Exogenous expression of Tcl1 in MEFs is sufficient to induce intra-genic promoters of epigenetic regulators and promote c-Myc expression. We further found a dramatic translation downregulation of transcripts bearing CNY cap-proximal trinucleotides, reminiscent of cells undergoing metabolic stress. These findings uncovered the role of Tcl1 oncogenic function in altering promoter usage and mRNA translation in leukemogenesis.The transformation of normal to malignant cells is accompanied by substantial changes in gene expression programs through diverse mechanisms. Here, we examined the changes in the landscape of transcription start sites and alternative promoter (AP) usage and their impact on the translatome in TCL1-driven chronic lymphocytic leukemia (CLL). Our findings revealed a marked elevation of APs in CLL B cells from Eµ-Tcl1 transgenic mice, which are particularly enriched with intra-genic promoters that generate N-terminally truncated or modified proteins. Intra-genic promoter activation is mediated by (1) loss of function of ‘closed chromatin’ epigenetic regulators due to the generation of inactive N-terminally modified isoforms or reduced expression; (2) upregulation of transcription factors, including  Tcl1 oncogene is highly expressed in CLL cells, and its expression level correlates with the CLL aggressiveness  (Chronic lymphocytic leukemia (CLL) accounts for 25–30% of all leukemias in the Western countries, with incidence rates ranging from 3.65 to 6.75 cases per 100,000 population per year . CLL is siveness . Tcl1 waeukemias . The cenEµ-Tcl1) . These mThe association of high TCL1 expression and malignant transformation is linked to its involvement in several major oncogenic pathways, including the enhancement of AKT activity ; interacThe transcription initiation of a specific gene can occur from more than one position in the DNA, creating several mRNA isoforms. Alterations in transcription start site (TSS) selection and alternative promoter (AP) usage increase transcriptome diversity and regulation. For example, the level of transcription initiation can vary between different TSSs under different growth conditions, in response to a specific signal, or different cell types and tissues. In addition, mRNA isoforms with different 5′ leaders can vary in their translation efficiency (TE) or half-lives. Likewise, AP usage can lead to the generation of protein isoforms that differ in their N termini and as a result can have different or even opposite biological functions. Recent large-scale promoter analysis in hundreds of human and mouse primary cell types shed light on the prevalence of AP usage in mammals  as well Tcl1 oncogene. Using healthy and Eµ-Tcl1 transgenic mice, we compared the TSSs landscape of normal and CLL B cells. Our findings revealed a marked elevation of APs in malignant CLL B cells with a particularly high prevalence of intra-genic promoters that are predicted to generate N-terminally truncated or N-terminally modified proteins. The induced cryptic promoters are driven by nearby enhancers and upregulation of their cognate transcription factors, including the c-Myc oncogene. A promoter shift in the opposite direction accounts for the induced expression of the immune checkpoint ligand PD-L2. Notably most of the intra-genic-generated transcripts are also efficiently translated. These remarkable changes of promoters and TSSs are linked to the loss of function of many ‘closed chromatin’ epigenetic regulators via induction of inactive isoforms by promoter shifts or by reduced expression, resulting in a feed-forward loop. The activation of intra-genic cryptic promoters and the c-Myc oncogene elevation are mediated, at least in part, by the Tcl1 oncogene itself and are therefore intrinsic to the CLL B cells. We further found a dramatic and specific translation downregulation of transcripts bearing CNY cap-proximal trinucleotides, reminiscent of cells undergoing energy stress. These findings explored the contribution of 5′ end mRNA isoforms and their associated translational changes during cellular transformation and uncovered novel Tcl1 oncogenic pathways.This study examined the changes in TSS selection and AP usage and their impact on the translatome during malignant transformation of B cells by the Tcl1 gene under the control of the immunoglobulin heavy chain (IgH) variable region promoter (VH) and immunoglobulin heavy chain enhancer (Eµ-Tcl1)  . Like thEµ-Tcl1) . SplenocEµ-Tcl1)  library Eµ-Tcl1) . An averEµ-Tcl1) , indicatEµ-Tcl1) . As expeEµ-Tcl1) . A signiEµ-Tcl1)  (exampleEµ-Tcl1) . We examEµ-Tcl1) . The abiEµ-Tcl1) . As no sEµ-Tcl1) , suggestEµ-Tcl1) . We founEµ-Tcl1) . LengthsEµ-Tcl1) . ConsistEµ-Tcl1) , generalEµ-Tcl1) . PromoteEµ-Tcl1) . SeparatEµ-Tcl1) .When an alternative TSS is located at the promoter or at the 5′ UTR regions, the original ORF is retained. However, new intra-genic TSSs located in introns or CDS are predicted to give rise to protein isoforms bearing alternative or truncated N termini and, consequently, potential different or even opposite biological functions (illustrated scheme in Pdcd1lg2/PD-L2). This gene has an intra-genic alternative TSS generating a protein isoform lacking the Ig-like extracellular domains of PD-L2 (Chd1), in which promoter shifting in Eµ-Tcl1 is within an intra-genic alternative TSS that results in truncation of several critical active domains, presumably leading to loss of function (Fbxo5) has two promoters located upstream to the main ORF differing only by their 5′ UTR length resulting in 490 nt shorter 5′ UTR in the Eµ-Tcl1-specific transcript isoform  analysis of genes affected by AP usage shows enrichment in critical CLL pathways, for example, NF-κB signaling , MAPK siHdac4, Hdac5, Sirt2, Smyd4, Kdm4a, Dnmt3a, Chd1, and Mgmt, are predicted to express truncated proteins that cause loss of critical domains due to DTU  or by environmental signals associated with cancer progression. To distinguish between these possibilities, we established three mouse embryonic fibroblasts (MEFs) clones stably expressing human TCL1, and determined by RT-qPCR the relative expression of the alternative intra-genic TSS of four selected genes (as shown in the scheme). We observed a significant elevation of the intra-genic truncating TSS of Ets2 and c-Myc correspond to the highly enriched Ets-related and bHLH motifs, respectively (Deregulation of the chromatin affects the accessibility of transcription factor (TFs) in promoter and enhancer regions. Since CAGE provides expression data of genes along with their promoter and enhancer regions, we investigated the contribution of TFs to the activation of cryptic promoters in Eµ-Tcl1. By generating position frequency matrices (PMSF) of TFBS motifs . Shown bectively .c-Myc oncogene is the hallmark of many hematopoietic malignancies , light polysomes occupying two to four ribosomes (Light), and heavy polysomes (Heavy) occupying over four ribosomes per mRNA transcript . The polNext, we examined the translatability of Eµ-Tcl1-specific transcript isoforms that arise from promoter shifts. By examining the CAGE data of total RNA and the polysome profiling fractions, we could determine the translatability of each isoform initiated by alternative TSS. Out of 543 DTUs found within the 5′ UTR, 142 were also differentially translated, of which 76 and 66 were transcriptionally up- and downregulated, respectively. Intriguingly, the vast majority of the transcriptionally upregulated DTU (60/76) are classified as High-TE, and most of the downregulated DTUs are classified as Low-TE (41/66) , indicatSmyd4, Sirt2, Sirt4, Hdac4, Hdac5, Kdm4a, Zcwpw1, and Wiz; DNA methylation factors: Dnmt3a and Mgmt and chromatin remodeling factors: Chd1, Smarca5, and Bcl7b. In addition, under ‘DNA/RNA synthesis’ GO, we could find Eµ-Tcl1-specific truncated isoforms of DNA repair genes such as Xrcc1, Recql5, and Tep1, and direct transcription regulators essential for hematopoiesis that includes Gfi1, Zbtb20, Ago3, Ssbp4, Ddx54, and Arntl (Dnmt3a leads to the loss of three exons (211 amino acids) that constitute its DNA-binding domain , a type III histone deacetylase (Hdac), interrupts the deacetylase domain positioned between residues 65 and 340 that generates a truncated isoform with higher translatability compared to the canonical isoform which is enriched in the polysomes fraction (Kdm4a) has an Eµ-Tcl1-specific cryptic transcription leading to a truncated isoform that is equally presented in polysome fractions as the canonical full-length isoform  and dramatic downregulation of Hdac11 and Hdac9 . When the count of intra-genic alternative TSS in the polysome fraction exceed 50% of the canonical TSS, we consider this intra-genic alternative TSS to substantially affect the function of the canonical full-length protein. In total, we found 73 intra-genic alternative TSS that are upregulated in Eµ-Tcl1 and exceeding 50% of the canonical TSS and 168 intra-genic alternative TSS which their lower translatability will less likely to impact protein functionality . GO analnd Arntl . These cg domain , which ig domain  and is gg domain . The Dnmimpaired , left. Afraction . middle.fraction . DTU fou isoform , right. Mdh2 gene, the reads of the two C in the CAC triplet are more enriched in the Free than the Polysomal fractions, while the opposite is seen for the reads that start with the A. The same trends are also seen in Psmb4 and Atp5f1, of which the (A)s are more highly translated than the Cs. As the 5′ UTR generated from these TSSs is almost identical, these examples highlight the important contribution of the first nucleotides to TE.The polysome-CAGE data enable the determination of the exact first nucleotide of every transcript and its impact on TE. We first determined the frequencies of TSS nucleotides in our Eµ-Tcl1 data and found that the frequency of A and G  is higher than the C and T , as expected . Yet, thA previous study that analyzed the relationship between the first nucleotide and the relative TE also reported that ‘C’ initiating transcripts are associated