Saccharomyces cerevisiae. Several hundred RHF and RTT genes encoding co-factors and restrictors of Ty1 retromobility, respectively, have been identified. Well-characterized examples include MED3 and MED15, encoding subunits of the Mediator transcriptional co-activator complex; control of retromobility by Med3 and Med15 requires the Ty1 promoter in the U3 region of the long terminal repeat. To characterize the U3-dependence of other Ty1 regulators, we screened a library of 188 known rhf and rtt mutants for altered retromobility of Ty1his3AI expressed from the strong, TATA-less TEF1 promoter or the weak, TATA-containing U3 promoter. Two classes of genes, each including both RHFs and RTTs, were identified. The first class comprising 82 genes that regulated Ty1his3AI retromobility independently of U3 is enriched for RHF genes that restrict the G1 phase of the cell cycle and those involved in transcriptional elongation and mRNA catabolism. The second class of 51 genes regulated retromobility of Ty1his3AI driven only from the U3 promoter. Nineteen U3-dependent regulators (U3DRs) also controlled retromobility of Ty1his3AI driven by the weak, TATA-less PSP2 promoter, suggesting reliance on the low activity of U3. Thirty-one U3DRs failed to modulate PPSP2-Ty1his3AI retromobility, suggesting dependence on the architecture of U3. To further investigate the U3-dependency of Ty1 regulators, we developed a novel fluorescence-based assay to monitor expression of p22-Gag, a restriction factor expressed from the internal Ty1i promoter. Many U3DRs had minimal effects on levels of Ty1 RNA, Ty1i RNA or p22-Gag. These findings uncover a role for the Ty1 promoter in integrating signals from diverse host factors to modulate Ty1 RNA biogenesis or fate.The Ty1 retrotransposon family is maintained in a functional but dormant state by its host,  S. cerevisiae have been identified in large-scale screens -retrotransposon in the yeast  screens . HoweverS. cerevisiae, with most copies being autonomous elements. Ty1 encodes two sense-strand transcripts. Genomic RNA (gRNA), emanates from the promoter in the U3 region of the 5′ LTR; U3 contains a TATA box sequence (TATAAAAC) and is dependent on the SAGA (Spt-Ada-Gcn5-acetyltransferase) complex and the SWI/SNF chromatin remodeling complex for activation of transcription , whose absence results in elevated retromobility, and Retrotransposition Host Factors (RHFs), whose depletion decreases retromobility . Five gepromoter . In addiing FUS3 , RTT101   of Ty1 retromobility and a second class of U3DRs. Further analysis of the latter class using the weak, TATA-less PSP2 promoter to drive Ty1his3AI expression revealed two subsets of U3DRs: one that likely depends on the low efficiency of the U3 promoter, and the other that may require its architecture. Several U3DRs did not detectably influence the levels of Ty1 RNA, Ty1i RNA or p22 expression. Together, the findings suggest that the U3 promoter functions as a hub for integrating the influences of diverse co-factors and restriction factors on retromobility. Moreover, our study adds to a body of evidence supporting the idea that U3 recruits factors that associate with Ty1 RNA and affect its utilization at post-transcriptional steps in retromobility.The work undertaken here has identified additional regulators of Ty1 retromobility whose functions require the U3 promoter, either because of its specific architecture or relative activity. Using a large collection of previously identified Saccharomyces cerevisiae strains used in this study are BY4741  and congenic strains with a single non-essential ORF replaced by a kanMX cassette  prior to transformation with plasmid DNA. A standard lithium acetate transformation protocol was used to introduce plasmid DNA into yeast strains  broth and agar. Mutant strains harboring deletion and hypomorphic alleles marked with  strains . Strainshis3AI-[∆1] retrotransposition indicator gene   . PTEF1mid pUG6  as templmid pUG6 . The PCR pBJC838 .URA3-CEN vector YCp50 carrying a functional, his3AI-[∆1]-tagged hybrid Ty1-912/Ty1-H3 element -Ty1his3AI. The U3 region of the 3′ LTR of Ty1-H3 in plasmid pGTy1-Cla (ApaI and XhoI sites using primers PJ762 and PJ763. The U3(H3) PCR fragment was digested with ApaI and XhoI and used to replace the ApaI-XhoI fragment on plasmid pBJC838  from strain BY4741 genomic DNA using primers PJ1480 and PJ1481. The promoter was cloned into plasmid BJC838 digested with ApaI and XhoI using New England Biolabs NEBuilder® HiFi DNA Assembly Master Mix.Plasmid pBJC1279 consists of the TEF1-Ty1his3AI, U3 (912)-Ty1his3AI and PPSP2-Ty1his3AI in different mutants, a semi-quantitative retromobility patch assay that measures the relative level of His+ papillation in cells in a ∼1 cm2 patch was performed. Patches of cells from four to seven independent transformants of plasmid pBJC1250, pBDG633 or pBJC1279 in strain BY4741 or a mutant derivative were spread on SC-Leu or SC-Ura agar, and plates were incubated at 30°C until patches of the mutant strains had grown to a similar density as patches of the wild-type strain BY4741 (2–4 days). The SC-Leu and SC-Ura plates were photographed, and then replicated to YPD agar and grown at 20°C for three to 6 days. After photographing the YPD plates, they were replicated to SC-His-Leu or SC-His-Ura agar, incubated for 4 days at 30°C., and photographed.As a screen for retromobility of PPGAL1-Ty1his3AI element, were grown in SC-Leu broth at 30°C overnight. Cells were diluted 1:100 in SC-Leu + 2% raffinose + 2% sucrose + 2% galactose and grown for 3 days at 20°C. A 1 or 2 µl aliquot was plated on SC-Leu agar and an aliquot of 10–1,000 µl was plated on SC-Leu-His agar. Plated cells grew at 30°C for three to 5 days. Colonies were counted, and the retromobility frequency of each culture was calculated as the number of His+ Leu+ colonies divided by the total number of Leu+ colonies in 1 ml of culture. The mean retromobility frequency (MRF) of seven independent transformants of the same genotype was determined. The relative retromobility frequency of each strain is the mean retromobility frequency (MRF) of seven independent transformants of the mutant compared to the mean retromobility frequency (MRF) of seven independent transformants of the wild-type strain measured in parallel at the same time.Single colonies of strain BY4741 and mutant derivatives harboring plasmid pBJC838 , which cR colony forming units per culture, and on SC-His agar to determine the number of His+ colony forming units per culture. Plates were grown for 3 days at 30°C before counting colonies. The Ty1kanMXAI retromobility frequency was calculated for each transformant as the number of G418R colonies divided by the total number of cells in an equal volume of culture, and the Ty1kanMXAI mean retromobility frequency (MRF) was the average from six independent transformants. The PPSP2-Ty1his3AI retromobility frequency was calculated for each transformant as the number of His+ colonies divided by the total number of cells in an equal volume of culture, and the PPSP2-Ty1his3AI mean retromobility frequency (MRF) was the average from the same six independent transformants.Independent transformants of strain JC6464 and JC6474 harboring plasmid pBJC1279 were grown overnight in SC-Leu broth at 30°C. Cultures were diluted 1:1,000 in YPD broth and grown at 20°C for 3 days. A 1:1,000 dilution of each culture was plated on YPD agar to determine the number of cells (colony forming units) in each culture. Aliquots of each culture were plated on YPD agar with 200 μg/ml Geneticin to determine the number of G418Saccharomyces genome database (SGD)  under accession number SRP047524  . Relativse (SGD) . p-valueRP047524  and PRJNRP047524 .600 of 0.1 in 50 ml YPD broth and incubated at 20°C until an OD600 of 0.6–0.8 was reached. Cells were pelleted, and pellets were washed in water before being frozen on dry ice and stored at −80°C. Total cellular RNA was isolated using a hot phenol extraction method (+ RNA with the Magnetic mRNA Isolation Kit (New England Biolabs) following the manufacturer’s protocol. Purified PolyA+ RNA (6 µg) was incubated in glyoxal loading dye (Ambion) for 30 min at 50°C. and fractionated on a 0.8% SeaKemGTG agarose gel. RNA was transferred to a HybondXL membrane (Amersham) utilizing a SSC gradient transfer system between 6x SSC and 10× SSC overnight at room temperature. Following transfer, membranes were UV crosslinked using the Optimal Crosslinking setting on the Spectrolinker XL-1500 (Spectronics Corporation). Membranes with bound RNA were stored at 4°C in 5× SSC.Yeast was grown in YPD broth at 30°C overnight. Cultures were diluted to an ODn method . Between32P-CTP were synthesized using an SP6 polymerase in vitro transcription reaction (Promega). The template for the antisense riboprobe that hybridizes to Ty1 gRNA and Ty1i RNA was plasmid pGEM-TyA1 digested with XhoI  flow cytometer. The geometric mean of fluorescence activity in 10,000 cells was measured for each pBJC1270 transformant and each pRS415 transformant. The geometric means of three pRS415 transformants of a specific strain were averaged to obtain a value for autofluorescence of that strain. This value was subtracted from the geometric mean of each of three pBJC1270 transformants in that strain, and the three resulting values were averaged to obtain the p22-Gag:GFP activity for each strain.Strain BY4741 and selected mutant derivatives were transformed with the vector, pRS415  or plasmRTT and RHF genes that regulate Ty1 retromobility via the U3 promoter, mutant alleles were tested for their effects on retromobility of a Ty1his3AI element in which the U3 promoter was replaced with the TEF1 promoter  within the coding region of the HIS3 gene in an antisense orientation. The his3AI gene is inserted in the 3′ untranslated region of the Ty1-H3 element in the opposite transcriptional orientation . As controls, mutants harboring a deletion of CHK1, which has no effect on Ty1 retrotransposition  were previously reported regulators of Ty1his3AI retromobility. Mutants harboring DAmP alleles of all five newly identified, essential RHF genes, CDC36/NOT2, CDC39/NOT1, MSL5, NDC1 and RPB7, are also included in the cohort of 82. The final one is not3∆, another newly identified rhf mutant. Greater than 80% of these U3IRs are retromobility co-factors.The Ppapillae . The corhis3AI expression, retromobility was measured quantitatively in eight rhf-u3ir mutants using a CEN-plasmid-based Ty1his3AI element driven from the inducible GAL1 promoter . Together with the results of the PTEF1-Ty1his3AI assay, these findings suggest that U3IRs act in a manner that is at least partially independent of the expression level of Ty1 RNA and the architecture of the U3 promoter.As an additional test of the idea that U3IRs regulate retromobility regardless of the strength or architecture of the promoter driving Ty1PGAL1; . PGAL1 