at the interaction between TP73 and BRF1 and BRF2 by performing co-IP assays using recombinant TP73 and recombinant TFIIIB subunits  in vitro. We are really sorry for that we cannot obtain so many recombinant proteins for these assays due to time and fund problems. Since our co-IP assay showed that TP73 did not bind to BRF1 but bind to TBP ; therefore, we still performed co-IP assays using TBP and BRF2 antibodies, and the result was consistent with that obtained in the co-IP assays with TBP and BRF1 antibody. These data were presented in Figure 6—figure supplement 1.It is a good commentReviewer #2 (Recommendations for the authors):The Abstract states that "…the pathways and factors that control Pol III-directed transcription have yet to be elucidated." This fails to acknowledge the considerable amount of published work investigating many aspects of pol III transcriptional regulation. The current manuscript makes a very valuable addition, but it is building on a well-established field.We are sorry for the inaccurate description about the research background. It has been corrected in the Abstract and elswhere in the revised manuscript. Thanks very much for the comment.P8. The authors should explain what is measured by the CCK8 approach.Yes, it is a good suggestion; we have added the explanation about the CCK8 method as suggested.P11. Figure 3I presents ChIP data in which binding of STAT3 is not detected at several pol III-transcribed loci. The conclusion that STAT3 does not act directly at these sites would be greatly strengthened by a positive control confirming that STAT3 binding can clearly be detected at other loci in this assay.Yes, It is a good suggestion. We added a STAT3-binding positive control ( BCL2 promoter) in Figures 3I.P16. Figure 6I seems to show some change in levels of BRF1, GTF3C2, and GTF3C3 when TP73 is depleted, contrary to the authors' statement (lines 340-341).It seems that some change was observed in the levels of those proteins. However, the load control GAPDH has also been showed the same trend. To make these data more confident, we repeated some of the experiments by Western blot and replaced the data in the revised manuscript .Petrie et al. (2019) demonstrated that pol III activity promotes the ability of cancer stem cells (CSC) to initiate tumours . Given the well-established role of STAT3 in CSC generation, it would be worth discussing if pol III control contributes to the stimulatory effects of STAT3 on CSC.Yes, it is a good suggestion. We have discussed our findings by referring previous publications about the roles of STAT3 and Pol III products in cell stem cells survival and growth in the Discussion section of the revised manuscript.Line 362:….STAT3 was originally identified as a positive regulator in RNA polymerase II-directed…should be rephrased into:….STAT3 was originally identified to be a positive regulator in RNA polymerase II-directed…It has been corrected as suggested. Thanks very much for the helpful comment.Line 405:Change:….To suppoort this probability…..into….To support this probability…..We are sorry for the mistake. It has been corrected as suggested.


Methylorubrum extorquens DM4 is a longstanding reference strain. Here, the primary 5′-ends of transcripts were obtained using a differential RNA-seq (dRNA-seq) approach to provide the first transcription start site (TSS) genome-wide landscape of a methylotroph using DCM or methanol. In total, 7231 putative TSSs were annotated and classified with respect to their localization to coding sequences (CDSs). TSSs on the opposite strand of CDS (antisense TSS) account for 31% of all identified TSSs. One-third of the detected TSSs were located at a distance to the start codon inferior to 250 nt (average of 84 nt) with 7% of leaderless mRNA. Taken together, the global TSS map for bacterial growth using DCM or methanol will facilitate future studies in which transcriptional regulation is crucial, and efficient DCM removal at polluted sites is limited by regulatory processes.Dichloromethane  is a toxic halogenated volatile organic compound massively used for industrial applications, and consequently often detected in the environment as a major pollutant. DCM biotransformation suggests a sustainable decontamination strategy of polluted sites. Among methylotrophic bacteria able to use DCM as a sole source of carbon and energy for growth,  Collectively, these stresses have a strong impact on gene regulation and the activity of regulatory factors, with extensive genome-wide modulation of gene expression ,13.dcmA gene expression is accompanied by high transcription levels of clcA encoding chloride/proton antiporter clcA, involved in dealing with chloride stress associated with DcmA dechlorination activity [Methylorubrum genus so far, and only very few transcriptional start sites (TSSs) have been experimentally determined, including in DCM-degrading strains [Modulation of transcription initiation is the first step in the regulation of gene expression. It is crucial for genes that are essential for growth and global regulation of metabolism, and thus for bacterial responses to stress and environmental variations. In the case of growth with DCM, for instance, activity . In addiactivity . However strains ,21,22,23Bacillus methanolicus MGA3 [Bacillus amyloliquefaciens strains XH7 [Bradyrhizobium japonicum USDA 110 [Mycobacterium smegmatis [M. extorquens DM4 from methylotrophic mid-exponential phase DCM and methanol cultures of the strain.Prediction of promoter regions based solely on computational methods does not allow full identification of transcription features of interest, such as alternate promoters and promoters that govern antisense transcription or are located within genes. Such features, however, are highly relevant for gene expression and transcription/translation coupling in prokaryotes (for a review see ). Expericus MGA3 ; Bacilluains XH7  and FZB4ains XH7 ), ProteoUSDA 110 ), and Acmegmatis ). MoreovM. extorquens wild-type strain DM4 was grown aerobically at 30 °C in M3 medium supplemented with methanol or DCM provided at 10 mM as the sole source of carbon and energy, in independent duplicates of 220 mL in 1.2 L Erlenmeyer flasks closed with gas-tight screw caps with Supelco Mininert R valves , as described