with lower TE, particularly following energy stress . We reanTo examine how CLL cells cope with the limited activities of epigenetic regulators, we isolated and cultured CLL and non-CLL B cells from Eµ-Tcl1 mice as well as B cells from healthy mice and treated them with inhibitors against the downregulated epigenetic factors HDAC3A, HDAC2A, KDM4A, and eIF4E in a dose–response assay. We found that the non-CLL or healthy B cells were inhibited by the HDAC2A and eIF4E inhibitors in a dose-dependent manner, whereas the CLL cells displayed reduced sensitivity . Interesc-Myc oncogene and several transcription factors that are specifically upregulated in Eµ-Tcl1 B CLL. Thus, the combined loss of epigenetic regulators and the induced expression of a specific subset of transcription factors underlie the dramatic TSSs and AP usage changes in Eµ-Tcl1 B CLL. Notably, these dysregulated gene expression features are most likely intrinsic to the Eµ-Tcl1 B CLL, as exogenous expression hTCL1 in MEFs is sufficient to drive the induction of intra-genic promoter of epigenetic regulators and to upregulate c-Myc.In this study, we used the Eµ-Tcl1 mouse model of CLL to investigate the global changes in TSSs and AP usage and their impact on the protein content of cells undergoing oncogenic transformation. By comparing the TSSs landscape of normal and CLL B cells, our findings revealed a dramatic induction of APs in malignant CLL B cells with a particularly high prevalence of intra-genic promoters predicted to generate N-terminally truncated or N-terminally modified proteins. Importantly, a major class of intra-genic APs activated in the Eµ-Tcl1 CLL cells is the ‘closed chromatin’ epigenetic regulators gene subset, predicted to generate a loss of function or dominant-negative isoforms. Enhanced chromatin accessibility due to downregulation of epigenetic gatekeepers is well known to promote transcription initiation from intra-genic cryptic promoters sites . Thus, iOur findings are consistent with a recent study that reported changes in promoter utilization in multiple cancer types . As thisA previous study described decreased methylation levels in Eµ-Tcl1 mice and in CLL patients and reported a strong interaction between TCL1 and de novo DNA methyl transferases DNMT3A and 3B along with inhibition of the enzymatic activity , suggestThe use of polysome-CAGE enabled us to assess the potential contribution of the N-terminally truncated/modified protein isoforms derived from intra-genic promoters to the functionality of the intact protein. This analysis reveals substantial impairments in several regulatory and metabolic pathways, including chromatin regulation and energy metabolism, that are expected to contribute to the transformed phenotype. Remarkably, upon analysis of the relationship of the exact cap-proximal nucleotides and TE, we observed that all ‘C’ initiating transcripts display lower TE compared to the A, G, and T. Furthermore, among the ‘C’ initiated transcripts, those that start with CNY display even lower TE relative to the other Cs. The effect of the ‘C’ on the TE is nicely demonstrated in the examples in which adjacent nucleotides from the same promoter region display dramatic differences in polysome occupancy . In thesConsidering the profound effect of TCL1 on multiple major regulatory pathways  and the Tcl1 to promote activation of intra-genic cryptic promoters of epigenetic regulators results in a feed-forward loop in which the loss of function of ‘closed chromatin’ inducers, by expression of N-terminally truncated isoforms, further enhance the activities of the cryptic promoter. This feature of Tcl1 and its ability to boost c-Myc oncogene levels expand the current knowledge on Tcl1 transforming capacities and its multiple modes of regulation. The utilization of the polysome-CAGE not only verified the expression of N-terminally modified protein isoforms but also uncovered the restricted protein synthesis capability of Eµ-Tcl1 B CLL as a point of vulnerability that can be exploited as a drug target. Our findings form the basis for future work on approaches to interfere in tumorigenic processes mediated by TCL1 in TCL1-overexpressing leukemias.In summary, the ability of C57BL/6wt were purchased from Harlan Biotech Israel . Transgenic Eμ-TCL1 mice  were kinHealthy and CLL splenic B cells were isolated from 1-year-old WT and Eμ-TCL1 mice by positive B-cell selection with CD19 magnetic beads . Briefly, spleens were squashed, treated with red blood lysis buffer (homemade) and filtered in phosphate-buffered saline (PBS) using 40-mm cell strainers. The cells were then counted and the B cells were purified according to the manufacturer’s protocol. Splenocytes subjected for polysome profiling were isolated using buffers (above) supplied with 100 µg/ml Cycloheximide (Sigma-Aldrich) to form 80S–mRNA complex.6 cells were washed with PBS supplemented with 0.5% bovine serum albumin and 0.1% sodium azide and stained with FITC-conjugated anti-CD5  and with PE-Cy7-conjugated anti-CD19  antibodies. Stained cells were washed and analyzed by flow cytometry , and the quantification of the measurements was analyzed using FlowJosoftware (TreeStar). The purity of WT mature B cells was estimated to be between 95% and 99%, and positive gated Eµ-Tcl1 B cells were estimated between 80% and 85%.Analysis of the surface expression of CD5 and CD19 on primary mature WT and Eµ-Tcl1 B cells was monitored by flow cytometry. Briefly, 1 × 102, and 100 µg/ml cycloheximide. The cells were collected and lysed with 500 µl of the same buffer that also contains 0.5% Triton, 0.5% DOC, 1.5 mM DTT, 150 units RNAse inhibitor , and 5 µl of protease inhibitor . The lyzed samples were vortexed and centrifuged at 12,000 × g at 4°C for 5 min. The cleared lysates were loaded onto 10–50% sucrose gradient and centrifuged at 38,000 rpm in a SW41 rotor for 105 min at 4°C. Gradients were fractionated, and the optical density at 254 nm was continuously recorded using ISCO absorbance detector UA-6. The collected samples were then pooled to create three main fractions: Polysome-free (Free), Light (2–5 ribosomes), and Heavy (over five ribosomes).Eµ-Tcl1 B cells were washed with cold buffer containing 20 mM Tris pH 8, 140 mM KCl, 5 mM MgClTotal RNA from WT and Eµ-Tcl1 purified B cells and the RNA from the pooled polysomal fractions (described above) were extracted in two biological replicates using TRIzol reagent  following the manufacture’s instructions. After phase separation, the aqueous phase was diluted back in TRizol reagent (1:3) to proceed with RNA purification by zymodirect RNA kit . The quality of RNA samples was assessed using Agilent 2200 TapeStation  to evaluate RNA integrity (RIN), and purity was measured by Qubit 4 fluorometer .D-sorbitol  at high temperature. Capped RNAs of RNA–DNA hybrids were Biotinylated  and purified by MPG streptavidin beads  followed by RNAse  digestion to release cDNAs corresponding to the 5′ ends of the original mRNA. 5′ linkers harboring barcodes sequences and EcoP15I  recognition site were ligated, and second-strand synthesis was performed. Then, fragments of the first 27 bp of the 5′ UTR were produced by EcoP15l digestion and ligated to 3′ linkers containing the Illumina primer sequence. The resultant CAGE tags were amplified by polymerase chain reaction (PCR), purified and sequenced by Hiseq 2500 (Illumina) with the addition of 30% PhiX spike-in to balance the low complexity of the 5′ ends of the CAGE libraries.Five µg RNA samples were subjected to library preparation of Cap Analysis Gene Expression CAGE using CAGE protocol adapted for Illumina sequencing . Specifi--very-sensitive preset , and the resulting SAM files were converted to sorted BAM files using SAMTools (1.9). BAM files of Light and Heavy polysome fractions were merged to form one BAM file representing all translated fractions, marked as Polysomes. BAM files were then loaded into the CAGEr package  and uniquely mapped tags were used for calling CTSSs setting parameters as follows: sequencingQualityThreshold = 20 and mappingQualityThreshold = 20. As part of CTSS mapping, CAGEr performed corrections for the CAGE-specific G-bias caused by template-free guanine incorporation upstream to the true TSS by the reverse transcriptase used for cDNA synthesis  and rRNA CAGE tags using Bowtie2 (2.3.5.1), demultiplexing and linkers trimming using FASTX-toolKit (0.0.13). Clean CAGE tags were aligned to the mouse reference genome (mm10) using Bowtie2 with the ynthesis . Metagenynthesis .Most of the CTSS analysis was performed using CAGEfightR package (1.10.0) 4, which uses several R packages from the Bioconductor project, v3.12. BIGWIG files that were exported by the CAGEr were loaded into CAGEfightR and closely spaced CAGE tags were clustered into unidirectional and bidirectional tag clusters (TCs) using the slice-reduce approach to define the global TSSs and enhancer candidates, respectively. After counting TCs with less than 1 TPM in the two Eµ-Tcl1 or WT CAGE libraries were removed in further analysis to remove very lowly expressed TC and to obtain likely biological relevance. Active enhancers predicated as balanced bidirectional transcription of capped eRNAs with a threshold of Bhattacharyya coefficient set to 0.95, where value of 1 correspond to perfectly balanced sites. TSS–enhancer physical interactions predicted by distance (50 kbp) and positive correlation  as previously shown by In order to know what transcription factors might be involved in the regulation of alternative TSSs, we downloaded DNA-binding motifs as position frequency matrices (PFMs) from the core collection of JASPAR database  and matcFor the 5′ RACE we used the template switching RT enzyme mix (NEB), which make use of the Template Switching Oligo (TSO) with known sequence followed by three RNA Guanidines. Using gene-specific RT primers  was added to the samples with anti-CD19 and anti-CD5 for 20 min in the dark on ice. Cells were washed twice with Annexin V Binding Buffer , then resuspended in 300 µl of Annexin V Binding Buffer