results , and lesrtt and rhf mutants had no detectable defect in PTEF1-Ty1his3AI retromobility relative to the wild-type strain -Ty1his3AI plasmid in multiple attempts. Transformants were grown as patches on SC-URA agar at 30°C, replicated to YPD agar and grown at 20°C, and then replicated to SC-Ura-His agar to select for His+ papillae in cells that retained the plasmid. A detailed summary of the results of the U3-Ty1his3AI screen and plate reference numbers can be found in A subset of 106 e strain . To iden control . One muthis3AI element  of retromobility.Fifty-five mutants had wild-type levels of retromobility of the U3-Ty1 element . The 55 his3AI retromobility phenotype that differed from that of the wild-type strain  had the opposite effect on CEN-Ty1his3AI retromobility as that reported previously  analysis. We further divided the genes in these two categories, and in the starting cohort of 188 genes, into o stress . Enrichme tested .S. cerevisiae genes contributing to gene expression changes in a large number of transcriptome studies; deletion strains corresponding to these genes share the property of slow growth and have a common gene expression signature that was primarily attributed to a redistribution of cells over the cell cycle, with a greater fraction of cells in G1 (RHF and U3IR genes (which are mostly RHF genes) are enriched for this slow growth gene set and show substantial effects on growth when mutated, while RTT and U3DR genes exhibit only modest effects  mutant, in agreement with our results. We conclude that higher Ty1 RNA expression in the mms1∆ and rtt109∆ mutants may be an underlying cause of higher retromobility levels. The other 24 rtt mutants tested had only minor changes in Ty1 gRNA compared to the wild-type strain. Together, these findings indicate that most of the rhf and rtt mutants tested do not have substantially altered levels of Ty1 gRNA that are consistent with their retromobility defect; thus, some U3 promoter-dependent host factors may act at a post-transcriptional step in retromobility or affect expression of Ty1i RNA. While major increases in Ty1i RNA were not observed in this single northern, resolving Ty1i RNA presents a challenge, and it may be that subtle changes in Ty1i RNA levels can have more pronounced effects on retromobility. Furthermore, Ty1i RNA was not detected in the wild-type strain, so northern blotting could not be used to determine whether a decrease in Ty1i RNA accompanies the increase in retromobility in u3dr-rtt mutants. These challenges prompted us to develop a sensitive assay to measure the relative level of Ty1i RNA product, p22-Gag, in u3dr-rtt and other mutants.Oligo-dT purified RNA from 16 ∆ mutant ; potentirtt and hypomobile rhf mutants, we designed a CEN-plasmid reporter construct that expresses p22-Gag:GFP from the Ty1i promoter , in agreement with previous findings of elevated Ty1i RNA and p22-Gag . This reduction in p22-Gag:GFP activity is consistent with the hypermobility of a med20∆ strain but decreased expression of Ty1i RNA or p22-Gag was not detectable by northern or western blot analysis , but rtt109∆ and rad54∆ did not. Of seven u3dr-rhf mutants tested, only rpl7a∆ and snf2∆ had significant increases in p22-Gag:GFP levels consistent with their hypomobility phenotypes . An elevated level of p22-Gag in the rpl7a∆ mutant parallels the elevated level of Ty1i RNA observed by northern blot analysis , while opi3∆, shown previously to have hypomobile phenotype (p < 0.05)  . These c < 0.05) . Since GPSP2 promoter in the corresponding mutants . Nineteen U3laDR genes were identified, 12 (63%) of which encode Ty1 repressors (RTT genes in bold). Another 31 mutants, including yhr130c∆ . Most U3arDRs (21/31) were Ty1 retromobility co-factors. Snf2, a component of the SWI/SNF complex that is required for U3 but not Ty1i promoter activity, was identified as a U3 architecture-dependent regulator, as predicted. Other U3arDR transcription-related proteins include the SAGA and TFIID component, Taf14; SAGA component, Sgf29; histone gene transcription factors, Hir2 and Hir3; and the putative transcription factor Cth1.Py1his3AI . The relme plate . Mutantsyel008w∆ , respectyhr130c∆ , had a sA major impediment to understanding how host cells control the activity of retroviruses and retrovirus-like transposons is the multitude of host genes that influence retroelement replication at different stages and in different cellular compartments. Genetic screens for retroelement host factors often yield large non-overlapping gene sets , suggesthis3AI elements to classify host genes encoding modulators of Ty1 retrotransposition based on whether or not they retain their effects on retromobility when a promoter with higher activity and different architecture drives the expression of Ty1 gRNA. Analysis of 181 previously identified and seven newly identified regulators of Ty1 retromobility revealed two major classes. The largest class comprises 82 U3-independent regulators that repress or stimulate retromobility of a Ty1his3AI element even when it is expressed from PTEF1, a strong, TATA-less, TFIID-dominated promoter. The second class of 51 U3-dependent regulators modulates the activity of a plasmid-based Ty1his3AI element when it is expressed from the TATA containing, SAGA-dependent U3 promoter but not when expressed from PTEF1. Of these U3DRs, 19 regulate the mobility of Ty1his3AI with the weak, TATA-less, TFIID-dominated PSP2 promoter, whereas 31 did not affect PPSP2-Ty1his3AI retromobility. Characterization of these subclasses, U3laDRs and U3arDRs, respectively, suggests that there are multiple genetic pathways that modulate Ty1 retromobility even within the categories of U3 low activity-dependent and U3 architecture-dependent promoters  had no detectable effect on retromobility of the CEN-Ty1his3AI element . This cohort is enriched for genes involved in mRNA catabolism, gene expression, and transcriptional elongation. Notably, mRNA catabolism-related genes are one of the most highly enriched gene categories among Ty1 co-factors  and the other three were U3 low activity-dependent . Of the latter three, only deletion of RAD27 caused the opposite phenotype for PPSP2-Ty1his3AI retromobility to that anticipated based on previous findings  upstream of the HIS4 proximal promoter  had an increased level of p22-Gag:GFP. Overall, the findings suggest that not all U3arDRs affect levels of the Ty1 restriction factor. Nonetheless, establishing whether the u3ardr-rhf mutants identified here operate by altering the ratio of Ty1i:Ty1 expression will require more in-depth studies.The unique relationship between Ty1 and Ty1i allows the possibility of an additional promoter-dependent mechanism that can affect retromobility. The promoters for Ty1 and Ty1i may be in competition, and mutants that affect the balance of this competition could affect retromobility without substantially altering Ty1 transcript levels. A similar competitive model was first suggested as a mechanism by which promoter . We prevpromoter . Howeverrpl7a∆ and med15∆ have increased Ty1i RNA levels and p22-Gag levels (RPL7A and MED15 act in a common pathway to modulate retromobility. While Mediator subunits interact directly with both the Ty1 and Ty1i promoter, Rpl7a has not been reported to do so. It seems more likely that a stress pathway is induced in the absence of Rpl7a that impacts the relative expression of Ty1i, potentially via the Mediator complex. Notably, loc1∆ was isolated in the same screen for genes that work in a copy-number specific manner as rpl7a∆; accordingly, both LOC1 and RPL7A repress p22-Gag expression (LOC1 was categorized as a U3-independent gene, and the p22-Gag:GFP activity was not increased in the loc1∆ mutant. Loc1 may modulate p22-Gag levels by a different mechanism than Rpl7a; another possibility is that the p22-Gag:GFP assay used here is not as sensitive to elevated levels of p22-Gag as assays employed in other studies.Many ribosomal protein genes and ribosome biogenesis factors have been identified as RHFs, and they enhance retromobility by multiple mechanisms . Thus, ig levels  (Ahn et g levels . Striking levels . These pg levels . Togethepression . But in S. cerevisiae has co-opted Ty1 as a family of mostly autonomous LTR-retrotransposons (S. cerevisiae is in the regulation of Ty1 retrotransposition and cDNA-mediated recombination, potentially at different steps in the retromobility cycle, in a variety of subcellular domains and at different points of the cell cycle.nsposons . Domestinsposons . Deleternsposons . This sy


Saccharomyces cerevisiae Sub1 (ScSub1) has been defined as a transcriptional stimulatory protein due to its homology to the ssDNA binding domain (ssDBD) of human PC4 (hPC4). Recently, PC4/Sub1 orthologues have been elucidated in eukaryotes, prokaryotes, and bacteriophages with functions related to DNA metabolism. Additionally, ScSub1 contains a unique carboxyl–terminal region (CT) of unknown function up to date. Specifically, it has been shown that Sub1 is required for transcription activation, as well as other processes, throughout the transcription cycle. Despite the progress that has been made in understanding the mechanism underlying Sub1′s functions, some questions remain unanswered. As a case in point: whether Sub1’s roles in initiation and elongation are differentially predicated on distinct regions of the protein or how Sub1′s functions are regulated. Here, we uncover some residues that are key for DNA–ScSub1 interaction in vivo, localized in the ssDBD, and required for Sub1 recruitment to promoters. Furthermore, using an array of genetic and molecular techniques, we demonstrate that the CT region is required for transcription elongation by RNA polymerase II (RNAPII). Altogether, our data indicate that Sub1 plays a dual role during transcription—in initiation through the ssDBD and in elongation through the CT region. S. cerevisiae, which was initially explicated as a transcriptional stimulatory protein, owing to its homology to hPC4, its ability to activate transcription [RNAPII transcription progresses through highly regulated steps assisted by a considerable number of auxiliary proteins: general transcription factors, elongation and termination factors, and a diversity of complexes working as activators, repressors, co-activators, and co-repressors . Howevercription ,3,4, as cription ,6.Schizosaccharomyces pombe [Magnaporthe oryzae [Lactococcus lactis [Burkholderia pseudomallei [L. lactis, and bacteriophage T5 have been elucidated. In the case of PC4, it forms homodimers interacting with a 5-nucleotide region of two opposing DNA strands [The homology of ScSub1 and PC4 resides in a ssDBD, which has also been described in other organisms , such ases pombe  and the e oryzae . In addis lactis , Burkholdomallei , and evedomallei . It is adomallei ,14,15, Mdomallei ,9,16, L. strands ,14,17. R strands , as well