previously [eviously . Cells wVertis Biotechnologie AG (Germany) constructed the libraries in a strand-specific manner, as described previously . Brieflyhttp://rna.tbi.univie.ac.at/TSSAR; accessed on October 2018) [M. extorquens DM4, TSSAR assigned a TSS only if a significant difference between the two banks was observed based on the Skellam distribution. When the most stringent TSSAR parameters were used, i.e., 10 reads  and 1 nucleotide , a number of assigned TSSs equivalent to the number of predicted M. extorquens DM4 annotated genes (5988) [p-value of 0.01. Visualisation of the genome-wide nucleotide localisation of assigned TSS on the reference genome was performed with IGV (Integrative Genomics Viewer) [Sequence raw data (.fastq) were quality-checked using FastQC  and aligned to the reference genome with the Burrows-Wheeler Alignment tool (Version 0.7.4) . A majorer 2018) . For eacs (5988)  were obt Viewer) . When, f Viewer)  and seqi Viewer) . Transcr Viewer) .Identification of −10 and −35 regions was performed by scanning upstream sequences of the chromosomal 2261 predicted P and IP TSS for conserved motifs using MEME Differential Enrichment mode . For theThe 5′-UTR region was defined as the sequence between the TSS and the start codon. 5′-UTR generally contains RBS, defined as the sequence that is partially complementary to 16S rRNA guiding the translation machinery to align with the start codon of the open reading frame (ORF) . Leaderlwww.ebi.ac.uk/arrayexpress, accessed on 21 June 2022) under accession number E-MTAB-11726. Overview of mapped reads, and coverage directly plotted on IGV genome browser are available on the MicroScope platform (https://mage.genoscope.cns.fr/microscope/mage/index.php?), Transcriptomics tab, RNAseq project TSS_VXTYE3_Mextorquens_DM4.RNA-seq data were deposited in the ArrayExpress database at EMBL-EBI , antisense TSS, internal TSS, and orphan TSS with a distribution shown in approach . This suentified ,44.M. extorquens DM4 . Two TSSs were found for gene dcmR corresponding to promoters PR1 and PR2 [A, PR1 and PR2  using nuclease S1 mapping, run-off or primer extension. In some cases, the correlation is excellent. This was the case for genes involved in methanol utilisation of the mxa operon as found for PmaxF in M. extorquens AM1 [M. organophilum also identified PmaxF despite sequence variation downstream of the −10 box and a 2 nt longer predicted 5′UTR [uens AM1 ,20 (Tabled 5′UTR  comparedM. extorquens AM1 qsc2 (mtkA-mtkB-ppc-mcl) operon identified two TSSs associated with Pqsc2-1 and Pqsc2-2 promoters, predominant in RNA extracted from cultures in methanol and succinate, respectively [mtkA. It most likely corresponds to the TSS of Pqsc2-1, resulting in a 46 nt-long 5′UTR. When the sequence corresponding to Pqsc2-2 was searched in the genome of strain AM1 [qsc2-2 activity in strain AM1, and its minor role for methylotrophic growth conditions, as suggested previously [Primer extension of the 5′end of ectively . In thisrain AM1 . This sueviously .sga encoding serine-glyoxalate aminotransferase, in addition to the previously detected TSS [qsc1 orphan-type TSS in this study). In two other cases, previously detected TSSs could not be confirmed here as class P TSS using dRNA-seq. Instead, TSSs were detected in upstream genes, suggesting co-transcription with adjacent genes (orf181 and pqqD). Co-transcription of genes ileS and lspA  was detected upstream of a key gene in C1 assimilation, gene cted TSS  Table 2rf181 in ) is inde1 in  is Altogether, the prediction of different promoter regions based on dRNA-seq correlated with those previously identified using different methods, in most cases. Nonetheless, we found some differences, which may originate from experimental design , sequendcmA and dcmR promoters (PA and PR1), no regulatory sequence associated with dichloromethane utilisation has been proposed so far [Besides the GATC(C/G)ATAGCCT motif present in the d so far . Of the d so far , some maM. extorquens DM4 map obtained here establishes a valuable complement to genome sequence and proteome data for the strain, with different nucleic acid motifs associated with gene expression, such as consensus sequences found in promoter regions, mapped out in unprecedented detail (Methylorubrum, not only for gene expression and organisation but also for gene discovery. This rich dataset will facilitate future studies of genes of interest as well as systems biology investigations of methylotrophic metabolism, and in particular on the utilisation of chlorinated C1 compounds and corresponding adaptative mechanisms.The detailed TSS of DCM-degrading strain d detail . More ge


Plasmodium falciparum, PfSAMS is encoded by a single gene and has been suggested to be crucial for malaria pathogenesis and transmission; however, to date, PfSAMS has not been fully characterized. To gain deeper insight into the function of PfSAMS, we generated a conditional gene knockdown (KD) using the glmS ribozyme system. We show that PfSAMS localizes to the cytoplasm and the nucleus of blood-stage parasites. PfSAMS-KD results in reduced histone methylation and leads to impaired intraerythrocytic growth and gametocyte development. To further determine the interaction network of PfSAMS, we performed a proximity-dependent biotin identification analysis. We identified a complex network of 1114 proteins involved in biological processes such as cell cycle control and DNA replication, or transcription, but also in phosphatidylcholine and polyamine biosynthesis and proteasome regulation. Our findings highlight the diverse roles of PfSAMS during intraerythrocytic growth and sexual stage development and emphasize that PfSAMS is a potential drug target.S-adenosylmethionine synthetase (SAMS) is a key enzyme for the synthesis of the lone methyl donor S-adenosyl methionine (SAM), which is involved in transmethylation reactions and hence required for cellular processes such as DNA, RNA, and histone methylation, but also polyamine biosynthesis and proteostasis. In the human malaria parasite  The S-adenosylmethionine synthetase SAMS, also called methionine adenosyltransferase (MAT), is an important enzyme found in all living organisms that catalyzes the synthesis of S-adenosyl methionine (SAM) from methionine and adenosine