with 7-AAD  Viability Staining Solution. Cells immediately were analyzed by flow cytometry.The survival of CLL B cells and healthy B cells was tested under the inhibitory effect of drugs targeting the activity of HDAC type II , HDAC type III , KDM4A , and DNMT3A . B cells isolated from Eµ-Tcl1 adoptive transfer and WT mice as described above. Triplicates of 2 million cells (per well) distributed in a 24-well tissue-culture plate treated with a serial four incremented concentrations of the above inhibitors (in DMSO). Following 48 hr, cells were washed with cold PBS, and resuspended in Annexin V Binding Buffer (BioLegend) at a concentration of 0.25–1.0 × 10Tcl1 cDNA was cloned in pCRUZ-HA expression plasmid (SantaCruz) using the restriction-free cloning method (g method  and cotrg method . After G Results presented in this article suggest that T-Cell Leukemia/Lymphoma 1 (TCL1) protein promotes alternative transcription site selection in chronic lymphoid leukemia, resulting in the production of N-terminally truncated versions of chromatin regulators and induction of expression of transcription factors including c-MYC. Finally, the authors provide evidence that TCL1 drives translational reprogramming akin to that observed under metabolic stress. Notwithstanding technical limitations that obscured direct detection of N-terminally truncated protein variants, it was found that this study is of broad potential interest inasmuch as it provides previously unappreciated evidence that TCL1 orchestrates epigenetic, transcriptional, and translational rewiring in leukemic cells. Based on this, the study by Ogran et al. should be of significant interest across a number of biomedical research disciplines ranging from regulation of gene expression to cancer research. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Ivan Topisirovic as the Reviewing Editors and Reviewer #1, and the evaluation has been overseen by Kevin Struhl as the Senior Editor.Thank you for submitting your article "Polysome-CAGE of TCL1-driven chronic lymphocytic leukemia revealed multiple N-terminally altered epigenetic regulators and a translation stress signature" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) It was found that the evidence of expression of putative N-terminally truncated proteins is required to fully support the proposed model. Notwithstanding that the overarching characterization of N-terminally truncated proteins across the proteome may be out of the scope of the paper, it was thought that the authors should at least provide evidence of in-depth-discussed proteins . Moreover, it was thought that orthogonal techniques  should be used to confirm the results of CAGE studies.2) Link between TCL1-dependent alterations in transcription site selection and/or promoter usage and chromatin remodelling was found to be largely correlative. Based on this, it was thought that the authors should provide more mechanistic data to support the role of apparent TCL1-dependent alterations in chromatin factors and perturbations in chromatin structure.3) As outlined in the individual reviews, some important issues were observed pertinent to bioinformatic analyses. It was also thought that several important controls were missing. For instance, a more detailed rationale should be provided for cut-offs/thresholds used in bioinformatic approaches. The reproducibility of the results between the two CAGE replicates was also not clear. An apparent lack of application of unique molecular identifiers in the CAGE approach was also observed. In addition, concerns were raised regarding the application of log-ratios to calculate translational efficiency. Finally, there was a lack of loading controls in some of the Western blots.4) In general, it was thought that the studies using epigenetic and translational inhibitors  do not show that the observed effects are TCL1-specific. Based on this, it was deemed that the authors should either provide additional data supporting the tenet that TCL1 allows adaptation to insults caused by pharmacological targeting of the epigenetic modifiers and/or translational apparatus or significantly temper claims in this section of their study.Reviewer #1 (Recommendations for the authors):- It was found that it would be crucial to test the expression of truncated proteins experimentally. It is understandable that it would be unreasonable to request this on a global scale, but these experiments should be carried out on a selected set of proteins that are mentioned in the study  to further support the author's model.- Although the polysome-CAGE approach to establish engagement of different 5'UTR isoforms on polysomes was highly appreciated, it was thought that the stoichiometry of ORF-truncated/retained vs. corresponding full-length proteins should be established for at least the subset of affected genes.- The cut-off of 50% for polysome engagement of a given 5'UTR isoform on corresponding protein function seems arbitrary. To this end, it was thought that the functional characterization of the proteins whose length may be affected by alternative promoter selections/transcription start site selection would be advantageous. The latter should provide some insights into the relevance of the alternative promoter usage/TSS selection to TCL1-driven phenotypes. This was thought to be important as is it remains unclear what is the contribution of proposed mechanisms to TCL1-driven phenotypes, whereby the prior evidence shows that TCL1 plays a rather pleiotropic function in CLL whereby it engages a number of oncogenic pathways. It is understandable that the full phenotypic characterization of the proposed model is likely to be out of the scope of this study, but the functional characterization of at least a few of the selected proteins appears to be warranted.- In line with the comment above, the link between TCL1-dependent ORF-retention and/or truncation of chromatin remodelers and alterations in chromatin appears to be purely correlative. The authors should either provide mechanistic evidence for the causative relationship between these phenomena  or tone down their claims pertinent for this part of the study.- There is no evidence that the observed effects of the inhibitors (figure 7) are TCL1-specific . These data are rather preliminary and insufficient to support the tenet regarding the adaptation of CLL B cells to epigenetic and translational reprogramming. Literature showing the potential effectiveness of targeting suggested pathways/factors in CLL  was also largely ignored. Moreover, the author referred to MNK1/2 inhibitor as eIF4E inhibitor, which was found to be inappropriate. This part of the study is significantly weaker than the rest, and the authors should consider either expanding it or substantially toning down their claims.- Some issues were noted pertinent to data analysis. For instance, the calculation of translational efficiency using log ratios that were previously demonstrated to be confounded by potential spurious correlations (PMID: 21115840). Addressing these issues along with providing more details related to the analyses that were performed throughout the study seems to be required.Reviewer #2 (Recommendations for the authors):1. Page 3 last paragraph starting with "The transcription initiation.…..opposite biological functions." Would benefit from referring to the literature for the different claims.2. On page 4, the feed-forward loop is introduced. This would benefit from being more clearly written.3. I likely missed this, but it was not clear to me which TSS was selected as the alternative. Was it the one close to the annotated transcription start site?4. On page 6 bottom paragraph "TSSs were enriched at the core promote…": what does the enrichment refer to? The length of these sequences? What were the odds-rations and the statistics for these enrichments?5. Figure 2G. It is claimed that truncating ATSS are more commonly upregulated in Tcl1 overexpressing cells. Can this be shown more clearly? Can one calculate statistics for this?6. On page 8 "TFBS frequencies mainly differ between….". But this is in component 2 which is not the main component.7. Figure 6E requires stats.8. The Results section "CAGE tag clustering, quantification and analysis" includes a few sentences lacking words.9. Is there an adjustment for the correlation p-values when performing TSS-enhancer analysis (described on page 18). It seems that false discovery rates rather than unadjusted p-values should be used.Reviewer #3 (Recommendations for the authors):Comments below are ordered following the flow of the manuscript:1) The authors perform total CAGE-Seq and polysome CAGE-Seq in duplicates. Please, show the correlation between duplicates.2) Figure 2D-F: Please, validate these alternative TSS usage examples by semi-quantitative PCR or Northern blot of wt versus Eµ-Tcl1 cells.3) Figure 3B: Please, provide the loading control for this Western blot, and an assessment of the level of over-expression compared to endogenous Tcl1.4) Figure 3B-C: To really assess whether changes in alternative TSS usage are due to Tcl1 over-expression, and not to environmental signals associated with cancer progression, the authors should perform CAGE-Seq in the MEF cell lines over-expressing Tcl1 that they have built and compare the overlap of detected TSS events with those found in B-cells. Right now, only 3 cases have been confirmed and a general conclusion cannot be reached.5) Figure 3C: Please, indicate the nº of independent biological experiments and replicates performed.6) Figures 4C-D: Please, provide supporting Western blots.7) Figure 5: The authors propose that Tcl1-mediated changes in TSS usage in chromatin modulators contribute to chromatin openness in Eµ-Tcl1 B cells. Can the authors directly test this by assessing global levels of histone acetylation or by performing ATAC-Seq of WT vs Eµ-Tcl1 B cells? Can the authors check the presence of stable, N-terminally truncated versions of some of the chromatin remodelers in Figure 5F by Western blot?8) Sup Figure 5C and differential