Multi-transcriptome analysis following an acute skeletal muscle growth stimulus yields tools for discerning global and MYC regulatory networks

Myc is a powerful transcription factor implicated in epigenetic reprogramming, cellular plasticity, and rapid growth as well as tumorigenesis. Cancer in skeletal muscle is extremely rare despite marked and sustained Myc induction during loading-induced hypertrophy. Here, we investigated global, actively transcribed, stable, and myonucleus-specific transcriptomes following an acute hypertrophic stimulus in mouse plantaris. With these datasets, we define global and Myc-specific dynamics at the onset of mechanical overload-induced muscle fiber growth. Data collation across analyses reveals an under-appreciated role for the muscle fiber in extracellular matrix remodeling during adaptation, along with the contribution of mRNA stability to epigenetic-related transcript levels in muscle. We also identify Runx1 and Ankrd1 (Marp1) as abundant myonucleus-enriched loading-induced genes. We observed that a strong induction of cell cycle regulators including Myc occurs with mechanical overload in myonuclei. Additionally, in vivo Myc-controlled gene expression in the plantaris was defined using a genetic muscle fiber-specific doxycycline-inducible Myc-overexpression model. We determined Myc is implicated in numerous aspects of gene expression during early-phase muscle fiber growth. Specifically, brief induction of Myc protein in muscle represses Reverbα, Reverbβ, and Myh2 while increasing Rpl3, recapitulating gene expression in myonuclei during acute overload. Experimental, comparative, and in silico analyses place Myc at the center of a stable and actively transcribed, loading-responsive, muscle fiber–localized regulatory hub. Collectively, our experiments are a roadmap for understanding global and Myc-mediated transcriptional networks that regulate rapid remodeling in postmitotic cells. We provide open webtools for exploring the five RNA-seq datasets as a resource to the field.

Myc is a powerful transcription factor implicated in epigenetic reprogramming, cellular plasticity, and rapid growth as well as tumorigenesis. Cancer in skeletal muscle is extremely rare despite marked and sustained Myc induction during loading-induced hypertrophy. Here, we investigated global, actively transcribed, stable, and myonucleus-specific transcriptomes following an acute hypertrophic stimulus in mouse plantaris. With these datasets, we define global and Myc-specific dynamics at the onset of mechanical overload-induced muscle fiber growth. Data collation across analyses reveals an under-appreciated role for the muscle fiber in extracellular matrix remodeling during adaptation, along with the contribution of mRNA stability to epigenetic-related transcript levels in muscle. We also identify Runx1 and Ankrd1 (Marp1) as abundant myonucleus-enriched loading-induced genes. We observed that a strong induction of cell cycle regulators including Myc occurs with mechanical overload in myonuclei. Additionally, in vivo Myc-controlled gene expression in the plantaris was defined using a genetic muscle fiber-specific doxycycline-inducible Myc-overexpression model. We determined Myc is implicated in numerous aspects of gene expression during early-phase muscle fiber growth. Specifically, brief induction of Myc protein in muscle represses Reverbα, Reverbβ, and Myh2 while increasing Rpl3, recapitulating gene expression in myonuclei during acute overload. Experimental, comparative, and in silico analyses place Myc at the center of a stable and actively transcribed, loading-responsive, muscle fiber-localized regulatory hub. Collectively, our experiments are a roadmap for understanding global and Myc-mediated transcriptional networks that regulate rapid remodeling in postmitotic cells. We provide open webtools for exploring the five RNA-seq datasets as a resource to the field.