triphosphate (ATP). SAM is the sole methyl donor in cellular processes such as the methylation of histones, DNA, and RNA, which are important in the regulation of transcription and translation . SAM is P. falciparum, the causative agent of malaria tropica, being the most serious form. The life cycle progression of P. falciparum in the human, as well as the mosquito vector, requires tight regulation of various cellular processes, including transcriptional and translational regulation, or protein turnover control, but also membrane biosynthesis and modification. PfSAMS is encoded by a single gene, and its functionality and druggability have previously been shown ; Pf92 [PF3D7_1364100]; Pfs230 [PF3D7_0209000]; PfAldolase [PF3D7_1444800]; PfAMA1 [PF3D7_1133400]; PfFalcilysin [PF3D7_1360800]; PfFNPA ; histone H3 [PF3D7_0610400]; PfSAMS [PF3D7_0922200].The following PlasmoDB gene identifiers (gene IDs) ; mouse anti-GFP antibody (Roche); mouse polyclonal antisera against Pf39 ; rabbit   found in this subnetwork is a DNA replication initiation protein that can complement yeast Orc2. PfOrc2 is known to be processed in the endoplasmic reticulum and trafficked to the nucleus through the classical secretory pathway in P. falciparum. The nuclear import of PfOrc2C depends on importin-α/β, also identified in the PfSAMS interactors [A small subnetwork of proteins that are putatively involved in DNA replication and repair has been identified. At least eight proteins of the mini-chromosome maintenance complex-binding proteins (MCM) and helicases show interconnection in this subnetwork with no clear link to the other clusters . Origin eractors . Other mPfSAMS interactors, based on the data mined in the String database. A PPI enrichment p-value of 3 × 109 was computed with a higher number of edges (4024) in the interactors than expected (3666) on random distribution and average local clustering coefficient of 0.327, indicating that the identified proteins have more interactions among themselves than would be expected for a random set of proteins of the same group size and degree distribution drawn from the P. falciparum 3D7 genome.The identified interaction partners show a prediction of interaction in a large network and subnetworks only among themselves without the intermediary of other proteins that do not belong to the query list of identified PfSET1, PfSET3, PfSET6, PfSET7, and PfSET9, as well as the three protein arginine methyltransferases PfPRMT1, PfPRMT5 and PfCARM1 [PfSAMS with various DNA-directed polymerase subunits, transcription elongation factors, and translation elongation factors as well as with the enzyme PfPMT that is important for alternative PC synthesis , which is important for PC synthesis during sexual commitment, the combined data suggest a crucial role of PfSAMS during the initiation of gametocytogenesis.Selected interactors were further grouped by biological functions . A high  PfCARM1 . In addiynthesis . Importaynthesis . In addi  DNA replication and repair; (2) transcriptional regulation and chromatin modeling; (3) translational regulation and protein synthesis; (4) protein processing and proteasome regulation; (5) PC metabolism; and (6) polyamine synthesis. Noteworthy, the clusters themselves are arranged in various sub-clusters, and the presence of multiple clusters and sub-clusters within the interactome suggests that PfSAMS synthesizes SAM in locally confined subcellular compartments or microbodies rather than producing SAM that would be freely trafficked to other compartments as previously thought.GO, KEGG, and String network analyses as well as functional classification strategies assigned PfSAMS to epigenetic control was to be expected since methylation of histones, particularly H3, as well as of DNA and RNA has previously been reported for the asexual and sexual blood-stage parasites [PfSAMS with five of the ten known PfSET proteins, i.e., PfSET1, PfSET3, PfSET6, PfSET7, and PfSET9, as well as with ribosomal RNA methyltransferases and DNA methyltransferase-associated proteins was confirmed. In addition, an interaction between PfSAMS and AP-2G, the transcriptional regulator of gametocyte commitment, was detected. In non-committed parasites, ap2-g expression is repressed by H3K9me3 methylation and HP1 binding, while during sexual commitment, the ap2-g locus is activated by HP1 release, a process promoted by GDV-1 [The link of arasites ,81,82,83by GDV-1 ,38,39.PfSAMS appears to interact with multiple ApiAP2 proteins in addition to AP2-G, including AP2-I, AP2-L, AP-O5, AP2-EXP, and the AP2/ERF domain-containing protein. AP2-I has been linked to binding to genes coding for proteasome complex subunits [var genes and hence to play a role in the virulence of the blood-stage parasites. RNA-Seq analysis in AP2-EXP mutant parasites revealed transcriptional changes in a subset of exported proteins encoded by clonally variant gene families characterized with an upregulation of RIFINs and STEVORs at the protein levels, highlighting the importance of the non-DNA-binding AP2 domain in functional gene regulation [P. berghei, AP2-L was further demonstrated to be important for the development of liver-stage parasites [P. yoelii, Pyap2-o5, plays a role in ookinete motility and early oocyst development [Noteworthy, subunits . On the gulation . Using tarasites , while telopment .PfSAMS in the parasite nucleus. Such direct interaction of SAMS enzymes with DNA-binding proteins has been suggested to occur in higher eukaryotes as well. For example, human SAMS serves as a transcriptional corepressor by directly binding DNA-binding proteins such as MafK and Bach1, which recognize the Maf recognition element (MARE) upstream of the nucleosome, thus recruiting methyltransferases and locally producing SAM, which is needed for histone methylation at the downstream nucleosome [The biotinylation of histone methyltransferases and transcription factors suggests their close location to cleosome ,89.PfSAMS with AP-2G suggests its involvement in gametocyte commitment, this event is particularly triggered by the lack of lysoPC in the medium. In fact, lysoPC deficiency forces the blood-stage parasites to synthesize PC by triple-methylation of phospho-ethanolamine using ethanolamine [PfPMT, another interaction partner of