TE explanation in the main text:The formula in Sup Figure 5C explains the concept of TE, but not of differential TE mentioned in the main text where a reference TSS (paired TSS) is claimed. From the drawing, it seems that differential TE is counted twice for any given two promoters, and this is confusing. What happens with genes containing more than 2 promoters? Which promoters do you compare with to decide on high or low TE? A reference promoter per gene should be chosen  and the data normalized to that reference.9) Figure 6E: Here the authors compare TSSs that start at almost the same location within a given gene, as they are only different by one nucleotide ('TSSs adjacent to each other'). How do the authors know that this one nucleotide differences are real, and not due to methodological CAGE-Seq errors? Why would such one nucleotide differences have profound effects on polysome association? Could this be an artifact of library preparation or bioinformatic treatment of CAGE-Seq? The conclusion that Eµ-Tcl1 cells are under metabolic stress just because of the similarities regarding 1st nucleotide choice between this study and that of ref 21 seems speculative.10) Figure 7: What can one conclude from the fact that inhibitors against 2 out of 3 of the epigenetic factors downregulated in Eµ-Tcl1 B cells have anyway no effect on the viability of B-cells no matter their origin? I'm not sure this figure tells much. Furthermore, controls that the drugs are really working  would be necessary. Essential revisions:1) It was found that the evidence of expression of putative N-terminally truncated proteins is required to fully support the proposed model. Notwithstanding that the overarching characterization of N-terminally truncated proteins across the proteome may be out of the scope of the paper, it was thought that the authors should at least provide evidence of in-depth-discussed proteins . Moreover, it was thought that orthogonal techniques  should be used to confirm the results of CAGE studies.As suggested, we now performed 5’ RACE to validate the CAGE results by picking four chromatin modifiers and examining the upregulation of the alternative TSS in Eu-TCl1 mice. The 5’ RACE was done using template switching RT enzyme mix (NEB), Template Switching Oligo (TSO) and a gene-specific reverse primer common to the canonical full-length isoform and the N’ terminally truncated one. In order to compare the transcription level of the truncated isoform between Eu-Tcl1 and WT mice, isoform-specific primers were used in the qPCR assay. The results of the 5’RACE-qPCR authenticate our CAGE-seq results, showing upregulation of the isoforms originating by alternative TSS of the four selected DNMT3a, CHD1, KDM4A and SIRT2 .Notably, we also confirmed that these isoforms are efficiently translated to proteins by combining CAGE with polysome profiling . Other proteomic approaches such as mass spectrometry (MS) and western blot for detecting the N-terminally truncated/modified proteins are less suitable. With the MS, the peptides derived from N-terminally truncated proteins are shared with the full-length protein. In western blot, there are several limitations that include the finding of high-quality antibodies directed against the C-terminus of the candidate proteins, the ability to distinguish between truncated and proteolytically cleaved protein, and the ability to predict the new translation initiation site accurately, the ORF and the size of the protein isoform. This is particularly challenging since, in most cases, the new promoters are located within an intron. Nevertheless, we performed multiple western blots by purchasing antibodies against the C-terminus of selected proteins, but we could not identify the truncated protein with high certainty.2) Link between TCL1-dependent alterations in transcription site selection and/or promoter usage and chromatin remodelling was found to be largely correlative. Based on this, it was thought that the authors should provide more mechanistic data to support the role of apparent TCL1-dependent alterations in chromatin factors and perturbations in chromatin structure.-1 promotes the activation of cryptic promoters is emerging. We briefly referred to this mechanism in the discussion of the original manuscript. In the revised manuscript, we further expand it with more specific details. Specifically, a previous study reported decreased DNA methylation levels in Eµ-Tcl1 mice and CLL patients and demonstrated an interaction between TCL1 and the de novo DNA methyl transferases DNMT3A and 3B along with inhibition of their enzymatic activity (1), suggesting for direct inhibition of de novo methylation by TCL1 during leukemogenesis. In addition, it was reported that TCL1 promotes the Α Serine/Threonine-Protein Kinase (AKT) activity. As AKT signaling itself reduces Dnmt3a activity , over-sensitization of the AKT by the constitutively overexpressed TCL1 is likely to reduce DNMT3A activity further. Our findings complement these observations by uncovering another mechanism of impairment of DNMT3A activity via activation of an internal promoter and generation of large amounts of truncated inactive protein . All these mechanisms inhibit DNMT3A and DNA methylation in these cells in an additive manner. We, therefore, propose that downregulation of DNMT3A directly and indirectly by TCL1 is the initial trigger for the activation of cryptic promoters in Eµ-Tcl1 CLL cells, which is further augmented by the activation of cryptic promoters of other chromatin regulators in a feed-forward loop and by activation of c-myc (see more details in the discussion).By combining our data with data from the literature, a mechanism by which TCL(1) Palamarchuk, A., Yan, P.S., Zanesi, N., Wang, L., Rodrigues, B., Murphy, M., Balatti, V., Bottoni, A., Nazaryan, N., Alder, H. et al. (2012) Tcl1 protein functions as an inhibitor of de novo DNA methylation in B-cell chronic lymphocytic leukemia (CLL). Proceedings of the National Academy of Sciences of the United States of America, 109, 2555-2560.Seminars in Cancer biology. Vol. 59. Academic Press, 2019.(2) Yang, Qi, Wei Jiang, and Peng Hou. "Emerging role of PI3K/AKT in tumor-related epigenetic regulation." Journal of Biological Chemistry 285.53 (2010): 41337-41347.(3) Popkie, Anthony P., et al. "Phosphatidylinositol 3-kinase (PI3K) signaling via glycogen synthase kinase-3 (Gsk-3) regulates DNA methylation of imprinted loci." 3) As outlined in the individual reviews, some important issues were observed pertinent to bioinformatic analyses. It was also thought that several important controls were missing. For instance, a more detailed rationale should be provided for cut-offs/thresholds used in bioinformatic approaches.As suggested, we now provide explicit reasonings for the bioinformatics cut-off/thresholds described in the method paragraph titled “CAGE tag clustering, quantification and analysis<milestone-start />״<milestone-end />. After counting CAGE reads into defined tag clusters (TC), we filtered out TCs with less than one tag per million (TPM) to remove very lowly expressed TC and obtain likely biological relevance. Where eRNA prediction is based on finding two balanced bidirectional (sense and antisense) TCs, we set the threshold of the Bhattacharyya coefficient (BC) to 0.95, where the value of 1 corresponds to perfectly balanced sites. In addition, when we measured the width of TCs of pooled CAGE tags, we used a 10-90% interquartile range (IQR) threshold to dampen the effect of possible straggler tags that can greatly extend the width of a TSS candidate without contributing much to expression. Deseq2-based results of differentially expressed TCs were independently filtered by setting the α argument to 0.05, which is the significance cutoff used for optimizing the independent filtering. For the analysis of differential TSS Usage (DTU) that was done by the diffSpliceDGE method of the edgeR package, we used a subset of genes with more than one TSS to analyze. By considering only TSS that correspond to more than 10% of total gene expression, we guarantee meaningful detection of promoter shifting events during CLL transformation.The reproducibility of the results between the two CAGE replicates was also not clear.We have added coefficient scores of CAGE library replicates derived from the RNA samples of Eu-TCL1 and WT, as well as CAGE library replicates from the polysome profiles of Eu-TCL1. . All replicates have shown high coefficient scores by the Pearson pairwise-correlation test, indicating the high reproducibility of our CAGE results.An apparent lack of application of unique molecular identifiers in the CAGE approach was also observed.Nucleic Acids Res41: e44 10.1093/nar/gks1128) (b) the nanoCAGE was optimized for the use of a small amount of starting RNA (50 ng) that lead to low-complexity libraries with high levels of duplicates . Also, it was claimed by Cvetesic, Nevena, et al., that the synthesis of truly random UMIs is problematic and subject to variability, thereby obscuring its use. Taking all these into consideration, the sequencing errors of UMI sequences , our ability to start with a higher amount of 5 ug total RNA to reduce PCR cycles to a minimum of eight, and by that avoid amplification biases, we decided to follow the classic CAGE method.Indeed, using the unique molecular identifiers (UMI) sequences for removing PCR-originated read duplicates became a gold standard and in 2017, the nanoCAGE protocol introduced the UMIs to the CAGE methodology. However, this method is claimed to be inferior to the classic CAGE method for the following reasons: (a) the template switching method used by nanoCAGE was shown to be sequence-dependent and, therefore, is potentially biased  and noted by Reviewer 1. Briefly, the calculation method we used is based on the ratio of the counts of the polysome fractions to the free fraction (see p. 9)Finally, there was a lack of loading controls in some of the Western blots.We have now added the missing loading control using antibodies against GAPDH .