strands , and refIMD2 expression [ScSub1 has been shown to participate in many processes, during mRNA biogenesis, all along the transcription cycle ,20. For pression , and thepression . In factpression . Additiopression , impactspression , and by pression ,28. Morepression . WhetherThe strains used are listed in SUB1, cloned into a centromeric plasmid under its own promoter, and with six copies of the HA epitope at its carboxyl–terminal for its detection. Mutant and wild-type strains were generated by transforming a sub1∆ strain lacking SUB1, with plasmids containing mutated or wt copies of SUB1, respectively. Deletion of the Sub1 carboxyl–terminal region was performed by homolog recombination directed to the chromosomal copy of SUB1 and replaced with 6xHA. For some experiments, a sub1∆ strain, transformed with a centromeric plasmid containing sub1∆CT, was also used. DNA binding mutations were created by site-directed mutagenesis of a wild-type copy of Cells containing HA-tagged Sub1 were grown at 28 °C in 200 mL of a rich medium or an SC medium to an OD600 of 1.0, harvested, washed with water, and followed by suspension in 1.5 mL of lysis buffer  containing protease and phosphatase inhibitors. The cell suspension was flash frozen in liquid nitrogen and then ground, in Spex Freezer Mill 6775, to a fine powder. Afterward, the cell lysate was thawed slowly on ice, transferred to pre-chilled tubes, and centrifuged at 13.200 rpm for 20 min. The supernatant was collected, and total protein concentration was estimated by measuring absorbance at 280 nm in a nanodrop. Exactly 1 μL of anti-HA was incubated with 25 μL of magnetic beads  for 1 h at 4 °C in PBS/0.1% BSA. After washing, the antibody coupled to beads was incubated, with whole cell extracts containing the corresponding amount of protein, for 2 h at 4 °C. The IPs were extensively washed with lysis buffer, and beads were suspended in an SDS-PAGE sample buffer. Thereafter, they were incubated at 65 °C for 5 min, and supernatants were loaded onto an SDS-PAGE gel.Western blot analysis was performed using appropriate antibodies in each case. Anti-phosphoglycerate kinase , anti-HA , and anti-Rpb1  were acquired from the indicated vendors. ECL reagents were used for detection. The signal was acquired on film and/or with a ChemiDoc XRS (Bio-Rad) system and, when necessary, quantified with the Quantity One software (Bio-Rad). The data plotted correspond to mean values from at least three different experiments, and the error bars represent standard deviations.® Premix Ex Taq™ (Tli RNaseH Plus) from Takara Bio Inc , following the manufacturer’s instructions. The four serial 10-fold dilutions of genomic DNA were amplified, using the same reaction mixture as the samples, to construct the standard curves. Real-time PCR reactions were performed in triplicate using at least three independent ChIPs. Quantitative analysis was carried out using the CFX96 Manager software . The values obtained for the IP PCR products were compared to those of the total input, and the ratio of the values from each PCR product, from transcribed genes to a non-transcribed region of chromosome VII or to chromatin samples incubated with beads but no antibody, was calculated. Numbers on the y-axis of graphs are detailed in the corresponding figure legend.Chromatin purification, immunoprecipitation (IP), quantitative real-time PCR (qPCR) amplification, and data analysis were performed as previously described ,29,31. BFMPL27 probes. Hybridization signals were quantitated by PhosphorImager analysis . The 18S rRNA signal was used for normalization, and then, the results were compared relative to the ATG probe, which was fixed at 1. Each experiment was performed at least three times.TRO assay was performed, essentially, as described in ,27,32. NTotal RNA was extracted as described by , and RT-The GLAM assay was carried out as in , with ceThe cells were grown at 28 °C in SC media, without uracil, to an OD600 of 0.5, split into two cultures, and 6AU was added to one of them to a final concentration of 100 μg/mL. Both cultures were incubated for 1 h, collected, washed, and resuspended in the appropriate buffer to isolate RNA. RT-qPCR was performed as described above. p ≤ 0.05.All experiments were performed at least in triplicate (n ≥ 3). The data were previously normalized with the square root and, then, scaled by the Pareto method. Data were processed using the statistical package Statgraphics Centurion XVI.II, from STSC, Inc. . The tests applied were the one-way analysis of variance (ANOVA) and the Fisher’s test to establish Homogeneous Groups at a significance level Multiple sequence alignment was generated using CLUSTALW and represented with Jalview program, version 2.11.2.5  https://toolkit.tuebingen.mpg.de/tools/hhpred, accessed on 16 March 2019). Crystal structure of the ssDNA-binding domain homodimers from hPC4 and MoSub1 structures were obtained with the following ID codes: PDB 7E4W  and PDB S. pombe, SpSub1 consists of 136 amino acids [M. oryzae, MoSub1 contains 162 amino acids [S. cerevisiae Sub1 is significantly larger than its human, fission yeast, and fungus counterparts. Specifically, budding yeast Sub1 has an extra-long CT region of 187 amino acids [ScSub1 (292 amino acids) shows a strong similarity to human PC4 (127 amino acids) over a 65-residue region (amino acids 40–105), containing an ssDBD and sequences essential for co-activator function  strains were tagged at the C-terminus of Sub1 with a 6xHA epitope, so protein levels were tested by Western blot. Levels of Sub1-K45A-HA and Sub1-Y66A-HA proteins were similar to those of wt Sub1-HA  and being replaced by 6xHA. We previously worked with an isogenic strain where a truncated copy of SUB1, containing only the ssDBD, was also cloned into a centromeric plasmid. We observed that Sub1ΔCT-HA protein levels were quite low, likely due to protein instability because we did not detect defect in transcription [sub1ΔCT cells, Sub1 becomes very unstable, which suggests that the CT region may contain sequences important for Sub1′s stability.To study the Sub1-CT region, we generated a mutant, deleting the CT region in the cription . Similarwt and mutant cells, and we observed that noneof the mutants displayed growth defects at 28 °C, neither SUB1 deletion , including sub1-FRN54-56AGG cells, as well as positive and negative controls, Sub1-HA wt and sub1∆ cells, respectively. We then analyzed, in normal growth conditions, the association of Sub1-HA with PMA1 and PYK1 promoters and, in addition with IMD2 promoter, whose expression depend on Sub1 [wt cells, Sub1-HA was efficiently recruited to the promoter of all of the analyzed genes .  on Sub1 . In wt ced genes C but wasreported . Similarpromoter . This ispromoter ,14,15, awt cells D. Nevertwt cells , and theipitated D, to relipitated E. We obsed genes F. This rWe have previously shown that Sub1 associates with coding regions in a transcription-dependent manner and influences transcription elongation rate . AccordiIMD2 gene expression, which depends on a functional Sub1 [To investigate if Sub1′s role in transcription elongation resides in the CT region, we used different strategies: (1) GLAM assays to study elongation efficiency; (2) reverse transcription, coupled with quantitative PCR (RT-qPCR), to analyze nal Sub1 ,26; (3) gene-length-dependent accumulation of mRNA), which specifically detects defects in elongation [PHO5 (acid phosphatase) coding sequence under the control of the GAL1 promoter, but with variable 3′ untranslated sequences, they were introduced into the wild-type (wt), ssDBD mutants (sub1-K45A and sub1-Y66A), and sub1ΔCT cells , when compared to wt cells  during active transcription, taking benefit of a strain expressing a chromosomal construction where the gene is expressed under the control of the GAL1 promoter. We measured the level of Rpb1 association with various positions within the 8Kb of the GAL1-FMP27 fusion gene upon galactose induction in wt, sub1Δ, and sub1ΔCT cells . First, we investigated whether Sub1-CT plays a role in transcription elongation by testing Rpb1 (the largest subunit of RNAPII) occupancy from the promoter to 3′-end regions of the extra-long gene CT cells A, top. Aescribed , as wellL1-FMP27 .FMP27 gene in wt, sub1Δ, and sub1ΔCT cells. We calculated the levels of active RNAPII along the gene, relative to the 5′ region  is a potent inhibitor of inosine-5′-monophosphate (IMP) dehydrogenase, which catalyzes the rate-limiting reaction of P levels , and celrogenase ,48. IMD2achinery , and in achinery ,49,50,51reatment ,26,52. IMD2 gene A. In facMD2 gene B, we dete of 6AU C, as expn mutant D. IMD2 wsub1ΔCT mutant behaves similar to the sub1Δ mutant because the expression of IMD2 cannot be induced during transcription elongation. In the case of the sub1ΔCT mutant, the function of Sub1 modulating the RNAPII rate during elongation would be affected [IMD2 gene, as a function of 6AU treatment (sub1Δ. All these data indicate that Sub1-HA actively participates in IMD2 transcription elongation via its CT region.In summary, the affected , likely reatment E, we obsSeveral orthologues of the human positive coactivator PC4 have been discovered in bacteriophages, bacteria, and eukaryotes, showing that it is an evolutionarily conserved factor. All of them are involved in different aspects of DNA metabolism and are characterized by the presence of an ssDBD ,10,11,12IMD2 transcription repression and the response to DNA damage [PC4/Sub1 proteins are similar in size, except for the budding yeast Sub1 A, which A damage ,53. Howesub1∆CT and spt5-194, which is not observed in the case of the ssDBD mutations (The truncation of the CT region produces a very low amount of the Sub1 protein, which is not due to defects in transcription, but it is likely due to protein instability/degradation. It is unknown if this region contains target sequences for Sub1 degradation. In any case, although low levels of the Sub1ΔCT protein only containing a functional ssDBD are produced, the interaction of the truncated protein with the gene promoters is stronger than in the case of a wild-type Sub1 protein F. Therefutations D. RemarkThe phosphorylation of PC4 negatively influences its dsDNA binding capacity, thereby fostering its escape from promoters ,57. In tOverall, our data indicate that the C-terminal region of Sub1 is key to understanding the dual role of Sub1 in transcription, which possibly occurs via the promotion of transition from initiation to elongation. Further, the data corroborate that Sub1 can act as a general regulator of RNAPII transcription during the whole transcription cycle by using different protein regions. Future research must precisely dissect the Sub1CT region and determine whether it contains distinct and functional domains, including sequences involved in its stability, and if its phosphorylation regulates Sub1 functions.