PfSAMS. In accord with these findings, lack of lysoPC in the medium results in increased PfPMT expression and decreased SAM levels, while PfPMT deficiencies increase intracellular SAM levels and repress sexual commitment, as has been described recently [While the interaction of nolamine ,43, and recently .PfSAMS in translational regulation and proteostasis, among others, by interacting with proteasome components. While the role of PfSAMS for protein degradation in malaria parasites has so far not been investigated, several studies in other eukaryotes point to a direct link of SAMS in regulating proteasome function via subunit methylation [PfSAMS with translational elongation factors and ribosome components suggests its direct involvement in these processes, for example, by direct methylation of ribosomal RNA, as has been reported in other organisms [The network analysis further reveals the involvement of hylation . In othehylation . Hence, hylation ,93,94, thylation . The intrganisms ,97,98.PfSAMS in this process. AdoMetDC/ODC has been shown to be important in male gametocyte development and transmission in P. yoelii [P. falciparum in vitro causes cytostatic arrest in the trophozoite stage of the asexual blood cycle, but it does not cure P. berghei infected mice in vivo [P. falciparum results in a generalized transcriptional arrest due to polyamine depletion as well as perturbation-specific compensatory transcriptional responses, including the decreased abundance of pfsams transcript [PfSAMS in polyamine synthesis.We also identified the single bifunctional enzyme AdoMetDC/ODC, which is the rate-limiting enzyme in polyamine biosynthesis, in our interaction network, indicating the involvement of . yoelii . Its inh in vivo ,101,102.anscript . AdditioPfSAMS, which generates the vital methyl donor SAM and thus represents a bottleneck of the parasite life cycle for the asexual and sexual blood stages of P. falciparum. We show that PfSAMS is crucial for the parasite through its involvement in a complex network with other proteins of various functions. Our interaction network analyses confirm the link of PfSAMS to epigenetic control via methylation of histones, DNA, and RNA, but also due to potential direct interactions with transcription factors. Our data further point to important functions of PfSAMS in PC and polyamine synthesis and in translational control as well as regulation of the proteasome. These newly identified important roles of PfSAMS for parasite viability emphasize the enzyme as a promising antimalarial drug target.Our combined data confirm the vital role of the single enzyme


Advanced computational methods exploit gene expression and epigenetic datasets to predict gene regulatory networks controlled by transcription factors (TFs). These methods have identified cell fate determining TFs but require large amounts of reference data and experimental expertise. Here, we present an easy to use network-based computational framework that exploits enhancers defined by bidirectional transcription, using as sole input CAGE sequencing data to correctly predict TFs key to various human cell types. Next, we applied this Analysis Algorithm for Networks Specified by Enhancers based on CAGE (ANANSE-CAGE) to predict TFs driving red and white blood cell development, and THP-1 leukemia cell immortalization. Further, we predicted TFs that are differentially important to either cell line- or primary- associated MLL-AF9-driven gene programs, and in primary MLL-AF9 acute leukemia. Our approach identified experimentally validated as well as thus far unexplored TFs in these processes. ANANSE-CAGE will be useful to identify transcription factors that are key to any cell fate change using only CAGE-seq data as input. TFs can bind to specific cis-regulatory elements, such as enhancers and promoters, controlling transcriptomic machinery. Upon DNA binding, TFs can induce complex and dynamic cellular processes, ranging from cell fate determination, cellular reprogramming and cell cycle progression, to disease progression, and carcinogenesis2.DNA-binding proteins known as transcription factors (TFs) have crucial roles in gene expression regulation4. Also in leukemogenesis TFs have a major role. Acute myeloid leukemias (AMLs) are often plagued by recurrent mutations involving proteins with DNA binding properties5. Prime examples are EVI1 (MECOM), CEBPA, RUNX1, and HOXA98.The powerful regulatory nature of TFs is exemplified by lineage-specific hematopoiesis; tight control of lineage-determining TFs will direct precursor cells to specific mature lineages. For example, GATA-1/2 or GFI-1B are essential for megakaryocyte/erythrocyte development, and SPI1 for myelopoiesis9, predicting mammalian TFs key to cell fate determination is still challenging. The cell’s constituents, such as DNA, RNA, and proteins, form complex regulatory networks and cannot be understood correctly if only discrete individual components are examined. To construct a reliable TF prediction model, the cell’s molecular state needs to be interrogated on a multitude of -omics disciplines. So far, different approaches have been developed to predict key TFs, some are either based on  gene expression11 and/or co-expression information12. Other include state-of-the-art integrative strategies, such as integrating TF binding site information13. Unfortunately, systematic chromatin immunoprecipitation sequencing (ChIP-seq) for every TF in every cell type is a laborious and, at present, an unrealistic task. Predicting TF binding is therefore an excellent alternative to uncovering key TFs governing cell fate.Despite major advances in computational biology14, even more so when incorporating TF binding profiles at enhancer regions15. For example, enhancer GRN-based method Analysis Algorithm for Networks Specified by Enhancers (ANANSE) is demonstrated to be a powerful tool for studying regulatory mechanisms. However, in order to use ANANSE to its full potential RNA-, ATAC-, and ChIP-seq (H3K27ac) data is required for at least two cell types.Cutting edge TF prediction incorporates (predicted) transcription factor binding sites and interactions between TF and their downstream target gene(s), also known as Gene Regulatory Networks (GRNs). Collectively, (predicted) TF binding