Excitingly, recent advances in single molecule imaging with capacity to reconstruct “bulk-cell” features of chromosome conformation have revealed cell-to-cell chromatin structural variation. The fundamental question motivating our analysis of the literature is, can altered chromatin structure drive tumorigenesis? As our community learns more about rare disease, including low mutational frequency cancers, understanding “chromatin-driven” pathology will illuminate the regulatory structures of the genome. We describe recent insights into altered genome architecture in human cancer, highlighting multiple pathways toward disruptions of chromatin structure, including structural variation, noncoding mutations, metabolism, and de novo mutations to architectural regulators themselves. Our analysis of the literature reveals that deregulation of genome structure is characteristic in distinct classes of chromatin-driven tumors. As we begin to integrate the findings from single cell imaging studies and chromatin structural sequencing, we will be able to understand the diversity of cells within a common diagnosis, and begin to define structure–function relationships of the misfolded genome.In the past decade, we have seen the emergence of sequence-based methods to understand chromosome organization. With the confluence of  The protructure –9. One oWe describe evidence for altered chromosome folding in cancer in the context of chromatin interaction domains, and chromosome structural variation in malignancies. Studies of chromatin loops, topologically associating domains (TADs), chromatin compartments, and structural variation (SV) provide evidence for this finding by revealing key elements of altered genome structure in cancer. Thus, we examine genome structure–function relationships in human malignancy, with a focus on alterations in chromatin interaction domains.cis-chromatin sequencing methods including Hi-C enable detection of chromatin loops and long-range interactions, more recent methods, including HiChIP [paired-end tags, or PETs that are useful for defining chromatin interactions, including functional enhancer-promoter interactions.In Hi-C data, chromatin loops appear as punctate regions of heightened interactions relative to neighboring chromatin , 11. Whig HiChIP , 9, TrACg HiChIP , and Capg HiChIP , focus oSubstituting the micrococcal nuclease (MNase) enzyme for other cutting enzymes increases resolution in sequencing shorter-range chromatin domains. Excitingly, 3C-based methods and micrococcal nuclease (MNase) have converged in recent methods for high-resolution chromatin structural sequencing, including Micro-C  and MicrMoreover, the higher-resolution chromatin interactions observed in MCC and Micro-C, also provide context for defining transcription factor (TF) binding sites within chromatin loops. This enables new approaches to understand TF-driven childhood malignancies such as the chimeric oncoproteins that drive rhabdomyosarcoma (RMS)  and EwinMYOD1 and SOX8 gene loci to promote positive autoregulation of tumor-specific gene activation [SNAI2 [SNAI2, promoting FN-RMS tumor differentiation [Recent studies in RMS, a rare pediatric soft tissue cancer, have revealed context-specific roles for chromatin looping. In the RMS subtype driven from TF-chimeras, termed fusion-positive (FP-RMS), there is evidence that the PAX3-FOXO1 oncoprotein has pioneer activity . The inttivation . The clitivation , 19. Wittivation . In anotn [SNAI2 . The essn [SNAI2 , 20. Thentiation . DetermiMYC) and tumor suppressors (CDKN2A). Conceptually, these findings motivate examining altered enhancer interactions in cancers. Analyzing CTCF binding sites in human cancers reveals recurrent mutations and deletions of these motifs in leukemia (T-ALL), esophageal tumors, and liver cancer [The motifs of TFs influence regulatory chromatin looping in human cancer. Recent studies reveal that highly penetrant noncoding genetic variants have the potential to affect chromatin interactions. Massively parallel TF-motif binding assays coupled to sequencing have revealed the specificities for disease-causing DNA mutations that alter the ability of TFs to recognize their motifs . A captur cancer .GATA3 gene expression levels can alter chromatin architecture in leukemia, and that polymorphisms in GATA3’s intronic regulatory sequences could impact its expression [Experimental evaluation of many of these CTCF motif alterations with a method called ChIA-PET , which epression . These scis chromatin interactions suggests that altered chromatin domains might serve dual roles as drivers and vulnerabilities in human cancer. This leads to the question, what are the characteristics of chromatin contact domains in cancer? We explore this question, in the context of driving alterations in key gene classes, and structural variation in cancer genomes.Recent evidence indicates that chromatin domains can be targeted by clinical  or pre-cde novo detection of SVs in cancer genomes , chromatin sequencing, and imaging methods, have revealed that Hi-C, especially in combination with whole genome sequencing, can be extremely powerful in identifying structural variation (SV) , 31. FroBCL11B gene locus and its enhancer, thereby driving its expression in progenitor cells [BCL11B gene locus were found to frequently involve transposition of enhancer elements that produced functional consequences in gene expression. The study also revealed enhancer-specific CNV (enhancer amplification) affecting BCL11B gene regulation. Thus, through structural repositioning, or amplification of enhancers, leukemia gene regulation is systematically altered. Recently, shallow Hi-C approaches have helped define SVs leading to ETV6-RUNX1 gene fusion events in leukemia, and have revealed new patterns of potential chromothripsis  [A recent study has revealed that in leukemia genomes, SV modifies the proximity of the or cells . The autgements) , 35. It gements) . Studiesgements) , 38 may TCF12 and amplification of its enhancer have been observed in Hi-C studies [MYC in lymphoma, through translocation events [GATA3 and FOXA1 may have characteristic altered gene expression, Hi-C has been used elegantly to detect patterns of CNV and SV [In diffuse intrinsic pontine glioma (DIPG), CNV affecting tumor-specific gene expression of  studies . In anotn events . In bladV and SV . In hemacis-chromatin interactions were observed at loci encoding the pioneer factor FOXA1 (prostate cancer), the cell cycle gene CDK12 (breast cancer), and the RAB36 gene (leukemia). Interestingly, RAB36 is frequently observed within a conjoined chromatin contact domain resulting from SV. Yue and colleagues observed that RAB36 gene expression was associated with poorer patient outcomes, linking SV-mediated chromatin domain alterations with disease etiology. We propose that the developmental alterations in gene expression patterns derived from SV-altered chromatin domains are highly relevant in human cancers, and we anticipate exciting advances to in this area in coming years.The developmental consequences of SVs on altered chromatin domains can be severe, with altered gene expression patterns resulting from de novo TAD formation (“neo-TADs”), TAD-fusion events, and altered boundaries (reviewed ) Fig. 11A,C,E. Icis-chromatin interactions. A recent comprehensive analysis of SV in human cancer observed recurrent enhancer-deletions for loci encoding tumor suppressive genes, and recurrent de novo TAD formation enabling oncogene expression[There is evidence that SVs occurring in human cancers are frequently more complex than in other tissues, and this has implications for xpression. Interesxpression. HoweverIn distinct cancers, there are common “SV pathways” toward recurrent fusion-oncogene events, while a diversity of SV types may result in amplification of common oncogenes or losses of major tumor suppressors . IncreasSDH class and IDH class of oncogenic mutations rely on distinct mechanisms to induce their convergent effects on the epigenetic state of the cell. One particular class of these penetrant mutations renders the SDH-family enzymes catalytically deficient, which results in accumulation of succinate before it can be processed. High levels of succinate can inhibit several classes of demethylase enzymes, including TET-family and JMJD-family demethylases, thus increasing methyl-CpG content [Increasing evidence has revealed chromatin structural phenotypes driven from recurrent cancer mutations in genes encoding metabolic regulators. Two major classes of metabolic mutation that each alter the Krebs cycle are highly penetrant in human tumors. In each case, toxic accumulations of metabolites result in differentially methylated regions (DMRs) across the genome. However, the  content , augment content , and inc content . Moreove content .FGF4 locus, and KIT insulator elements. The altered DMRs at these loci resulted in deregulated gene expression for these two GIST drivers. These could be targeted as vulnerabilities with clinical FGFR4, and KIT inhibitors.Connecting altered CpG methylation and altered tumor metabolism, a recent report revealed DNA hypermethylation at CpG islands in IDH-driven gliomas . ImportaPDGFRA [A related class of metabolic cancer mutations in the IDH enzyme has also been reported as a driver in leukemias and gliomas . SimilarPDGFRA . StudiesPDGFRA , identifWhile metabolic products can alter chromatin structure–function relationships through enzymatic processes, there is evidence that non-enzymatic processes link metabolic outputs and chromatin structure as well. With new insights from non-enzymatic covalent histone modifications (NECMs) , 52, thecis-chromatin interactions, imaging-based methods can capture multi-locus interactions. With 3D-STORM based studies, Zhuang and co-workers examined the cohesion-dependence of domain organization in single cells. With rapid RAD21 depletion [Bulk-cell genomics has revealed internally consistent principles for contact domains, compartments, and loops. However, single cell imaging sometimes yields distinct or complementary answers to the questions of genome organization. While there is a diffraction-limiting feature in traditional imaging experiments on the order of the