Genetic and environmental exposures cause variability in gene expression. Although most genes are affected in a population, their effect sizes vary greatly, indicating the existence of regulatory mechanisms that could amplify or attenuate expression variability. Here, we investigate the relationship between the sequence and transcription start site architectures of promoters and their expression variability across human individuals. We find that expression variability can be largely explained by a promoter’s DNA sequence and its binding sites for specific transcription factors. We show that promoter expression variability reflects the biological process of a gene, demonstrating a selective trade-off between stability for metabolic genes and plasticity for responsive genes and those involved in signaling. Promoters with a rigid transcription start site architecture are more prone to have variable expression and to be associated with genetic variants with large effect sizes, while a flexible usage of transcription start sites within a promoter attenuates expression variability and limits genotypic effects. Our work provides insights into the variable nature of responsive genes and reveals a novel mechanism for supplying transcriptional and mutational robustness to essential genes through multiple transcription start site regions within a promoter. Transcriptional regulation is the main process controlling how genome-encoded information is translated into phenotypes. Hence, understanding how transcriptional regulation influences gene expression variability is of fundamental importance to understand how organisms are capable of generating proper phenotypes in the face of stochastic, environmental, and genetic variation. Through differentiation, cells acquire highly specialized functions, but need to still maintain their general abilities to accurately regulate both essential pathways as well as responses to changes in the environment. To achieve robustness, regulatory processes must be capable of attenuating expression variability of essential genes , while sGenetic variation affects the expression level  of the mPrevious studies aimed at identifying processes involved in the regulation of gene expression variability have indeed revealed regulatory features mostly associated with the promoters of genes, such as CpG islands and TATA-boxes , the chrHere, we provide a comprehensive characterization of the sequences, TSS architectures, and regulatory processes determining variability of promoter activity across human lymphoblastoid cell lines (LCLs). We find that variability in promoter activity is to a large degree reflected by the promoter sequence, notwithstanding possible genotypic differences. Furthermore, the presence of binding sites for specific TFs, including those of the ETS family, are highly predictive of low promoter variability independently of their impact on promoter expression levels. In addition, we demonstrate that differences in the variability of promoters reflect their involvement in distinct biological processes, indicating a selective trade-off between stability and plasticity of promoters. Finally, we show that flexibility in TSS usage is associated with attenuated promoter variability. Our results reveal a novel mechanism that confers mutational robustness to gene promoters via switches between proximal core promoters. This study provides fundamental insights into transcriptional regulation, indicating shared mechanisms that can buffer stochastic, environmental, and genetic variation and how these affect the responsiveness and cell-type restricted activity of genes.To characterize human variability in promoter activities, we profiled TSSs using CAGE  as a measure of promoter expression dispersion, revealing how the normalized expression across individuals deviates from the mean for each identified promoter. We observed that the promoter CV2 decreases by increasing mean expression ; To assess which sequence features the CNN had learned to distinguish the classes, we calculated importance scores using DeepLift  for eachTo systematically test how predictive TF binding sites are of the variability of active promoters, we made use of binding sites predicted from motif scanning for 746 TFs . TF bindReverse engineering of the random forest classifier   allowed TFs associated with highly variable promoters are mostly related to tissue-specific or developmental regulation  while TFs predictive of low promoter variability are generally associated with ubiquitous activity across cell types and a diverse range of basic cellular processes . In addition, TFs predictive of high variability  tend to have binding sites in relatively few highly variable promoters while TFs predictive of low promoter variability  show a propensity for having binding sites present in a large number of promoters . This suAlthough the adjusted dispersion of promoters was separated from their expression level , we obseMany of the TFs identified as being predictive of low variability  belong to the ETS family of TFs , a largeTaken together, our results indicate that promoter sequence can influence both low and high promoter variability across human individuals independently from its impact on expression level. Our results further indicate that variable promoters exhibit highly diverse binding grammars for TFs that are associated with relatively few promoters, while a more uniform regulatory grammar is indicated for stable promoters, being highly associated with higher CpG content and ETS binding sites.The high performance of predicting promoter variability from local DNA sequence and the distinct TF binding profiles of low and highly variable promoters imply that attenuation and amplification of variability are driven by distinct regulatory mechanisms. This argues that favoring robustness (low variability) over plasticity (high variability) should reflect the biological processes where this provides regulatory advantages. Supporting this hypothesis, we observed that low variable promoters were highly enriched with basic cellular housekeeping processes, in particular metabolic processes . In contInterestingly, the same features found to be predictive of low and high promoter variability across individuals, including CpG-content and TATA-boxes (TBP binding sites), are also associated with low and high transcriptional noise across individual cells . The pre–16, two sample t-test; –16; In agreement, genes of highly variable promoters tend to have higher transcriptional noise than those of low variable ones across GM12878 single cells  as posited, while low variable promoters were mostly unaffected or down-regulated  promoters according to their positional width . Althoug–16, OR = 2.04, Fisher’s exact test). Low variable promoters, on the other hand, were enriched among those of size 10–25 bp , but beyond this width the association is lost  of their CAGE-inferred TSSs within a width of 1–5 bp (p<2.2 × 10 is lost . To simu is lost . This me is lost . Consist is lost . We reas is lost  demonstr is lost .UFSP2 (RIT1 (10-transformed CV2) of local-maxima decomposed promoters. Interestingly, many of the decomposed promoters showed a vastly different variability across individuals compared to the promoters they originate from  nucleosome positioning, and changes in +1 nucleosome positioning can alter the preferred TSS . Hence, Taken together, our results demonstrate that flexible usage of core promoters within promoters with permissive nucleosomal architectures provide stability to the overall expression of a large subset of gene promoters with low variability.While genetic variants associated with gene expression levels  frequently occur within gene promoters, they are rarely found associated with housekeeping or ubiquitously expressed genes, and when they are, they have limited effect sizes . One exp–16, To test if flexibility in core promoter usage within a promoter may cause mutational robustness, we first performed local eQTL analysis on promoters (within 25 kb). We tested both the association between the genotypes of common genetic variants (MAF ≥10%) and the expression of promoters . Despite the limited association of the variant with decomposed promoter 3, it still had a noticeable association with the overall promoter activity , but in opposite directions  promoters  are less affected by the presence of genetic variants compared to highly variable promoters. In addition, we find that part of this tolerance can be explained by a, previously unreported, mechanism of mutational robustness through plasticity in TSS usage. The prevalence of expression-independent decomposed promoters within low variable promoters, as suggested by low pairwise correlation, indicates an extensive regulatory role of TSS plasticity in attenuating expression variability of essential genes.In this study, we provide an extensive characterization of promoter-associated features influencing variability in promoter activity across human individuals and demonstrate their importance for determining stability, responsiveness, and specificity. Overall, we show that the local DNA sequence, putative TF binding sites, and transcription initiation architecture of promoters are highly predictive of promoter variability.Although the classifier based on TF binding site sequence and CpG island status was able to predict promoter variability well (AUC = 0.79 on the test set), it did not perform as well as the CNN model (AUC = 0.84 on the test set), which was trained on DNA sequence alone. This indicates that additional information influencing variability may be encoded within the promoter sequence. For instance, the density and variations of Initiator elements within promoters could influence TSS flexibility . In addiDespite a clear association with high promoter CpG content and housekeeping genes, low variable promoters were not strongly associated with a broader width, which we would expect from promoters in CpG islands and with housekeeping activity . Rather,Flexibility in TSS usage may ensure transcriptional robustness of genes both in different environments and in the face of genetic variation. Since promoter shape is generally conserved across orthologous promoters , it is pNotably, many of the promoter features we, and others , have idWe observed less restrictive +1 nucleosome positioning across individuals at low variable promoters with flexible TSS usage. Notably, these promoters are also associated with a fuzzy +1 nucleosome positioning across cells within an individual. This indicates that positional shifts of nucleosomes in coordination with shifts in core promoter usage may be due to an inherent property of these promoters in addition to the influence by genetic variation. Our observations indicate that multiple configurations of accessible chromatin may exist for low variable promoters across single cells, which may cause stochastic TSS selection with no or only low impact on expression level. This is compatible with a high density of pyrimidine/purine (YR) dinucleotides within broad promoters , which pIt is important to note that the regulatory programs of EBV-immortalized LCLs, like other cultured cells, have been shown to be susceptible to genotype-independent sources of variation, such as primary cellular heterogeneity and EBV’s viral cellular reprogramming . While wTaken together, our results favor a model in which the regulation of transcriptional noise across single cells reflects specificity across cell types and dispersion across individuals with shared mechanisms conferring stochastic, genetic and environmental robustness . There aMycoplasma hominis, which showed no indication of contamination for any sample  and to have at least 10 read counts in more than 10 LCLs. The resulting 29,001 gene-associated CAGE-inferred promoters were later decomposed by local maxima decomposition to split multi-modal tag clusters  were identified from 5’ ends of mapped CAGE reads for each strand separately. The expression of CTSSs for each LCL was quantified from the number of CAGE reads sharing 5’ ends using CAGEfightR (version 1.10) . To idenGene expression data quantified in the recount2 project  using GeGM12878 10 X Genomics scRNA-seq data  was down2) of TPM-normalized promoter (or decomposed promoter) expression across the LCL panel and subsequently log10-transformed. Adjustment of the mean expression-dispersion relationship was performed by subtracting the expected log10-transformed dispersion for each promoter according to its expression level, using a running median  of raw dispersions (log10 CV2) ordered by mean expression level (TPM) across the panel, as described elsewhere  from the human reference genome (hg38) to predict low and highly variable promoter activity as output. Although CAGE-inferred promoters varied in width, we made use of fixed-length 600bp sequences for each promoter centered on its pooled CAGE summit CTSS. 600bp was used to make sure that sequences influencing promoter variability contained within regions that could cover a central open chromatin site (150–300bp) as well as within flanking nucleosomal DNA (150–200bp) were captured, where also most of the expression output of a promoter originate .The model  consistshttps://github.com/keras-team/keraPromoter sequences from chromosomes 2 and 3 were used as the test set and those from the remaining chromosomes were used for training and hyperparameter tuning with a fivefold cross-validation. Hyperparameters were manually adjusted to yield the best performance on the validation set. The neural network model was implemented and trained in Keras .We initially used the first and third quartiles (Q1 and Q3) to distinguish low variable promoters (≤Q1) from highly variable promoters (>Q3), corresponding to an adjusted logTo interpret the neural network model we used DeepLIFT , a featuWe used Tomtom  (hg38) were obtained from JASPAR    and for 0, hg38) .Similarly to the neural network model, promoters from chromosomes 2 and 3 were only used as the test set. The remaining promoters were used for training and hyperparameter tuning with fivefold cross-validation. The Random Forest model was implemented and trained in Scikit-learn (version 2.3.0) with 500 trees, a maximum depth of trees of 10, 50samples split per node, and 50samples to be at a leaf node. The remaining hyperparameters were kept with default values.Instead of selecting features directly from the Random Forest model, the BorutaShap package  was usedTo evaluate the contribution of TF binding site presence for predicting promoter variability, we trained a baseline model based on CpG content status and TATA-box presence only. CpG content status (CpG observed/expected ratio>0.5) and the presence/absence of predicted TBP binding sites were used as features for predicting high and low variability using a