site and GRN information can be used to predict key TFs in one cell state compared to another. This control in comparison design has been shown to be valuable in TF prediction16. The method of capturing 5’-end steady-state capped RNAs identifies mRNA transcription start sites (TSSs), including non-characterized alternative TSSs, which are often tissue- and cell type-specific17; TSSs of long non-coding RNAs (lncRNAs); promoters accompanied by upstream antisense RNA (uaRNA); and enhancer RNAs (eRNAs). The latter two are characterized by bidirectional transcription19.Ideally, we want to reduce the necessity of multiple experimental setups while improving TF prediction by integrating information based on cis-regulatory elements, such as promoters, promoter-proximal regulatory elements, or enhancer elements, especially, when biological material is limited. One technique that uncovers a wide range of central gene regulatory elements is Cap Analysis Gene Expression sequencing (CAGE-seq)16 (ANANSE-CAGE). We demonstrated a strong correlation between bidirectional transcription and cis-regulatory elements, and its use in predicting cell type-specific TF binding profiles. Supervised modelling was used to improve TF binding prediction and cell conversion simulations were used to validate our findings. Next, we identified key TFs during induced erythroid differentiation, myeloid differentiation, and we predicted TFs important for immortalisation of MLL-AF9-driven leukemic cells. Subsequently, we predicted key TFs unique in either cell line- or primary- associated MLL-AF9-driven immortalisation, and key TFs in primary AML. The results demonstrated the wide use case of ANANSE-CAGE for studying development and pathology of haematological systems and its potential for uncovering novel key TFs and developing hypotheses.Here, we created an open source computational workflow that incorporates the enhancer network-based method ANANSE with the CAGE-seq analysis framework called CAGEfightR5’-end capped RNA capturing methods, such as CAGE-seq, quantifies two types of transcription initiation events: one is defined as unidirectional transcription initiation, associated with Transcription Start Sites (TSSs), and the other is classified as bidirectional transcription initiation, which mark enhancer-associated regions Fig. a.Figure 16. TCs manifesting gene expression were identified by an array of transcription events on the same strand, the expression of which was profiled to confirm this unidirectional transcription pattern  into Tag Clusters (TCs) using a slice-reduce approach in the myeloid leukemia cell line K562Next, we investigated the various genomic loci that were accompanied by these bidirectional sites. In total 2,182 regions adjoined its nearest gene with an absolute distance between 0 and 5 kb  and histone H3 lysine 27 acetylation (H3K27ac), and bidirectional transcription regions. Nearly all bidirectional regions were either associated with both H3K4me1 and H3K27ac or H3K27ac alone ; TF motif z-scores; average binding signal of TFs across cell types from the curated TF ChIP-seq database ReMap 202223, keratinocytes24, astrocyte derived from the cerebral cortex25, human induced pluripotent stem cells (hiPSCs)27, and hepatocytes29. In almost every case, we predicted the entire set of experimentally validated TFs  were amongst the top predicted TFs, confirming our predictions  and five different conversion cell types, including macrophages33. Similarly, these methods rank predicted TFs, though with a different algorithm or type of data. We included three different performance metrics to confirm performance in five conversion strategies . First, by determining the fraction of  TFs ranked in the top 10 highest scoring TFs per cell conversion strategy, we demonstrated the methods ability to rank (confirmed) important TFs. ANANSE-CAGE outcompeted nearly all other reported methods in this regard , MLXIP (late), FOXK2 (late-definitive), and RFX7 (definitive). Interestingly, GWAS studies suggest HIVEP1, MLXIP, FOXK2, and RFX7 to affect red blood cell phenotypes38. Together, we observed predictions that not only correlate well with known literature, it also provides a rich and instructive set of factors potentially essential for erythropoiesis that can be further explored.To identify key TFs that drive erythroid differentiation, we applied ANANSE-CAGE to three stages of K562 hemin induced differentiation program: early 3.5 h), late (24 h) and definitive (96 h)  is capable of interacting with MLL-AF9, sustaining leukemogenic potential of MLL-AF9 leukemic cells. Pharmaceutical inhibition of LSD1 results in induction of differentiation in these leukemic cells. This inhibition leads to monocyte differentiation accompanied with upregulated CD86 expression, a myeloid differentiation marker40. To validate the LSD1 inhibition, we first quantified CD86 expression40. Using RNA-seq and flow cytometry we observed a steady increase in CD86 expression over the course of 24 h  and in early myeloid differentiation (treated condition). First, TFs that drive normal myeloid differentiation were determined. For this, we used a GSK-LSD1 inhibitor to induce myeloid differentiation in MLL-AF9 positive THP-1 cells4 h Fig. a-b, in lP < 0.05, log2 fold change > 2) after GSK-LSD1 treatment and only 3 downregulated genes . Despite the few differentially expressed genes (log2 fold change > 2) we were able to confirm significant higher CD86 expression upon GSK-LSD1 treatment, even at this very early timepoint. Importantly, CAGE-seq allowed the identification of bidirectional transcription sites  and wild-type THP-1 (target type) Fig. . Between45. Furthermore, we predicted MLL-AF9 targets FOS, FOSB46, and KLF6. The latter of which has not yet been described in MLL-AF9 AML. Second, we performed ANANSE-CAGE to predict TFs unique to THP-1 by comparing GRNs between primary samples (source type) and THP-1 cells (target type). Surprisingly, most of the top ranked factors were associated with neural and craniofacial development  .Importantly, our method does not require a large set of samples or multiple genome-wide measurements to provide high prediction performance. It does not depend on large collections of reference data, making it a profitable method