visible wavelengths of light (~ 200 nm diffraction limit), 3D-STORM imaging approaches 20 nm resolution . This inepletion , the autepletion . Similarepletion . In receepletion . Thus, repletion . Thus, wepletion , 49, 61.In recent advances, Boettiger and co-workers have reported reconstruction of chromatin interaction domains from high resolution imaging, optical reconstruction of chromatin architecture (ORCA), providing new insights about TAD function and transcription during development . With ORDefining the fundamental connections between deposition of heterochromatin marks and chromatin folding in human cancer will be impactful in coming years. Moreover, applications of ORCA and 3-D STORM imaging to understand conserved properties of chromatin domains in cancers will be highly impactful, as these methods can take into account cellular heterogeneity in human tumors, as well as defining RNA transcription in the precise cellular context in which the chromatin structure is measured. Moreover, integrations with single cell Hi-C (sc-HiC) –71 and hSMC1A, SMC3), and for the genes encoding associated STAG1/2 proteins. The class of hematologic “cohesinopathy” connects basic studies of chromatin looping [RAD21, STAG2, SMC1A, and SMC3 as driver mutations in acute myeloid leukemia (AML), chronic myeloid leukemia (CML), and pre-leukemic states  [An important epigenetic pathway towards architectural dysregulation is the mutation of genes encoding major drivers of genome structure, cohesin complexes . The Can looping , 73, witme; MDS) .STAG2, SMC1A mutations). Of note, cohesinopathy mutations have high variant allele frequency, and are considered as founder- or driver-events in leukemic tumor evolution [STAG2 or SMC3 mutations occur in MDS or de novo AML, supporting the role of cohesin loss in early, driving events in leukemogenesis [STAG2 alterations in chromatin structural phenotypes in EWS [While cohesin mutations are frequently loss-of-function, it is of high interest to characterize rare sub-classes of cohesinopathy resulting in gain-of-function function rather than haploinsufficiency or total loss  frequent noncoding mutations at chromatin loop anchors and domain insulators, (2) altered TF binding at sites of chromatin interaction, (3) structural variation resulting in domain redistricting, and (4) mutations in cohesin and metabolic genes, upon which chromatin structure is heavily reliant. In each case, further work will be required to establish causality of the chromatin architecture in tumorigenesis. New technology to enable sequencing of altered chromatin domains in human cancer  and next-generation imaging of chromatin domains  will allow for integration of 3D sequencing and microscopy to define common structural drivers. We anticipate that connections between chromatin structural alterations and patient outcomes will ultimately influence clinical decision making. For example, for low mutational burden tumors with high SV illuminated through 3D genomics, radiation therapy may not be the most efficacious strategy . We look


Restricting ribosome biosynthesis and assembly in response to nutrient starvation is a universal phenomenon that enables cells to survive with limited intracellular resources. When cells experience starvation, nutrient signaling pathways, such as the target of rapamycin (TOR) and protein kinase A (PKA), become quiescent, leading to several transcription factors and histone modification enzymes cooperatively and rapidly repressing ribosomal genes. Fission yeast has factors for heterochromatin formation similar to mammalian cells, such as H3K9 methyltransferase and HP1 protein, which are absent in budding yeast. However, limited studies on heterochromatinization in ribosomal genes have been conducted on fission yeast. Herein, we shed light on and compare the regulatory mechanisms of ribosomal gene transcription in two species with the latest insights. Saccharomyces cerevisiae, and each cell contains 100,000–200,000 ribosomes in the fission yeast Schizosaccharomyces pombe [S. cerevisiae [S. cerevisiae and S. pombe [The ribosome is massive intracellular machinery translating mRNA, transcribed from DNA, into proteins in living organisms. This machinery is constructed by a small 40S subunit composed of 18S ribosomal RNA (rRNA) and 33 ribosomal proteins (RPs) and a large 60S subunit composed of three rRNAs  and 46 (in yeast) or 47 (in human) RPs [S. cerevisiae carries approximately 150 rDNA tandem repeats on chromosome XII, S. pombe has 100–150 repeats on both ends of chromosome III, and humans carry a total of ~350 repeats on five different chromosomes [S. cerevisiae [S. pombe [S. cerevisiae [To achieve rapid and robust rRNA transcription, multiple copies of ribosomal DNA (rDNA) containing 18S, 5.8S, and 25S/28S regions exist tandemly in the genome . For exaomosomes ,7. The 5S. pombe . FurtherS. cerevisiae and more than 500 assembly factors and 300 snoRNAs in humans [Transcription of ribosome-associated genes requires three RNA polymerases. That is, consecutive sequences of 18S, 5.8S, and 25S/28S rRNA are transcribed by RNA polymerase I in the nucleolus as the precursor rRNA from 5′ETS  to 3′ETS, including ITS  1 and ITS 2 . 5S rRNAIn summary, numerous factors contribute to ribosome biogenesis, and these genes are actively transcribed. Accordingly, intracellular resources are substantially devoted to ribosome synthesis. Therefore, ribosome biosynthesis must be promptly halted to reduce intracellular energy consumption when the cells encounter nutrient depletion.One way to cease ribosome biogenesis is epigenetic gene regulation through modifying histones and methylation of cytosine residues on DNA without altering the DNA sequences. DNA methylation does not occur in budding and fission yeasts ,14. HistThis review summarizes the findings on the transcriptional regulation mechanisms of ribosomal genes and compares the difference between budding and fission yeasts.S. cerevisiae revealed that the concentration of active rDNA is reduced in the stationary phase  compared to that in the logarithmic growth phase (or when cells are cultured in a nutrient-rich medium) [When the nutrient supply decreases, ribosome biosynthesis must be halted immediately to avoid the consumption of intracellular resources. Early studies using  medium) . In addi medium) , and tyr medium) , indicat medium) . Jorgens medium) .S. pombe, the depletion of sulfur, an indispensable component for many coenzymes such as coenzyme A and biotin, diminishes the expression of ribosome-related genes [In ed genes . A compred genes . We alsoed genes ,27.S. cerevisiae [S. pombe, oxidative, cadmium, heat, osmotic, and DNA damage stress commonly repress ribosomal genes [Intriguingly, ribosomal genes can also be repressed by stresses other than nutritional starvation. For example, mild heat shock leads to quick but transient repression of RP genes in revisiae ,28. In Sal genes . Since nS. cerevisiae. Two types of TOR proteins, Tor1 and Tor2, are found in budding yeast, forming TOR complex 1 (TORC1) and TOR complex 2 (TORC2), respectively [TOR was initially identified as a target gene for the macrolide immunosuppressant drug rapamycin by genetic screening using ectively ,31. Notaectively ,33. Withectively ,35,36  strongly accumulates on 35S rDNA through the DNA binding domain box A and box B, forming a fragile chromatin structure ,54,55,56cription ,58. An icription . These fMO1 gene ,60  in the rDNA region rather than by repressing transcription of 35S rRNA [S. cerevisiae) by SIRT7 (Sir2 in S. cerevisiae) enhances the association of histone and RNA polymerase I on rDNA, leading to an upregulation of rRNA transcription [Finally, we refer to a case where rDNA transcription is epigenetically regulated in ng yeast . Hence,  repeats , it does35S rRNA . Interescription ,80.S. cerevisiae COMPASS complex involving Set1 promotes histone H3K4 methylation and represses transcription from rDNA during nutrient-rich conditions, suggesting that the Set1 complex silences the dormant rDNA unit [S. cerevisiae rDNA silencing during starvation remains to be determined, and whether it is regulated by TORC1.Instead, the DNA unit ,82  by methyltransferase Nop1 and is found to accumulate specifically in the nucleolus. In addition, the ChIP-seq profile indicates that H2AQ105me is enriched within the 35S rDNA regions. Since a mutant with an alanine substitution at Q105 mimicking methylation exhibits increased rRNA transcription, the enrichment in H2AQ105me is assumed to facilitate rRNA transcription [Remarkably, cription . This idcription   carry a UAS (Upstream Activating Sequence), a T-rich element, and a TATA box as upstream regulatory elements. The transcriptional activator Rap1 (repressor/activator protein 1) recognizes the UAS and associates with ~90% of the RP genes [lacZ gene, which is fused downstream of the RPL16 gene, revealed that transcription levels in cells lacking the Rap1 binding site at the RPL16 gene promoter were reduced to 10% of that in wild-type cells, confirming that Rap1 activates RP genes [S. cerevisiae cells are exposed to heat, osmotic stress, or rapamycin, the localization pattern of Rap1 is not altered despite the observed repression of RP gene transcription, suggesting that Rap1 is just a scaffold for other transcription factors governing their expression [The repressive mechanisms of RP genes ,86,87,88RP genes . Howeverpression   pathway during nutritious conditions. In contrast, it is increased during amino acid starvation [As described above, RP gene expression relies on complicated mechanisms, but the behavior of these factors is dynamically altered when cells shift into nutrient-poor conditions. Starvation and rapamycin treatment triggers the dissociation of Ifh1, Sfp1, and Esa1 from the RP gene. The dissociation of Esa1 was studied by Joo and coworkers . A transarvation   are involved in ogenesis . The upsogenesis ,111,112.ogenesis . It is kogenesis . Additioogenesis   demonstrated that the Sfp1-binding motif is concentrated in the promoter of ribosome biosynthetic genes repressed during nitrogen starvation [S. pombe ribosomal gene expression. A homolog of the transcriptional activator Sfp1 is also present in arvation , indicatS. pombe. However, Fhl1 predominantly contributes to the regulation of genes related to the transport process and mating/sporulation, with little or no influence on the ribosome-related genes [The forkhead protein Fhl1 is also regulated by TORC1 in ed genes .S. cerevisiae Crf1 and Ifh1 are annotated with the S. pombe homolog Crf1, it has not been determined whether Crf1 regulates ribosome-related genes. A homolog of Stb3 is also present in S. pombe, but its function has yet to be discovered. Finally, there is no annotation and homolog information for Dot6 and Tod6 on PomBase (https://www.pombase.org/ accessed on 31 January 2023). Altogether, the analysis of transcription factors equivalent to those revealed in S. cerevisiae is poor in S. pombe.Although S. pombe ribosomal gene repressions. Yet, histone deacetylase (class I) Clr6, homologs of Rpd3 repressing rRNA, RP, and Ribi genes in S. cerevisiae, do not contribute to the repression of ribosome-related genes. In contrast, class II deacetylase Clr3, a homolog of Hda1, represses at least rRNA gene expression [S. pombe, accumulate at rDNA regions to repress transcription [S. pombe as in S. cerevisiae  positively regulates rRNA gene expression . Previouctivator . Additioctivator , which mctivator  , also contributes [D. melanogaster or mice as model organisms.In HEK293 cells and β-TC6 cells, a mouse pancreatic beta cell, mTOR binds to the rDNA promoter, and this binding is dissociated upon rapamycin treatments and amino acid starvation . This sutributes . In the