decision tree classifier. The decision tree model was implemented and trained in Scikit-learn (version 2.3.0) using default parameters. Training and test data were defined as for the CNN and Random Forest models.RNA-seq gene expression values across tissues were obtained from the GTEx consortium . PromoteCAGE gene expression values across cell types were obtained from the FANTOM5 project . The avehttps://github.com/macarthur-lab/gene_lists; Gene lists for FDA approved drug-targets , essentiGM12878 cells were obtained from the NHGRI Sample Repository for Human Genetic Research at Coriell Institute for Medical Research. Unstimulated GM12878 cells and those stimulated with 25 ng/ml TNF-α for 6 hr were harvested with four replicates for each condition. Cell culturing, CAGE library preparation and mapping were done as described above for the LCL panel. CAGE reads supporting each of the final filtered promoters identified in the LCL panel were counted for each replicate using CAGEfightR  data from 7 EBV-immortalized LCLs  were obtained from GEO (GSE36979) .The locations and fuzziness scores of nucleosomes were called with DANPOS2 (version 2.2.2)  using thPositional cross correlations were calculated between CAGE TSSs and 5’ ends of MNase-seq reads on the same strand at dominant TSS positions of CAGE-inferred promoters ±500bp (maximum lag 250) to identify their most likely separation. Cross-correlation analysis was performed on either pooled CAGE data  versus pooled MNase-seq data  or using only CAGE and MNase data from one LCL (GM18516). Finally, for both analyses, a weight for each promoter was calculated from the geometric mean of aggregated MNase and CAGE signals. This was used to calculate a weighted average of the cross correlations across considered promoters.We tested both the association between the genotypes of common genetic variants (MAF ≥10%) and the expression of promoters  and decomposed promoters , as well as their association with the contribution of to the overall expression of the encompassing (non-decomposed) promoter . prQTLs, dprQTLs, and frQTLs were mapped using the MatrixEQTL R package (version 2.3) . We contFor prQTL detection, all 29,001 promoters were tested using TPM-normalized expression values. For frQTLs, we calculated the fractional contribution of each decomposed promoter to the expression of its original promoter. To focus the dprQTL and frQTL analyses on relevant shifts in TSS usage, we considered only decomposed promoters whose expression accounted for at least 5% of the overall promoter expression in at least half of all LCLs and promoters with at least 2 such decomposed promoters, resulting in 37,663 decomposed promoters of 14,889 promoters.For each promoter, we tested common  biallelic SNVs  at a max This paper presents valuable findings about how human genetic variation impacts gene expression. Using a compelling analysis of new experimental data based on cell lines from 108 individuals, the authors uncover features that distinguish promoters with highly variable expression across individuals from those exhibiting low variability. This work and the associated resource will be of broad interest for further investigations of the interplay between genetic variation and gene expression control. 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 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and George Perry as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Promoter sequence and architecture determine expression variability and confer robustness to genetic variants" 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) Please consider alternative interpretations for the observations as suggested by the reviewers, and perform additional checks to consider which ones may be more plausible.2) It would be great if the authors could provide additional support for promoter plasticity, or provide an alternative explanation that is better supported by their analyses.3) The reviewers have made a case to address the role of initiator (YR) motifs and nucleosome positioning, especially regarding the decomposed promoters.4) Additionally, the reviewers suggested considering how the possible heterogeneity of gene expression may have an impact on the interpretation of the results.5) Please reassess the interpretations of observed correlations as causation. A general change to a more cautious language that follows more closely the data is overemphasised is advised.6) Improved clarity on some of the descriptions is also requested, like the one mentioned by the reviewers about the ETS family.Reviewer #1 (Recommendations for the authors):– I think that the data is great and that you analysed it well. I just think that many of your interpretations of the results are not the only possible ones, and at least some are unlikely. I have addressed this in the public part of the review and would recommend that you consider each point and perform a couple of additional checks where they can decide which interpretation is more likely.– The role of initiator (YR) motifs and nucleosome positioning, especially with regard to the decomposed promoters, should be addressed.– Anecdotally, from looking at many GTEx expression profiles of individual genes, genes in EBV-immortalised lymphoblastoid cell lines often show expression levels of multiple otherwise broadly expressed genes that are outliers to all other cell/tissue types in the corresponding panels. How much is known about heterogeneity of gene expression across EBV-transformed cells in general, and how would that affect the interpretation of your analyses?Reviewer #2 (Recommendations for the authors):– The authors use language that pimples a causal relationship. A great example is on the bottom of page 10: "promoter sequence can influence both low and high" (the word "influence"); and "several TFs were identified as contributing partially to the variability in promoter expression" (the word "contributing"). Another example is the transition into the next section, where the authors state that "[DNA sequence motifs provide] distinct mechanisms for attenuating or amplifying variability".The authors do not directly show that these motifs are casual for promoter variability, and I'm not inclined to believe they are causal. I think the most natural model is one where natural selection constrains the expression of genes with some promoter architectures  more than others because of their important biological function. I think the authors should use language indicative of correlation rather than causation throughout these sections.– The authors state on page 10 that "individual ETS family members are independently strong predictors of low promoter variability". But I think the ETS family members being identified simply reflects their shared DNA sequence, as the authors imply in the next sentence. I think it's confusing to readers to state this as a result, then state the obvious technical reason it happens. I think this section should be reworked.– Much of the evidence for genetic plasticity currently involves correlations between promoter variability (defined as the squared coefficient of variation across the population) and either piQTL presence or effect size. I do not find these correlations to be all that convincing to the argument for plasticity as it stands. Promoter variability, as defined by the authors, essentially measures variation across the population, which will also lead to that promoter being more likely to have a piQTL. Thus, the logic seems at least somewhat circular as it stands. By contrast, I found the arguments around frQTLs to be much more convincing – the idea that specific SNPs can change the fraction of a decomposed promoter's contribution is much more helpful to the authors' argument. As it stands, though, this is a fairly minor portion of the manuscript buried in the results paragraphs. I think it would help if the authors played this up a bit more.Along these same lines, I wonder if the authors have individually tested the decomposed promoters for decomposed-prQTLs (maybe dprQTLs)? Identifying nearby dpr-QTLs that have opposite effects could also support the author's argument in a way that does not follow by definition. Reviewer #1 (Recommendations for the authors):– I think that the data is great and that you analysed it well. I just think that many of your interpretations of the results are not the only possible ones, and at least some are unlikely. I have addressed this in the public part of the review and would recommend that you consider each point and perform a couple of additional checks where they can decide which interpretation is more likely.We thank the reviewer for constructive criticism and for good suggestions on how to improve the manuscript. In our revised manuscript we have followed the recommendations by the reviewer and:– Toned down implied causal relationships and added additional interpretations to our results, including YR positional preferences and the suggested model of competition between core promoters (decomposed promoters) underlying low variable promoters– Performed additional analyses on nucleosome positioning within decomposed promoters of low variable promotersIn all, we believe these revisions have substantially improved our manuscript and even strengthened our previous conclusions.– Anecdotally, from looking at many GTEx expression profiles of individual genes, genes in EBV-immortalised lymphoblastoid cell lines often show expression levels of multiple otherwise broadly expressed genes that are outliers to all other cell/tissue types in the corresponding panels. How much is known about heterogeneity of gene expression across EBV-transformed cells in general, and how would that affect the interpretation of your analyses?Thank you, this is a very good point. Such heterogeneity does not only affect our study but most studies relying on individually generated cell cultures. Previous studies have shown that EBV-immortalized LCLs can in fact show genotype-independent variability, due to factors not fully understood. In the revised version of the manuscript, we acknowledge that using immortalized cell cultures has its caveates when studying expression variation.“It is important to note that the regulatory programs of EBV-immortalized LCLs, like other cultured cells, have been shown to be susceptible to genotype-independent sources of variation, such as primary cellular heterogeneity and EBV’s viral cellular reprogramming . While we cannot exclude that such sources may influence the measured variability of some genes, we speculate that non-genetic variation would rather dampen than elevate the associations and effect sizes we report here, in particular the decomposed promoter switches highlighted by our genotype association analysis.”Hence, one could speculate that the phenomenon of stabilizing promoter switches is even more common than what we can observe with our current data.Reviewer #2 (Recommendations for the authors):– The authors use language that pimples a causal relationship. A great example is on the bottom of page 10: "promoter sequence can influence both low and high" (the word "influence"); and "several TFs were identified as contributing partially to the variability in promoter expression" (the word "contributing"). Another example is the transition into the next section, where the authors state that "[DNA sequence motifs provide] distinct mechanisms for attenuating or amplifying variability".The authors do not directly show that these motifs are casual for promoter variability, and I'm not inclined to believe they are causal. I think the most natural model is one where natural selection constrains the expression of genes with some promoter architectures  more than others because of their important biological function. I think the authors should use language indicative of correlation rather than causation throughout these sections.– The authors state on page 10 that "individual ETS family members are independently strong predictors of low promoter variability". But I think the ETS family members being identified simply reflects their shared DNA sequence, as the authors imply in the next sentence. I think it's confusing to readers to state this as a result, then state the obvious technical reason it happens. I think this section should be reworked.We thank the reviewer for pointing out these concerns. We have carefully reworded the mentioned sentences and other conclusions of the text to better reflect our findings and to clarify their meanings.– Much of the evidence for genetic plasticity currently involves correlations between promoter variability (defined as the squared coefficient of variation across the population) and either piQTL presence or effect size. I do not find these correlations to be all that convincing to the argument for plasticity as it stands. Promoter variability, as defined by the authors, essentially measures variation across the population, which will also lead to that promoter being more likely to have a piQTL. Thus, the logic seems at least somewhat circular as it stands. By contrast, I found the arguments around frQTLs to be much more convincing – the idea that specific SNPs can change the fraction of a decomposed promoter's contribution is much more helpful to the authors' argument. As it stands, though, this is a fairly minor portion of the manuscript buried in the results paragraphs. I think it would help if the authors played this up a bit more.We thank the reviewer for this comment. We do not dispute that more variable promoters are more likely to be associated with a prQTL. However, we feel that showing that this is in fact the case further highlights the interesting finding of frQTLs of low variable promoters. Despite the fact that these promoters do not show strong prQTL effect sizes, we see evidence of variability in TSS usage, as revealed by frQTLs and decomposed promoter eQTLs . This indicates that low variable promoters are indeed affected by genetic variation but that these effects are masked due to compensatory switches between core promoters.Along these same lines, I wonder if the authors have individually tested the decomposed promoters for decomposed-prQTLs (maybe dprQTLs)? Identifying nearby dpr-QTLs that have opposite effects could also support the author's argument in a way that does not follow by definition.We thank the reviewer for this great suggestion. In the revised version of our manuscript, we also tested the association between the genotypes of common genetic variants (MAF ≥ 10%) and the expression of decomposed promoters . Interestingly, while low variable promoters are less likely to have prQTLs with large regulatory effects, dprQTLs exhibited similar prevalence  and maximum effect sizes  across promoter variability classes. We believe these results strengthen our previous results from frQTLs.