Myc is a transcription factor known to drive cellular plasticity (1,2), epigenetic reprogramming toward stemness as a Yamanaka factor (3)(4)(5)(6), and rapid growth via proliferative and nonproliferative mechanisms (7)(8)(9). A proto-oncogene that dimerizes with MAX and interacts and/or complexes with numerous other proteins, (10,11) MYC protein is a "universal amplifier" and "supermanager" of transcription with intricate and multifaceted functionality (12)(13)(14). Evidence for a role of MYC in syncytial muscle fiber growth emerged 20 to 30 years ago (15)(16)(17). A decade ago, MYC was hypothesized to be a key aspect of skeletal muscle adaptation to exercise (18). We recently found that the promoter region of Myc was hypomethylated in myonuclei following short-term mechanical overload of the mouse plantaris muscle (19). MYC protein binds the ribosomal DNA promoter during muscle overload (20), consistent with its influence on ribosome biogenesis and protein synthesis in striated muscle (21,22). We also report that MYC-associated areas of ribosomal DNA are differentially methylated in murine myonuclei and human muscle tissue after acute loading (23). Dysregulation of Myc results in tumor development and maintenance in mononuclear cells (8,9). Even transiently elevated MYC can cause tumors in some cells (24); however, Myc transcript and protein (MYC) may be elevated for up to 2 weeks during continuous mechanical overload in mouse muscle without an overt deleterious effect (23,(25)(26)(27)(28). MYC protein abundance in human muscle after resistance training also associates with the magnitude of hypertrophic adaptation (29). The unique multinuclear, terminally differentiated postmitotic nature of muscle fibers likely explains how muscle is resistant to developing cancer (30)(31)(32)(33) and why sustained MYC is tolerated in this tissue. Although the role of MYC in proliferative cells is well studied, its ‡ Co-Senior. * For correspondence: Kevin A. Murach function as a transcription factor in postmitotic myonuclei during muscle growth is incompletely defined.
In the current investigation, we generated four interrelated murine muscle RNA-sequencing (RNA-seq) datasets using a proven hypertrophic loading stimulus (34,35) to understand muscle tissue and myonucleus-specific transcriptional dynamics at the onset of rapid muscle growth. We focused on the effects of Myc given its (1) established role in driving nonproliferative tissue growth and overall cellular plasticity in nonmuscle cell types (1,7) and (2) proposed role in human (23,29,36) and rodent (17,20,22,(25)(26)(27)(28) loading-induced skeletal muscle hypertrophy. We then performed a genetically driven muscle fiber-specific in vivo Myc overexpression experiment along with in silico chromatin immunoprecipitation sequencing (ChIP-seq) analysis to provide focused insight on how Myc may contribute to global gene expression during rapid muscle remodeling.
Results Figure 1A is a study design schematic. Experiment 1 defines the global transcriptome using RNA-seq after 72 h of synergist ablation mechanical overload of the mouse plantaris muscle. Experiments 2 and 3 used the tissue from Experiment 1 to provide information on the contribution of active transcription versus mRNA stability to global gene expression with overload. Experiment 4 details the myonucleus-specific transcriptome during overload to identify muscle fiber-enriched genes, which is further informed by Experiments 2 and 3. Myonuclei only comprise 30% of all nuclei after short-term mechanical overload (19), so defining the transcriptome specifically in myonuclei is critical for understanding muscle fiber adaptation. The transcriptional regulation and localization of Myc and its impact on muscle gene expression were explored using data from Experiments 1 to 4. in the plantaris to understand what genes Myc controls within muscle fibers and how this relates to myonuclear gene expression during overload (Experiment 4); we corroborated the results from these analyses using computational ChIP-seq (37 (Table S1 and S2). A large proportion of downregulated genes were related to oxidative metabolism ( Fig. 1C) (Table S3). This repressed gene signature could contribute to a "Warburg effect" that occurs during rapid overload-induced muscle hypertrophy, marking a shift toward "aerobic glycolysis" for rapid biomass accumulation (38)(39)(40)(41). Consistent with our prior murine studies (19,25,26) and human resistance exercise time course data (23), Myc was higher after overload (Log2FC = 2.0, adj. p = 0.006) (Fig. 1D).