How does an organism regulate its genes? The involved regulation typically occurs in terms of a signal processing chain: an externally applied stimulus or a maternally supplied transcription factor leads to the expression of some downstream genes, which, in turn, are transcription factors for further genes. Especially during development, these transcription factors are frequently expressed in amounts where noise is still important; yet, the signals that they provide must not be lost in the noise. Thus, the organism needs to extract exactly relevant information in the signal. New experimental approaches involving single-molecule measurements at high temporal precision as well as increased precision in manipulations directly on the genome are allowing us to tackle this question anew. These new experimental advances mean that also from the theoretical side, theoretical advances should be possible. In this review, I will describe, specifically on the example of fly embryo gene regulation, how theoretical approaches, especially from inference and information theory, can help in understanding gene regulation. To do so, I will first review some more traditional theoretical models for gene regulation, followed by a brief discussion of information-theoretical approaches and when they can be applied. I will then introduce early fly development as an exemplary system where such information-theoretical approaches have traditionally been applied and can be applied; I will specifically focus on how one such method, namely the information bottleneck approach, has recently been used to infer structural features of enhancer architecture. Escherichia coli could switch from growing on glucose to lactose depending on lactose’s presence in the environment showed that cells can respond to environmental stimuli by gene regulation [The discovery that gulation . As the gulation : the lacgulation , 4. A moAnother canonical model for gene regulation is based on sharp thresholds: this idea originated from Wolpert  concerniThe advantage of these models is their simplicity, or their usefulness as a ‘limiting case’: for example, the analysis of the graph-theoretical models has shown that the Hill function from equation (2) gives the steepest possible slope (or threshold) of all various individual combinations of transcription factor binding , 11. ThuYet, one important change in thinking about gene regulation around the early 2000s was the focus on noise and stochasticity of gene expression . This stOn the theoretical side, calculations for noise originate from work in 1977 on chemotaxis (which involves the sensing of a molecular gradient by receptors) : Berg anRemarkably, this limit presents still a lower bound for noise in binding in equilibrium. More general work in the early 2000s , which ianges to , 185 may raise the noise by increasing the correlation time of the input noise, impeding noise averaging , 22.The above discussion already shows that it is difficult to calculate both the mean and noise of gene expression in a model bottom-up. In addition, there are a series of experimental insights since the early work on gene regulation, which make the situation even more complex.In eukaryotic organisms, the regulatory architecture is different from the lac-operon: genes can be regulated by one or more promoters, as well as several regions with binding sites for transcription factors  which can be several kilobasepairs away from the promoter or the gene  sketch . These eModifications of these models to incorporate the more complex regulatory landscape of individual transcription factor binding have, for example, been made by the so-called ‘thermodynamic models’ for transcription , 25. HerThe situation is further complicated by the idea that transcription may involve a topological change in the genome that changes enhancer-promoter distances . One addThese heterogeneous transcription factor distributions matter from the modeling perspective: frequently, transcription factor concentrations are only available averaged across the entire cell, but the local concentration of the transcription factor close to its binding site is required for the model (see equation 2). If these concentrations are unknown, estimating parameters for more specific models might lead to flawed conclusions. Similarly, calculating the noise of binding at the binding site regions is almost impossible when neither the number of transcription factors nor the mechanism for their accumulation around the binding site is known.Overall, the added experimental complexities mean that although many advances have been made regarding modeling the regulation of individual genes in specific developmental time periods, an overall conceptual picture is still lacking. Such a conceptual picture is nevertheless important: conceptual understanding can help predict whether a particular gene may have many enhancers, where they might be located, or what binding site arrangements can detrimentally change expression.Thus, in the following section, I will introduce a ‘top-down’ approach to complement to the ‘bottom-up’ mechanistic models; this approach is based on data and attempts to infer structural features necessary for precise gene regulation from these data.A complex system where it has been similarly complicated to draw up simple models due to the large number of functional elements involved is a net of neurons, such as the brain. One can think of the Hill functions, or more specific molecular schemes, as being like the Hodgkin–Huxley model for the electrical dynamics of neurons . An alteOne particularly exciting approach here is to treat the interpretation of the transcription factor concentration(s) as a  sensing problem. Such ‘efficient’ sensing approaches have been successful in neuroscience, for example, concerning olfaction  or conceBefore briefly introducing the information-theoretic optimization in the sensing problem introduced by Laughlin as an example of a signal processing optimization, I want to emphasize one difference between electronic signal transfer and biological systems: In electronic signal transfer, one can consider how to best represent (source coding: optimizing entropy), and how to best transmit a message (channel coding: optimizing error correction). In biological systems, it can be difficult to differentiate the signal from what the message should be . In addition, in the processing of gene regulatory signals, the source  and the channel characteristics (noise profiles) can be modified biologically (i.e. have evolved evolutionarily). Thus, it is not a priori helpful to think of coding categories as having been separately optimized in a joint source-channel coding sense , but betLaughlin was wondering how a class of insect eye neurons, the so-called large monopolar cells (LMCs) can sense light intensity from different natural landscapes. We can denote the light intensity or signal by eceptors  and usesTo make the problem simple, we assume that the noise in the encoding J0−1P(J) , 61. ThuIf the noise tipliers : the dishis data .In the next section, we will apply this sensing optimization to transcription factors.We note that similarly to the light intensity, we assume that the ‘intensity’ or concentration of the transcription factor provides relevant information to cells. Especially in early fly development, the concentrations of certain transcription factors, such as the maternal morphogen Bicoid, provide information about a particular cell’s fate : cells clleagues , 63, 64.In the case for fly development, there is thus a clear variable we care about  from the concentration of Hb, and ask how much information an optimal sensor Further analysis of these thresholded measurements showed that the threshold positions did not need to be fine tuned: specifically, thresholds at higher transcription factor concentrations could be placed more loosely. Intuitively, this means that in concentration regimes where Hb is expressed noisily, the precise levels are not as important. Biologically, transcription factor concentrations at high concentrations are often measured with weak binding sites. We deduced that this suggests that how many weak binding sites an enhancer has does not matter as much; this, again, is known in biology.Second, we see that about 10 thresholds are required to sense hunchback correctly. When we use realistic estimates for how well a single enhancer can sense in a Hill-function model with the Berg–Purcell noise (equation 4), we obtained 1–3 bits. While this may just be enough information to sense Hunchback correctly, it is not enough when we want to obtain information about all four gap transcription factors together: there, we needed about 3.8 bits or ca 50 thresholds to get to an accuracy that gets to about 10% of the information provided. This shows that many enhancers are required to read the gap transcription factor signals.Finally, in order to determine what enhancer architectures should look like if they sensed optimally, one can perform a comparative calculation. We optimized four separate sensors with the constraint that each sensor should only sense one gap transcription factor each. We found that this was always worse than having a single sensor that sensed them together  and with degeneracies for weak binding sites allow the fly to extract most of the protein signal that is provided in the gap transcription factors, and this, in turn, allows the fly to make the correct cell fate decisions.in vivo applications, but also for an appreciation of the regulatory complexity.The hope is that sensing or inference approaches can, together with mechanistic approaches, help us understand faster why certain regulatory features are there; this could be important not only for a better Future directions: Especially for synthetic gene regulation, where one hopes to engineer gene regulatory systems , often u