The complex and dynamic three-dimensional organization of chromatin within the nucleus makes understanding the control of gene expression challenging, but also opens up possible ways to epigenetically modulate gene expression. Because plants are sessile, they evolved sophisticated ways to rapidly modulate gene expression in response to environmental stress, that are thought to be coordinated by changes in chromatin conformation to mediate specific cellular and physiological responses. However, to what extent and how stress induces dynamic changes in chromatin reorganization remains poorly understood. Here, we comprehensively investigated genome-wide chromatin changes associated with transcriptional reprogramming response to heat stress in tomato. Our data show that heat stress induces rapid changes in chromatin architecture, leading to the transient formation of promoter-enhancer contacts, likely driving the expression of heat-stress responsive genes. Furthermore, we demonstrate that chromatin spatial reorganization requires HSFA1a, a transcription factor (TF) essential for heat stress tolerance in tomato. In light of our findings, we propose that TFs play a key role in controlling dynamic transcriptional responses through 3D reconfiguration of promoter-enhancer contacts. Here the authors show genome-wide chromatin changes and transcriptional reprogramming occur in response to heat stress in tomato, leading to the transient formation of promoter-enhancer contacts to drive the expression of heat-stress responsive genes. Furthermore, they show chromatin spatial reorganization requires HSFA1a, a transcription factor (TF) essential for heat stress tolerance in tomato. Chromatin is a compact, multiscale-organized, and dynamic structure: in eukaryotes, DNA is wrapped around histones octamers 4, and this structure folds again to form chromatin loops, domains and territories, organizing the genome at multiple scales5. Chromosomes occupy defined territories inside the nucleus, as revealed by three-dimensional -fluorescence in situ hybridization (3D-FISH) methods and by high-throughput chromosome conformation capture (Hi-C) that show preferential interactions of each chromosome with itself6.The linear conception of the genome has been replaced by an emerging understanding of the highly complex and dynamic 3D organization of chromatin within the nucleus7. TAD associations form “chromatin compartments”. Active (A) compartments represent active chromatin defined by active histone marks, a high density of genes, and an elevated transcription rate supported by high RNA polymerase II (RNAPII) activity. In contrast, B compartments represent inactive chromatin defined by repressive histone marks, DNA methylation, and an increased number of transposable elements9. At a smaller scale, the formation of chromatin loops brings together distal regulatory sequences with promoter regions, or isolates them from promoters10. These short and long-range interactions modulate the expression of neighbouring and distant genes11. Chromatin loop may involve self-interacting transcription factors (TFs), that bring together the genome regions they bind13.In mammals, chromosomes are further organized into topologically associating domains (TADs), genomic regions that preferentially form contacts with each other rather than with surrounding sequences. TADs have been proposed to allow coordinated regulation of genes situated inside the TAD by allowing contact with distal regulatory sequences with in the TAD and limiting their access to sequences outside of the TAD; although this model is still under debate14. However, plant chromatin organization shows several differences from that in animals. First, plant TAD-like structures represent only about 25% of the rice (Oryza sativa) genome15 compared to 75% for humans16. However, this low number of TADs in rice be a consequence of how we currently define TADs and may not reflect the characteristics of plant genomes17. Likewise in wheat (Triticum aestivum), TAD-like structures correspond to large heterochromatin domains highly enriched in transposable elements, rather than to groups of co-regulated genes3. The term “intergenic condensed spacers” (ICONS) has been proposed to describe these structures, as they seem to condense silent chromatin to allow loop formation between genes located outside of the ICONS, forming transcription factories. Second, mechanisms controlling chromatin folding differ between plants and animals. In animals, cohesin complexes extrude chromatin via their ring-like shape until the chromatin binds at two convergent CTCF proteins at specific CTCF binding sites that define TAD boundaries18. However, plant genomes apparently lack CTCF homologues, therefore how TAD-like domains are structured in plants is unclear.All these organizational levels have been defined in animals and observed in plants: A and B compartments alternate along the genome, with A compartments predominantly in telomeric regions and B compartments in pericentromeric regions in various crop species19, depending on cell type20 or in response to changes in environmental conditions21. In plants, some changes in chromatin architecture in response to various stresses likely coordinate global transcriptome modifications for appropriate cellular and physiological responses. The importance of chromatin conformation for gene regulation in response to environmental stress in Arabidopsis thaliana has been well documented22. However, whether and how stress induces chromatin reorganization in crops is less understood, and only a few studies have examined the plant response to temperature changes. Rice chromosomes decondense under cold stress, decreasing long-range interactions above 1 Mb and increasing A-B compartment interactions15. Conversely, exposure to heat stress reduces short-range interactions between A-B compartments, as A-A and B-B compartment interactions decrease15.Although the overall structure of chromatin architecture is very robust across organs and growth conditions, detailed analyses of chromatin folding revealed it can be modulated during developmentZea mays)24. Modulating these interactions in response to stress is likely functionally relevant, as has been shown in Drosophila25 and human cells26, but remains largely unexplored in plants.Gene expression is also controlled by establishment or dissociation of interactions between distal regulatory elements (REs) and promoters, such as enhancer-promoter interactions. In plants, such long-range interactions between promoters and distal regulatory sequences are most prominent in species with large genomes, such as maize (Solanum lycopersicum) during heat stress (HS). We find that HS induces profound changes in chromatin accessibility, as well as dynamic interactions between promoters and distal REs. We further show that the Heat Shock Factor (HSF) TF HSFA1a, a master transcriptional regulator of heat stress responses in plants27, plays a key role in the dynamic formation of promoter-enhancer contacts in response to heat at several loci. Together, these results allow us to propose a model in which changes in chromatin accessibility and binding of HSFA1 promote the formation of promoter-enhancer contacts to induce gene expression in response to HS.Here, we studied the chromatin-based regulation of RE-promoter contacts and how nuclear DNA reorganization affects gene expression. To this end, we captured the temporal dynamics of these contacts using a time-course analysis in tomato  and histone H3 lysine 27 monomethylation H3K27me1), associated with transcriptionally active chromatin and constitutive heterochromatin, respectively. Immuno-staining revealed these marks were not homogeneously distributed in the nucleus, indicating distinct euchromatin and heterochromatin compartments in tomato: euchromatin occurred in the centre of the nucleus while constitutive heterochromatin occurred at the nuclear periphery  in multiple treatments 33. We postulated that this feature could also be present at distal active enhancers in plants. To test this hypothesis, we performed RNAPII ChIP-seq on tomato plants subjected to HS and analyzed its accumulation on distal REs specifically active at 1 hour after HS. We identified a large number of RNAPII binding sites near TSSs, but also present at the distal REs identified by our chromatin analyses  interactions for which neither anchor is not accessible, (ii) interactions for which only one anchor is not accessible, and (iii) interactions for which both anchors are accessible. The strength of interactions in which both anchors are accessible was stronger than the interactions in which only one anchor or no anchors are accessible , a powerful tool to delineate spatial and functional relationships at specific chromatin regions. To this end, we generated a biotinylated RNA bait library specifically targeting 212,735 promoter regions  according to the structural features of their oligomerization domains36. Class A HSFs are essential for transcriptional activation, whereas class B and C HSFs have no activator function because they lack the acidic amino acid residue containing motifs37. Previous studies have shown that HSFA1a is the master regulator of the transcriptional HS primary response39. Interestingly, previous work in S. lycopersicum showed that HSFA1a is both a master regulator of the HS response and basal thermo-tolerance38. We thus hypothesized that HSFA1a may be implicated in RE-Promoter interactions during early HS responses. To test this, we first determined HSFA1a binding sites using a TF-DNA binding assay called DNA affinity purification and sequencing (DAP-seq)40. This approach revealed a large number (5034) of putative HSFA1a binding sites  carrying a hairpin RNA construct that downregulates HSFA1a expression27  being downregulated and 61 (24.7%) upregulated       Fig. . These rst) Fig. . With re-B) Fig. . The for04) Fig. . We usedst) Fig. . Similar42. Although the precise mechanisms underlying transcription factories organization remain poorly understood, they are thought to play a critical role in clustering co-regulated genes43. The presence of transcription factories in the tomato nucleus reveals their importance in transcriptional regulation and in particular on the optimization of transcription in response to environmental cues by concentrating TFs in discrete nuclear compartments.This study presents a deep