To provide insight on what cell types contribute to global gene expression profiles in sham and overload, we conducted digital deconvolution analysis with CIBERSORTx using Experiment 1 transcriptome data (42). The analysis algorithm was trained using single cell RNA-seq data from a 10 days muscle regeneration dataset (43) (Fig. 1E). The interstitial cell proportion in muscle increases at the onset of overload, outnumbering myonuclei (19). Despite this shift, the largest contribution to gene expression in muscle was predicted to be from muscle fibers (i.e., myonuclei) after 72 h of overload. The second largest contributions were from muscle stem cells (satellite cells) and fibro-adipogenic progenitors (Fig. 1E). We recently reported that successful ECM remodeling during the first 96 h of overload determines the long-term hypertrophic response (44). Early stage ECM remodeling is strongly influenced by satellite cells and fibro-adipogenic progenitors (44,45); it follows that these cell types are major contributors to early-phase gene expression during growth.

Experiments 2 and 3: Nascent and stable mRNA transcriptomes after 72 h of mechanical overload
Most transcription in skeletal muscle is rRNA (46), and rRNA levels are further augmented specifically in myonuclei during overload (25). We conducted mRNA profiling to understand global transcriptional dynamics in the non-rRNA pool during growth using 5-Ethenyl uridine (EU) metabolic labeling (25) (Tables S4-S9). In the EU (nascent, Experiment 2) and non-EU (not actively transcribed and presumably stable, Experiment 3) mRNA fractions, ECM remodeling was among the most upregulated processes during the last 5 h of overload versus sham (Fig. 2, A and B) (Tables S4-S6, and S8). Myc was also significantly higher in nascent and stable fractions relative to sham (Fig. 2C). Active Myc transcription during overload could be facilitated by hypomethylation of its promoter in myonuclei (19). Thus, ECM and Myc gene expression are highly regulated during the acute phase of muscle loading. In addition, RHO-GTPase genes were higher in both fractions with overload relative to sham (Fig. 2, A and  B). RHO-GTPases are implicated in muscle mass regulation, but their role in load-induced hypertrophy is still being defined (47). Genes related to epigenetic control of gene expression (specifically histones and Dnmt1) were enriched in the non-EU fraction with overload ( Fig. 2, D and E). Myonuclear histone turnover (48) and dynamic regulation of DNA methylation in myonuclei (19,23,49) likely facilitates hypertrophic gene expression and adaptation in muscle fibers.
In the EU and non-EU fractions, oxidative metabolismrelated gene expression was lower during overload ( Fig. 2, F and G) (Tables S7 and S9); these data inform the findings from Experiment 1. Pgc1α (Ppargc1a), a core regulator of mitochondrial biogenesis (50,51), was among genes that were lower in both fractions during overload (Tables S4 and S5). Apart from epigenetic-related genes, most mRNA differences between sham and overload at the pathway level were attributable to differences in both nascent transcription and, presumably, enhanced mRNA stability.

Experiment 4: The myonuclear transcriptome after 72 h of mechanical overload
To understand what genes were specifically regulated in muscle fibers during rapid muscle growth, we conducted RNA-seq on fluorescent activated nuclear-sorted (FANS)purified myonuclei using the HSA-GFP mouse (Fig. 3A) (19,52). Relative to sham, genes related to ECM remodeling (primarily collagens, matrix metalloproteinases, and secreted factors) and immune signaling (namely chemokines) were most highly enriched in myonuclei from overloaded muscle (Fig. 3B) (Tables S10 and S11). We and others have reported that matrix metalloproteinase 9 (Mmp9) is responsive to loading in skeletal muscle and is a key component of muscle growth (53)(54)(55). We confirm here that Mmp9 is enriched in myonuclei during overload in vivo (Fig. 3C). Interstitial cells of the muscle microenvironment are generally viewed as the primary contributors to ECM deposition and turnover. Emerging evidence suggests that muscle fibers also play a major role in ECM remodeling (53,(56)(57)(58), which the current data reinforces.