TERT gene being tightly repressed. During the development and progression of thyroid carcinomas (TCs), TERT induction and telomerase activation is in general required to maintain telomere length, thereby conferring TC cells with immortal and aggressive phenotypes.Telomerase is a ribonucleoprotein complex consisting of a catalytic component telomerase reverse transcriptase (TERT), internal RNA template and other co‐factors, and its essential function is to synthesize telomeric DNA, repetitive TTAGGG sequences at the termini of linear chromosomes. Telomerase is silent in normal human follicular thyroid cells, primarily due to the TERT loci including TERT promoter's gain‐of‐function mutations, copy number gain, fusion and rearrangements, have recently been identified in TCs as mechanisms to induce TERT expression and to activate telomerase. Importantly, numerous studies have consistently shown that TERT promoter mutations and TERT expression occur in all TC subtypes, and are robustly associated with TC malignancy, aggressiveness, treatment failure and poor outcomes. Therefore, the assessment of TERT promoter mutations and TERT expression is highly valuable in TC diagnostics, prognosis, treatment decision, and follow‐up design. In addition, the TERT promoter is frequently hypermethylated in TC cells and tumors, which is required to activate TERT transcription and telomerase. Dysregulation of other components in the telomerase complex similarly upregulate telomerase. Moreover, shortened telomeres lead to altered gene expression and metabolism, thereby actively promoting TC aggressiveness. Here we summarize recent findings in TCs to provide the landscape of TC‐featured telomere/telomerase biology and discuss underlying implications in TC precision medicine.The genomic alterations of the TERT gene aberration and expression‐based molecular classification of TCs is proposed, and for such a purpose, the standardization of the assay and evaluation system is required. Moreover, the TERT‐based system and 2022 WHO TC classification may be combined to improve TC care.Mechanistic insights into telomerase activation and TERT induction in TCs are important both biologically and clinically. The  TERT gene encoding the telomerase catalytic component. TERT promoter mutations and hypermethylation occur across TC subtypes and are associated with aggressive diseases. The assessment of telomerase biomarkers is highly valuable in TC diagnostics, prognostication and treatment decision. TERT/telomerase‐based TC molecular classification is suggested. Telomerase is activated in TCs, predominantly resulting from genomic, epigenetic and transcriptional aberrations in the  Across TC subtypes, the presence of C228T and C250T is mutually exclusive, whereas the C228T mutation is predominant Figure . NumerouTERT transcription  create de novo ETS binding motifs that are bound by the ETS transcription factors, thereby activating n Figure .22 Then Figure .22 Con2.1.2TERT gene is located on chromosome 5p, and its gain or amplification is widespread in many cancers.TERT‐amplified tumours expressed the highest levels of TERT mRNA and telomerase activity (a gene expression signature for telomerase activity estimation was applied), which suggests a critical role of the TERT amplification in telomerase activation during carcinogenesis.TERT copy number gain is at a very low frequency (.3%–4%).TERT copy numbers in 25 of 495 tumours (5%).TERT copies in follicular thyroid tumours and observed TERT gains in 8% (6/77) of FTCs.TERT copies occurred in 21% (4/19) of follicular tumours of uncertain malignant potential.TERT gene gains/amplification in PDTCs and ATCs. Panebianco et al. reported TERT copy gain in 2/4 of PDTC/ATC tumours,TERT copy gains in 5/6 of ATC tumours.TERT gene amplification in PDTCs and ATCs.The TERT structural rearrangement in nine widely invasive FTCs.TERT transcription.TERT gene fusion and rearrangements seem occasional events in PTCs, PDTCs or ATCs.TERT gene alterations in these tumours.Yoo et al. identified one tumour with PDE8B‐TERT fusion and one with a 2.2TERT transcription.TERT gene is transcriptionally silent, there exist hypomethylated UTSS and TSS or proximal TERT promoter regions, whereas TERT‐expressing PTC and FTC cell lines exhibit hypomethylated proximal promoter but hypermethylated UTSS  and the other from distant metastasis (FTC‐238), more malignant.2.3TERC is a 451 nucleotide‐long lncRNA and serves as an RNA template during telomerase‐mediated telomere synthesis.TERC expression in oncogenesis may be attributable to different mechanisms, including aberrant alterations in transcriptional and epigenetic statuses, oncogenic signalling activation, gene amplification and among others, which depend on cancer types.DKC1 gene.The dysregulation of 2.4TERT transcription on the other.In addition to TERT and TERC composed of the telomerase holoenzyme, the telomerase complex contains several co‐factors Figure ; however3TERT transcription and telomerase activation in the late stage of carcinogenesis has long been observed, but it remains elusive whether they have a causal relationship in TCs and other cancer types. As genes near telomeres and even at positions with a certain distance from telomeres can be regulated by telomere length, so‐called telomere position effect (TPE) and TPE‐over long distances (TPE‐OLD), shortened telomeres in premalignant and transformed cells may directly regulate gene expression to promote oncogenesis.TERT gene has been shown to be controlled by TPE‐OLD  normal and cancer cells.44.1Thyroid nodules are extremely common in adults, and distinguishing between benign and cancerous ones is an essential precondition to avoid unnecessary treatment for patients with benign conditions and to provide appropriate cares for those with TCs. As TERT promoter mutations occur specifically in TCs, although it is absent in benign tumours and normal thyroid tissues, it should be a reliable TC biomarker. Thyroid samples from fine needle aspiration biopsy and core needle biopsy have been analysed for TERT promoter mutations.However, two issues have to be addressed here. First, discovering TERT promoter mutations in thyroid nodules that represent a malignant disease, however, in adult DTCs, including FTCs and PTCs, the prevalence of the TERT promoter mutation is <25% in Western countries while can be lowered to <3% in certain populations,TERT gene is expressed in TC tumours with and without TERT promoter mutations, and theoretically, as a TC diagnostic marker improves sensitivity. However, TERT mRNA is detectable if the presence of lymphocytes or thyroiditis in benign nodules, which may compromise the specificity.The 4.2V600E mutation, and its coexistence with TERT promoter mutations leads to the formation of the most aggressive PTC tumours and even promotes the evolution of PTC to deadly ATC.Clinical investigations of TC patients have revealed the association of TERT promoter mutations with aggressive diseases, including advanced stages, extrathyroidal extension, vascular invasion, lymph node metastases, distant metastases and recurrence.The 2022 WHO classification of thyroid neoplasms categorizes follicular cell (thyrocyte)‐derived tumours into benign, low‐risk and malignant ones.TERT gene fusion or rearrangement, and both expressed dramatically high levels of TERT mRNA.TERT gene copies and hypermethylated promoters upregulate TERT expression, promoting FTC progression. Therefore, all these genomic and epigenetic alterations in TERT loci should be taken into consideration for the molecular classification of FTCs or other TC subtypes. In that setting, TERT mRNA assessment combined with TERT promoter analyses may be more appropriate. Indeed, higher TERT expression has been shown to predict shorter disease‐free survival in FTC and PTC patients.TERT gene expression‐based molecular classification of FTCs or PTCs outperforms that by the TERT promoter mutations.Interestingly, Park et al. integrated the TERT promoter status into the WHO classification of FTCs, and they observed substantial improvements in outcome prediction.4.3In addition to TERT, Montero‐Conde et al. also observed TERC as an independent factor for progression‐free survival prediction of TCs.5TERT expression is induced by not only TERT promoter mutations, but also other genomic and epigenetic (promoter methylation) aberrations, we would suggest TERT expression together with TERT promoter mutations rather than promoter mutations alone as the TERT‐based molecular classification of both FTCs and PTCs. Towards this purpose, the standardization of detection and evaluation systems is needed, and integrated analyses of telomerase/telomere‐related biomarkers may be more helpful (Figure The studies of telomerase activation in TCs have not only led to a mechanistic understanding of TC‐featured telomerase biology and pathogenesis but also provided rationales for the development of telomerase‐based tools in TC care Figure . For exal Figure . It shouThe authors declare that they have no conflict of interest.