analysis of the tomato nuclear architecture by integrating capture-Hi-C, ATAC-seq, ChIP-seq, HiChIP, and RNA-seq data. Results obtained highlight the specific functionalities of the different levels of chromatin spatial organization underscoring the regulatory importance of nuclear organization as a whole. Chromatin conformation in tomato is strongly compartmentalized and organized around focalized sites of transcription in the active chromatin that resemble transcription factories21. In metazoan interphase nuclei, chromatin is organized into TADs, which are thought to bring together co-regulated genes. In Drosophila, HS induces dramatic rearrangement of the 3D nuclear organization44, suggesting that TAD organization of metazoan genomes is plastic and is reconfigured in response to environmental changes25. However, a recent study in human and Drosophila cells has shown that HS only affects a subset of enhancer–promoter interactions whereas TADs and compartment structures remain mostly stable upon HS45. In plants, HS induces high-order reorganization of the constitutive heterochromatin compartment28. In line with these results, we observed that HS in tomato negatively affects the chromatin interactions between constitutive heterochromatin regions, thus affecting the strength of constitutive heterochromatin compartmentalization.Temporal and spatial gene expression is influenced by dynamics chromatin changes and is implicated in the control of developmental programs as well as in response to environmental stresses47. In animals, distal REs are associated with the specific histone marks H3K27ac and H3K4me1, defining a specific chromatin state48. By contrast, in plants, studies in maize and wheat have proposed that H3K27ac, H3K4me3 and H3K9ac are histone marks preferentially associated with distal REs51. Here, we focused on differentially accessible REs as a proxy for active REs and found that active distal and proximal REs displayed different chromatin signatures. We have found that in tomato distal REs are characterized by the presence of nucleosomes rich in H3K9ac and H3K18ac and devoid of H3K4me3, flnaking the region with maximal accessibility. Importantly, we demonstrated that some of these RE are capable to act as transcriptional enhancers.REs are non-coding DNA sequences containing binding motifs for one or several TFs involved in transcriptional regulation of target genes, and are also key components of the 3D chromatin organization. These sequences act as proximal or distal RE and their activity is reflected by their accessibility, which is likely controlled by the action of pioneer TFs and chromatin modifiers56. In plants, RE-promoter interactions were analyzed in maize using HiChIP for H3K4me3, H3K27me3 and H3K27ac, which identified long range RE-promoter interactions that contribute to the control of gene expression58. This analysis uncovered the existence of long-range RE-promoter interactions. In line with this observation, using both capture-Hi-C and HiChIP approaches in tomato, several thousands of RE-promoter interactions were discovered. Notably, we have found examples for a promoter interacting with several REs as well as examples where one RE interacts with multiple genes. These two types of chromatin could be defined as promoter-centric hubs or RE-centric hubs respectively59.3D chromatin architecture dynamically controls the access of REs to their target genes by promoting or inhibiting RE-promoter interactions. RE sequences can be hundreds to thousands of base pairs upstream or downstream from the gene they regulate, making it difficult to define which REs control which genes. The rapid development sequencing technologies has enabled multiple high-throughput experimental methods to measure chromatin interactions, including Capture-Hi-C and HiChIP, which are particularly useful to detect enhancer–promoter interactions. In animals, promoter C-Hi-C has been used for the analysis of long-range RE-promoter interactions and their control of gene expression using different cell types. These analyses have revealed a massive change in RE-promoter interactions taking place during cellular differentiation61. Plants genomes appear to lack CTCF homologues, but SMC complexes are highly conserved. Therefore, this loop extrusion may be conserved in plants and involve yet unknown factors that act as insulators. In addition to the action of SMC and CTCF, TFs serve as protein anchors and determine 3D genome organization35. TFs may mediate RE-promoter interactions through different mechanisms, including direct protein homo-dimerization62 and co-factor protein recruitment to generate oligomers35. In Arabidopsis, the CONSTANS TF plays a crucial role in forming of an enhancer-promoter loop to control the expression of FLOWERING LOCUS T63, thus suggesting that TFs also impact 3D chromatin organization in plants. However, most of studies in plants have focused on deciphering the role of TFs in 3D chromatin architecture and gene regulation under steady-state conditions, thus providing clues on the mechanisms involved in the maintenance of RE-promoter interaction rather than in their formation. To decipher the molecular mechanisms involved in establishing distal RE-promoter interactions, we carried out a multidimensional study combining stress at different time points. Our data show that 3D chromatin interactions are dynamically modified by HS to allow a temporally evolving connection between a diversity of activated REs and promoters. Furthermore, our integrative analyses revealed that the binding motif of HSF was over-represented in the sequences involved in RE–promoter contacts at the onset of HS treatment. These data suggest that transcription factors of the HFS family play a major role in RE–promoter contact formation. Studies in yeast have shown that HSF1 mediates the clustering of its targets and dynamically influences the reconfiguration of the genome64. Our data for tomato indicates that HSFA1a plays a critical role in the dynamic formation of promoter-enhancer contacts and in controlling the transcriptional response at the onset of HS. Future work shall help determine whether loop formation is a pre-requisite for transcription activation, or is rather a consequence of it. Few studies have assessed the dynamic role of TFs in the formation of chromatin loops in response to a stimulus, even in animals. One study revealed that KLF4 and ZNF750 TFs, rather than cohesins, are key to establishing of RE-promoter contacts to promote gene expression during epidermal cell differentiation55. Collectively, our findings shed light on the complexity of distal and proximal RE contact loop formation and provides evidence for a key role of TFs in controlling transcriptional response through the 3D reconfiguration of the chromatin structure in animals and plants.One of the mechanisms proposed to shape the 3D genome structure in animals is the loop extrusion model, that involves the structural maintenance of chromosomes (SMC) cohesin complex and the zinc-finger DNA-binding protein CTCFSolanum lycopersicum) lines used in this study were in the cv. M82 and cv. Moneymaker backgrounds. The hsfa1a mutants line1 and line2 were obtained from Dr.Fragkostefanakis’s lab27. Seeds were directly sown on soil and plants grown in growth chambers at 24 °C under long-day (16 h light) conditions. For heat treatment, 4-week-old plants were treated at 45 °C for 1 h and 6 h in a climatic chamber . The fourth leaf was used for all experiments in this research.Tomato  of H3K9me2 , H3K9ac , H3K27me1  and RNAPII . Slides were washed and then incubated for 1 h at room temperature in the dark with Goat anti-Rabbit Alexa Fluor Plus 488 (A11034 Invitrogen) and Goat anti-Mouse Alexa Fluor Plus 555 (A32727 Invitrogen) or with Goat anti-Rabbit Alexa Fluor Plus 555 (A32732 Invitrogen) and Goat anti-Mouse Alexa Fluor Plus 488 (A32723 Invitrogen) secondary antibodies (400X diluted). DNA was counterstained with 4,6 diamidino-2-phenylindole (DAPI) in SlowFade Diamond Antifade mounting media. Slides were directly imaged on a confocal microscope (Zeiss Microsystems).Immunostaining experiments were performed according to Latrasse et al66 and Jegu et al67. DNA libraries were checked for quality and quantified using a 2100 Bioanalyzer (Agilent) and subjected to 2 × 75 bp high-throughput sequencing by NextSeq 500 (Illumina). Two independent biological replicates were generated.Approximately 100 mg of 4-weeks-old tomato fourth leaves were ground, passed through a 100 µm filter and the nuclei were isolated using nuclei isolation buffer . The nuclei were re-suspended in 2× TD buffer  and 2.5 µl of Tn5 transposase (Illumina FC-121-1030). The transposition reaction was performed at 37 °C for 30 min, and DNA was purified using a Qiagen MinElute Kit . DNA libraries were amplified for a total of 10 cycles as described by Buenrostro et alTotal RNA was extracted from the fourth leaves of 4-week-old tomato with Nucleospin RNA kit (Macherey-Nagel), according to the manufacturer’s instructions. RNA-seq libraries were prepared from 2 μg of total RNA using NEBNext Ultra II Directional RNA library Preparation Kit (NEB) according to the manufacturer’s instructions. RNA libraries were checked for quality and quantified using a 2100 Bioanalyzer (Agilent) and subjected to 1 × 75 bp high-throughput sequencing by NextSeq 500 (Illumina). Two independent biological replicates were generated.3 using DpnII enzyme (New England Biolabs). DNA libraries were prepared using NEBNext Ultra II DNA library preparation kit (NEB) according to the manufacturer’s instructions (10 cycles for the PCR amplification step). DNA libraries were checked for quality and quantified using a 2100 Bioanalyzer (Agilent) and the libraries were subjected to 2 × 75 bp high-throughput sequencing by NextSeq 500 (Illumina). Two independent biological replicates were generated.In situ Hi-C experiments were performed according to Concia et al.For C-Hi-C, the in situ Hi-C libraries were used for the capture step. Capture was performed by using SureSelect XT Target Enrichment System for Illumina Paired-End Multiplexed Sequencing Library (Agilent) according to the manufacturer’s recommendations. All tomato cultivar M82 promoters were selected1.5 kb in length and 212,735 probes (length of each probe = 120 bp) were designed to catch all promoters. The capture step was performed using 1 µg of in situ Hi-C libraries following the