to use with new data. The rich epigenomic and transcriptomic landscape that is important in understanding hematological disease can be examined using just a single transcriptome-based Next Generation Sequencing technique. The framework is widely applicable to other cell systems and organisms. ANANSE-CAGE is open-sourced and available to expand upon  for 8 h and RNA was isolated using Mini RNA Isolation II Kit (Zymo Research).The leukemic cell line THP-1 (ATCC) was maintained in RPMI 1640  supplemented with 10% heat-inactivated fetal calf serum (FCS) at a density between 2E5 and 1E6. The cells were kept in a humidified incubator at 37 °C in 5% COLibrary generation was performed on 500 ng RNA (singlicate) using KAPA RNA HyperPrep Kit with RiboErase (HMR) (Kapa Biosystems) with RNA fragmentation of approximately 300 bp fragments for 6 min at 94 °C. Library size distribution was measured using High Sensitivity DNA analysis (Agilent) on an Agilent 2100 Bioanalyzer and its corresponding software. Libraries with average sizes with approximately 300–400 bp were used for sequencing with a NextSeq 500 system (Illumina).https://github.com/vanheeringen-lab/seq2science) .The fastq files (GSE204710) were mapped to the reference human genome hg38 through the Seq2science pipeline . Genome coordinates were converted between assemblies using UCSC’s utility program liftOver.CAGE libraries were prepared using 2 µg total RNA from wildtype and GSK-LSD1 treated THP-1 cells and from blast cells of three MLL-AF9 AML patients. Kabushiki Kaisha DNAFORM performed CAGE sequencing on an Illumina system and basic data pre-processing. Fastq files  were mapped using the BWA and HISAT2 aligners to hg38. Post-mapping processing involved converting BAM to CTSS BED files (https://fantom.gsc.riken.jp/5/sstar/Browse_samples)53. Publicly available ChIP-seq data was downloaded from the ENCODE portal (https://www.encodeproject.org)22. All datasets (hg19) are summarized in Supplementary Table CAGE-seq data for GM12878, HeLaS3, HepG2, H1-hESC, K562, fibroblasts (skin), astrocytes , hepatocytes, human iPSCs, keratinocytes, macrophages, K562 Hemin induced time course, and primary CD14 + monocytes were obtained from the FANTOM5 collection , pooled, and pre-filtered with default settings. Subsequently, tag clusters were quantified using a slice-reduce method. Tag Clusters (TCs) expressed in at least 2 or more libraries were kept and noise or weakly expressing TCs were discarded (> 1 TPM). CTSSs within a range of 20 bp on the same strand were systematically clustered into unidirectional clusters. Bidirectional transcription was established using a balance threshold of 0.95 and a window size of 201 bp. Unidirectional TCs were summed per gene annotation (TxDb objects) and used for differential expression analysis (DESeq2)58. For more details, a R markdown script is freely available at https://github.com/vanheeringen-lab/ANANSE-CAGE.CAGE tags were analysed with the R package CAGEfightR v1.14.015. To train the models we used bidirectional regions for 230 TFs from ReMap 2022 in five cell lines from FANTOM5: GM12878, HeLaS3, HepG2, H1-hESC, and K56221. Only factors defined in Lovering et al. were used59. The window size was normalized to 200 bp, centred on the middle of each bidirectional region. Bidirectional TPMs were log-transformed and quantile normalized. To evaluate the model training, including the general model, we performed cross-validation by held-out chromosomes and cell types, respectively. PR AUC scores were calculated using only enhancers located on held-out chromosomes in held-out cell types. For unavailable TF data, we trained a general model so that we can implement the model training for all TFs.The model training for bidirectional regions is based on the model training of ANANSE for H3K27ac and ATAC-seq data, described previously by Xu et al.60, was used to predict TF binding using three types of data: enhancer intensity score in TPM , TF motif scores, and the average ReMap 2022 ChIP-seq coverage21. Equation , and influence scores were calculated as previously described by Xu et al.62. Elbow method was used to determine the optimal value of k in k-means clustering  by comparing the respective timepoints to timepoint 0 h. K-means clustering was performed ComplexHeatmapSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.


Whereas MAB_2248c has 51.6% similarity to M. tuberculosismbtE, the other gene, MAB_2122, has 55% similarity—although the similarity between the two is only 42.8%. Genes around MAB_2122 are also designated mbt genes  between them, into M. abscessus (ATCC 19977) bearing plasmid pJV53, used for recombineering (Zeor (600) of 0.2, washed them twice in phosphate-buffered saline (PBS), diluted them to a concentration of ~2,500 CFU/μL, and spotted 2 μL onto 7H9-glycerol plates supplemented with either ADS, mycobactin J, hemin, or no supplementation . Although all three genes appear to share significant homology, and by length MAB_2122 is actually closer to Rv2380c than MAB_2248c, when we used the Clustal Omega online tool for protein sequence homology . In both MAB_2248c and Rv2380c, the program predicted the existence of a promoter in the (−45) to (−10) bp region, with high homology between the two predicted promoters. In contrast, in MAB_2122, no promoter was predicted at close proximity to the start codon, and the closest predicted promoters were found as far away as (−110) bp from the start codon. Of note, if indeed MAB_2122 has a poorly defined promoter (if any), this may also explain why in our previous work  under iron-limiting conditions.Additionally, we performed a promoter prediction analysis on the (−250) bp sequence for MAB_2122, MAB_2248c, and Rv2380c, using the online tool PromoterHunter  resulted in substantial growth retardation on iron-poor media, the deletion of MAB_2122 did not appear to have the same effect, and bacteria grew apparently normally on media that did not support the growth of Tn_2248c. The role of MAB_2122 could therefore be either negligible or completely compensated by MAB_2248c, whereas MAB_2122 could not compensate for the loss of MAB_2248c to any substantial effect. In our previous experiment, iron deprivation caused an increase in the transcription of MAB_2248c but had no effect on the transcription of MAB_2122, which is in agreement with the apparent lack of an obvious promoter region for this gene. Whether the duplication of mbtE resulted in one active and one inactive enzyme or whether the differences were related to regulation (and activity of MAB_2122 under specific conditions that we did not identify) remains unclear. Coevolution of transcriptional and enzymatic regulation in what appears to be duplicate genes has been described before (M. abscessus is, in contrast to that of MAB_2248c, negligible.To summarize, d before . However