manufacturer’s recommendations. The quality of the libraries was assessed using a 2100 Bioanalyzer (Agilent), and the libraries were subjected to 2 × 75 bp paired-end high-throughput sequencing by NextSeq 500 (Illumina). Two independent biological replicates were generated68 was then applied using the DpnII restriction enzyme (NEB) and 3ug of anti-RNAPII antibody . The quality of the libraries was assessed using a Bioanalyzer (Agilent), and the libraries were subjected to 2 × 75 bp paired-end high-throughput sequencing by NextSeq 500 (Illumina).Nuclei were isolated from the fourth leaves of 4-week-old tomato plants using the same procedure as for the in situ Hi-C experiments. The Hi-ChIP protocol from Mumbach et al.69 using 3ug of anti-H3K27me1 , anti-H3K9ac , anti-H3K14ac , anti-H3K18ac , anti-H3K27ac , anti-H3K4me3 , and anti-RNAPII  antibodies. ChIP-seq libraries were prepared from 10 ng of DNA using NEBNext Ultra II DNA Library Prep Kit for Illumina (NEB) according to the manufacturer’s instructions. Two independent biological replicates were generated for each time point of heat stress. DNA libraries were checked for quality and quantified using a 2100 Bioanalyzer (Agilent) and subjected to 1 × 75 bp high-throughput sequencing by NextSeq 500 (Illumina).ChIP-seq assays were performed on the fourth leaves of 4-week-old tomato plantsaccording to Bio-protocol of Ramirez-Prado et al.40 with modifications. Briefly, gDNA was extracted from 4-week-old tomato fourth leaves, fragmented, and a library was generated using NEBNext Ultra II DNA library preparation kit . SolycHsfA1a open reading frames were transferred into the Gateway-compatible pIX-HALO expression vector and expressed using Promega TNT Coupled Wheat Germ Extract Systems . The expressed proteins were immobilized on Magne HALO-Tag beads , washed, and incubated with the DNA library. After bead washing, DNA was eluted and amplified with indexed NEBNext primers (20 PCR cycles were used). DNA libraries were checked for quality and quantified using a 2100 Bioanalyzer (Agilent) and subjected to 1 × 75 bp high-throughput sequencing by NextSeq 500 (Illumina). Two independent biological replicates were generated.AmpDAP-seq assays were performed according to Bartlett et al.Four-week-old tomato fourth leaves were cross-linked in 1% (v/v) formaldehyde at room temperature for 20 min. Cross-linked plant material was ground and nuclei were isolated and treated with 0.5% SDS at 62 °C for 5 min. The SDS was then supplemented with 2% Triton X-100. Digestions were performed overnight at 37 °C using 150U of DpnII enzyme (New England Biolabs). Restriction enzymes were inactivated by adding 1.6% SDS and incubated at 62 °C for 20 min. SDS was supplemented with 1% Triton X-100. DNA was ligated by incubating at 22 °C for 5 h using 4000U of T4 DNA ligase (Fermentas). Reverse crosslinking was performed by overnight treatment at 65 °C. DNA was recovered after Proteinase K treatment by phenol-chloroform extraction and ethanol precipitation. Relative interaction frequencies were calculated by quantitative PCR using 15 ng of DNA. A region uncut by DpnII was used to normalize the amount of DNA. Details for primers used for 3C-qPCR are listed in Supplementary Data Luciferase activity was quantified using reagents from the Dual Luciferase Reporter Assay System  according to the manufacturer’s instructions.70 was used for trimming with the following parameters: minimum length of 36 bp; mean Phred quality score >30; leading and trailing bases removal with base quality <5. The reads were mapped onto the M82 V1.071 assembly using Bowtie2 V2.3.572 with mismatch permission of 1 bp  were trimmed and reads <30 bp after trimming were dropped. The trimmed files were then processed with HiC-Pro V2.11.479 for different resolutions (500 bp for short-range interactions and 20 kb for long-range interactions). For the short range interactions, both anchors of the interactions (genomic bins) were annotated with genes using bedtools intersect80. We removed interactions without any gene annotations, self-loops, and duplicates.Valid pairs generated were further analyzed using HOMER V4.11Further information on research design is available in the Supplementary InformationDescription of additional Supplementary FileSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


Motif scanning of enriched sequences revealed a discrete 14 nucleotide‐wide motif found most prominently at the junction between the protein coding region's termination sequence and the start of the 3′ untranslated region . Proof of concept investigation of motif location along enriched transcripts Myh1 and Myl4 revealed 3′ UTR binding, suggesting RBM3 involvement in transcript half‐life regulation. Together, these results demonstrate the potential influence of RBM3 in reshaping the skeletal muscle proteome through post‐transcriptional regulation of mRNAs crucial to muscle adaptations.Post‐transcriptional regulation of gene expression represents a critical regulatory step in the production of a functional proteome. Elevated expression of post‐transcriptional regulator RNA binding motif protein 3 (RBM3), an RNA binding protein in the cold‐shock family, is positively correlated with skeletal muscle growth in adult mice. However, mechanisms through which RBM3 exerts its effects are largely unknown. The purpose of this study was to perform RNA immunoprecipitation followed by RNA sequencing (RIP‐seq) and apply a network science approach to understand biological processes (BPs) most associated with RBM3‐bound mRNAs. In addition, through nucleotide‐sequence‐scanning of enriched transcripts, we predicted the motif for skeletal muscle RBM3 binding. Gene set enrichment analysis followed by enrichment mapping of RBM3‐bound transcripts  has been recognized as a potential regulator of muscle size, while potential mechanisms are unknown. This study isolates and characterizes the RBM3‐bound transcriptome of resting C2C12 myotubes, and identifies potential transcripts associated with muscle size, which can be leveraged for future and more targeted research. At near confluency, GM was replaced with differentiation media (DM: 2% horse serum (Gibco), 1% P/S (Gibco), DMEM) which was replaced every other day for 5 days until myotubes reached full differentiation. A total of three technical replicates per condition were prepared for RIP‐seq.The purpose of our study was to identify transcripts bound to RBM3 under resting homeostatic conditions using C2.22C12 myotubes were washed with ice‐cold PBS and scraped into microcentrifuge tubes for centrifugation at 1500 rpm for 5 min at 4°C. Lysates were prepared by resuspending cell pellets with RIP lysis buffer (Millipore) and incubated 5 min at 4°C. Two 20 μl aliquots were stored at −80°C for RNA quality assessment and input referencing for sequencing. Magnetic bead‐antibody complexes were prepared by washing beads with RIP wash buffer twice prior to 30 min incubation at room temperature (RT) with 5 μg anti‐RBM3 . C2.3FC >3.0; FDR adjusted p < 0.01) and analyzed using Gene Set Enrichment Analyses  as well as term size (5–500) to reduce term redundancy  < 0.05), an enrichment map representing the biological processes of RBM3‐bound targets was generated for visualization and interpretation.Following QC of raw reads, trimming, and alignment  were imported into the MEME Suite (https://meme‐suite.org/meme/), and an E‐value threshold of 0.05 was used to filter for significantly enriched motifs  was used to predict and characterize the preferential RNA binding motif(s) of skeletal muscle RBM3 . Briefly, MEME analyzes recurring and ungapped sequence patterns within sequencing data and reports the best‐fit width, number of occurrences, and description of the predicted motif  was used to ensure specificity of anti‐RBM3 antibody. Purified RNA with RIN scores of 8 or higher were used for sequencing. A total of three technical replicates were provided for sequencing. Sequencing and library preparation was performed by an unbiased and blinded third party (Novogene). To analyze only the most enriched genes, a FC of greater than 3.0 and a 33.12C12 myotubes as a model system to characterize which transcripts RBM3 binds under normal resting conditions  and enrichment mapping using Cytoscape (https://cytoscape.org/) to reveal mechanistic insights into the role of RBM3 in the skeletal muscle. GSEA revealed the most common molecular functions of RBM3‐bound transcripts to be associated with Translation Factor Activity, Heat Shock Protein Binding, and RNA Cap Binding. The most common cellular compartment of RBM3‐bound transcripts was found to be at Nuclear Envelope, Contractile Fiber, the Extracellular Exosome. Lastly, the most enriched biological processes of the gene set included Muscle Cell Development, Protein Stabilization, and Regulation of Intracellular Transport.We have previously shown the expression levels of RBM3 closely track with changes in muscle size , alpha‐actinin‐2 (Actn2), and integrin subunit beta 1 binding protein 2 (Itgb1bp2)  and 26 S proteasome non‐ATPase regulatory subunit 7 (Psmd7) , served as a bridge between numerous clusters associated with RNA metabolism and transport, including Cajal Body , Spliceosome , and Extracellular Exosome (Anxa1)  Figure ; Table 1) Figure ; Table 1) Figure ; Table 1g Figure ; Table 1Ttn and Myh1, and protein degradation molecules including Fbxl22 and Trim63 (Murf1), were each significantly enriched with RBM3 IP. In addition, evidence of an anabolic influence in protein synthesis was found in the enrichment of numerous translation initiation factors including Eif4b and Eif2b1, among numerous other subunits of eIF2 and eIF3. The enrichment of eIF subunits as well as previous reports implicating compensatory role of RBM3 in muscle mass regulation are altogether suggestive of the integrated stress response (ISR). The ISR is an evolutionarily conserved mechanism that cells use in situations of stress, through post‐transcriptionally and post‐translationally remodeling the cell to return to homeostasis  and the 3′UTR Figure . While 3n Figure . The GA‐n Figure . Target Catalytic Step 2 Spliceosome, and the Precatalytic Spliceosome. These results suggest a role for RBM3 in both the production of mature mRNA and the shuttling of mRNA from the nucleus to the cytoplasm, explaining the presence of RBM3 in both compartments .The authors have no conflicts of interest to declare.Not applicable.