N6-methyladenosine modification (m6A) fine-tunes RNA fate in a variety of ways, thus regulating multiple fundamental biological processes. m6A writers bind to chromatin and interact with RNA polymerase II (RNAPII) during transcription. To evaluate how the dynamics of the transcription process impact m6A deposition, we studied RNAPII elongation rates in mouse embryonic stem cells with altered chromatin configurations, due to reductions in linker histone H1 content. We found that genes transcribed at slow speed are preferentially methylated and display unique signatures at their promoter region, namely high levels of histone H1, together with marks of bivalent chromatin and low RNAPII pausing. They are also highly susceptible to m6A loss upon histone H1 reduction. These results indicate that RNAPII velocity links chromatin structure and the deposition of m6A, highlighting the intricate relationship between different regulatory layers on nascent mRNA molecules. N6-methyladenosine (m6A) is one of the most prevalent internal modifications on mammalian mRNA, regulating many aspects of its fate, including pre-mRNA splicing, alternative polyadenylation, mRNA decay, or translation, with fundamental implications in a variety of physiological processes. m6A is installed co-transcriptionally by a multi-subunit writer complex, composed of the methyltransferase-like 3 (METTL3), methyltransferase-like 14 (METTL14), Wilms-tumor-1-associating protein (WTAP), and the factors RBM15/RBM15B, VIRMA, ZC3H13, and HAKAI  from its paused state [Interestingly, recent work has shown that m6A also regulates the transcription process, per se. For example, METTL3 is recruited to promoters of highly expressed genes in ed state . Also coed state . Togetheed state ), since ed state . In part3 U/mL LIF , 100 U/mL penicillin , and 100 µg/mL streptomycin , at 37 °C and 5% CO2.Mouse embryonic stem cells were grown in DMEM , supplemented with 10% fetal bovine serum , 1 × non-essential amino acids , 1 mM sodium pyruvate , 2 mM L-glutamine , 50 µM b-mercaptoethanol , 106 reads on average per library). Then, the 0- and 5-min time-point libraries were sequenced at a higher depth to calculate RNAPII elongation rates (70 × 106 reads on average per library).Nascent transcription labeling assays were carried out as previously described ,11. BriebamCoverage function from deepTools [makeTagDirectory function. Histograms were computed with annotatePeaks.pl, setting the following parameters: −size 52000 −hist 20 −ghist −d, and, as inputs, the generated tag directories and the mouse reference protein-coding transcriptome, including positions transcription start sites (TSS) − 2 kb and TSS + 50 kb. This reference transcriptome was obtained from RefSeq and curated to include the longest version of each transcript, resulting in a list of 20,621 protein-coding genes that was used in the rest of the analyses.Reads were aligned to the mm10 reference genome using Bowtie2  and staneepTools  and loadeepTools  and the processesfind_boundary_4sUDRBseq.m script [In order to obtain the transcription elongation rate information, sequencing alignments were processed using Matlab and a previously published protocol ,16, withm script  estimateg for 5 min at 4 °C, resuspended in homogenization buffer  at a concentration of 2 × 106 cells/mL and incubated on ice for 3 min. Nuclei were then layered on top of a 3.5 mL sucrose cushion (homogenization buffer and 10% sucrose), and centrifuged for 20 min at 900× g and 4 °C. The nuclei pellet was resuspended in ice-cold wash buffer  at a concentration of 2 × 106 nuclei/mL and divided in 1 mL aliquots. Two of these aliquots were treated with 0 and 600 units of MNase  after adding 1 mM CaCl2 for 6 min at 25 °C. Enzymatic reactions were stopped by adding 9 mM EDTA and 3.5 mM EGTA and placing the samples at 4 °C. The fragments corresponding to the mononucleosomal fraction  were purified from agarose gels using a Speedtools PCR Clean Up Kit  following the manufacturer’s instructions. Sequencing libraries were generated with the NEBNext DNA Library Prep Kit  and sequenced in 2 × 75 bp paired-end reads at Fundación Parque Científico de Madrid.Subconfluent cells were washed twice with PBS and crosslinked by a 10 min incubation with PBS + 1% formaldehyde at room temperature. Crosslinking reactions were stopped through the addition of 125 mM glycine and additional rocking for 5 min. Cells were collected by scrapping in 2 mL ice-cold PBS and protease inhibitors , collected by centrifugation at 180× p-value cut-offs (0.1 to 5 × 10−5), and the number of nucleosomes showing increased fuzziness was calculated and plotted in WT and H1-TKO. Nucleosome profiles were generated with NUCwave [Paired-end reads were aligned to the mm10 reference genome using the bwa mem algorithm. Only fragments delimited by two paired reads mapping the same chromosome and separated by less than 250 bp were considered for the analysis; those fragments were trimmed 25 bp at both ends and used as input for the DANPOS software , which c NUCwave .CheRNA data was downloaded from Fernández-Justel et al. . Reads wTKOs is the spike-in normalized RNAPII signal and TKOr is the total reads normalized RNAPII signal. Reads were normalized by this correction factor, the total number of aligned sequences for each experiment, and the transcript sizes. Means were computed for both replicates in each cell type. To obtain the RNAPII pausing index for each gene, we calculated the ratio between RNAPII occupancy in the promoter and in the gene body.RNAPII ChIP data was downloaded from Fernández-Justel et al. . Reads wbedtool genomecov function. Aligned BAM files were merged with the reference mouse transcriptome of 26,021 genes. Since the m6A mark has been mainly reported around the TTS, we used a modified version of the transcriptome list that included the TSS − 2 kb and the TTS + 2kb positions as coordinates. Sequencing reads were normalized by the total number of aligned sequences in each experiment. For the quantification of m6A methylation levels, the number of reads between the MeRIP samples and the CheRNA input were computed per transcript. After excluding genes with no data in one of the two replicates, means were finally calculated for both replicates in each cell type.MeChRIP data was downloaded from Fernández-Justel et al. . Reads wPairwise comparisons between cell types and/or elongation rate groups were performed in R using the Wilcoxon and Mann-Whitney test. Correlations among variables were calculated based on Spearman’s ρ or Pearson’s r correlation coefficients in R. Mean, median, and standard deviation information of the analyzed distributions are detailed in p-value was computed for biological processes and molecular functions and the 20 terms with the lowest p-values were selected.GO-term enrichment analyses were performed using Panther v17.0 software . −Log10 macs2 according to the parameters set in [Peak-calling was performed using s set in . Both biThe datasets of DRB-TTseq and MNase-seq experiments generated in this work were deposited in GEO: GSE213270. Datasets from CheRNA, RNAPII-ChIP, and MeChRIP sequencing experiments in WT and H1-TKO mESCs were generated in our laboratory and deposited in GSE166426 . H3K4me3https://github.com/aliciagallego/rates_m6A accessed on 31 July 2022.All scripts used in this work are publicly available at the GitHub repository: p-value = 9.22 × 10−10), while medium and fast genes are transcribed significantly faster . The increased elongation rates of medium and fast genes occurring in H1-TKO mESCs might be related to the increased fuzziness of nucleosomes when linker histones become limiting on chromatin . Moreover, slow-elongating genes displayed a lower pausing index , than the other two groups . Importantly, reduction in histone H1 content did not result in statistically significant differences in any of the above comparisons, suggesting that the relationship between low RNAPII pausing, slow elongation speed, and low nascent mRNA levels occur regardless of the chromatin alterations mediated by H1 depletion.To relate different parameters of the transcriptional cycle with the RNAPII elongation speed, we first integrated genome-wide datasets of RNAPII occupancy (RNAPII ChIP-seq) and chromatin-enriched RNA production (CheRNA-seq) recently generated by us in both cell types  Figure A,B. We fo groups E, indicap-value = 3.7 × 10−7 for WT; and ρ = −0.03, p-value = 0.032, for H1-TKO, respectively), nor when analyzing a reduced dataset of elongation rate measurements derived from a related WT mESCs line . Thus, p-value = 9.3 × 10−17 and p-value = 7.91 × 10−18, for slow and medium genes, respectively). We conclude that slow-transcribed mRNAs display low RNAPII pausing and high m6A levels, and that decreasing histone H1 content in chromatin alters m6A deposition preferentially at this gene-rate class.We have previously reported that m6A levels on chromatin-RNAs were reduced in H1-TKO cells relative to their WT counterparts . InteresTo investigate the chromatin architecture at the promoter regions of the genes transcribed at different rates, we analyzed available ChIP-seq datasets of histone post-translational modifications related to transcriptional regulation, including H3K4me3, H3K9me3, and H3K27me3, from WT and H1-TKO mESCs . We alsop-value = 2.2 × 10−18 and p-value = 2.27 × 10−17 for the slow-medium and slow-fast comparison in H1c; and p-value = 2.35 × 10−12 and p-value = 1.22 × 10−11 for the slow-medium and slow-fast comparison in H1d, respectively). To relate histone H1 content and m6A reductions upon H1 depletion more directly, we sorted slow-transcribed genes by histone H1 promoter-proximal occupancies in WT cells and plotted the extent of m6A loss (MeChRIP H1-TKO/WT ratio) at the 684 genes common in both cell types (Since slow-transcribed mRNAs have higher m6A levels and also display the highest reduction upon histone H1 depletion, we hypothesized that their promoter regions will be marked by higher histone H1 occupancy in WT conditions. As predicted, we found that slow-transcribed genes displayed higher levels of the histone variants H1c and H1d around their TSS compared to medium and fast-transcribed genes B (Wilcoxll types C. IntereFinally, we tested whether, not only the total m6A levels, but also the relative distribution of m6A peaks along the gene bodies, vary between the three gene-rate groups D. We fouOverall, these integrative analyses suggest that the chromatin configuration imparted by high levels of histone H1 and H3K27me3/H3K4me3 at TSS-proximal regions leads to reduced RNAPII promoter-pausing and a slow elongation rate, together facilitating m6A deposition on nascent mRNAs encoding proteins critical for cellular and developmental processes.Pioneer studies by Slobodin and co-workers demonstrated that the deposition of m6A on mRNAs was linked to RNAPII transcriptional dynamics, which, in turn, affected mRNA translation. In particular, using RNAPII elongation mutants, they showed that a suboptimal rate of transcription resulted in higher m6A deposition on mRNAs and a tendency of lower translation efficiency . Our resDrosophila cells [In addition, slow-rate genes show limited RNAPII pausing at their promoter regions, which is in agreement with the finding that m6A RNA modification has a positive effect on RNAPII pause release in la cells . Their pla cells . Interesla cells . AltogetA surprising finding from our work is that slow RNAPII-transcribed genes are highly susceptible to m6A loss upon histone H1 reduction. Consistently, these genes have comparatively higher levels of linker histone H1 at their TSS than the other speed groups. It follows that the chromatin composition of bivalent promoters is marked by high levels of histone H1, which likely contributes to its repressed state in mESCs. In a recent study, we unveiled a novel role of histone H1 in regulating non-protein coding RNA transcription and turnover on chromatin in a m6A-mediated manner . Taking