Numerous cell cycle regulators were enriched in myonuclei after overload (Fig. 3B) which included Myc (top 30 upregulated gene, adj. p = 0.0136 × 10 −7 ) (Fig. 3C) (Table S11). Runx1, another transcription factor, was highly abundant and enriched in myonuclei by overload (Log2FC = 4.1, adj. p = 0.05 × 10 −14 ) (Fig. 3E); it was also markedly higher in total RNA, EU, and non-EU fractions (Tables S1, S4 and S5). Runx1 induction during overload is intuitive since it regulates muscle ACCELERATED COMMUNICATION: Myc in muscle during hypertrophy mass, myofibrillar organization, and autophagy in myofibers (59). RUNX1 is also known to interact and complex with MYC (60,61). Ankrd1 (Marp1, also Carp1) was the most upregulated gene with overload in myonuclei (Log2FC = 6.0, adj. p = 0.0127 × 10 −62 ) as well as the EU fraction (Log2FC = 5.1, adj. p = 0.038 × 10 −60 ) (Fig. 3F) (Tables S4, S5, and S10); however, it was only the 1495th most differentially regulated gene in the total RNA dataset (Table S1). Ankrd10 was also among the most upregulated genes in myonuclei and the EU fraction. Ankrd1 localizes in myotendinous junction myonuclei (62) and is induced by eccentric exercise in rodent and human muscle (63,64). Perhaps Ankrd1 upregulation during mechanical overload is partially explained by muscle lengthening and/or myotendinous junction remodeling (35,65). These results highlight the power of EU-labeling and myonucleus-specific transcriptomics for identifying potentially important genes for muscle growth. Our findings also provide impetus for further investigation of Ankrd1 during muscle hypertrophy. The category of genes most downregulated during overload in myonuclei was oxidative metabolism (Fig. 3G) (Table S12). Thus, the total mRNA and EU results are likely driven by changes within the myofiber.
To corroborate MYC regulation of target genes in muscle, we compared our overload myonuclear and Myc overexpression RNA-seq data to published MYC ChIP-seq data from myogenic cells (77). Of genes regulated by both overload and MYC (Fig. 4, D and G), Anp32b, Aqp4, Atp1a1, Cdkn1b, Cntfr, Epas1, Ftsj3, Jak2, Ncl, Nr1d2/Reverbβ, P2ry1, Pcmtd1, Rpl3, and Slc7a5 featured MYC occupancy in myogenic cells. After differentiation, MYC-binding peaks on all these genes except Aqp4, Atp1a1, and P2ry1 were altered, indicating regulation by MYC in dynamic conditions. Of note, MYC binding to Ankrd1 increased during myotube formation (77). Brief Myc overexpression in the soleus did not induce Ankrd1, so MYC may function cooperatively with another factor that is induced during overload to regulate this gene (14,82). To confirm MYC transcription factor binding of myonuclear DNA during mechanical overload, we utilized our RNA-seq data to perform epigenetic Landscape In Silico deletion Analysis (Lisa) (37). Lisa incorporates transcriptome input data with an extensive library of publicly available transcription factor ChIP-seq and global chromatin accessibility profiles to infer transcriptional regulators. Leveraging our MYC overexpression RNA-seq data as a control (Experiment 5), MYC/ MYCN was the highest ranked transcription factor driving upregulated genes (Table S14), confirming the accuracy of Lisa. Using the first 500 differentially regulated genes in each direction from Experiment 4, MYC was in the top 5% of transcription factors controlling upregulated genes (p = 1.7 × 10 −24 ) in myonuclei during overload and the top 10% for controlling downregulated genes (p = 2.4 × 10 −11 ) (Table S15).

Discussion
Interrelated RNA-seq datasets define the early phase of growth processes in differentiated muscle fibers (Fig. 5). Lower oxidative metabolism-related gene expression during the onset of rapid muscle growth is due to changes in mRNA transcription and stability and occurs specifically in myonuclei. The overload datasets also revealed an under-appreciated role for muscle fibers in ECM remodeling during adaptation. Regulation of several collagens and remodeling enzymes such as Mmps by active transcription, transcript stability, and in myonuclei emphasizes the importance of ECM dynamics for muscle hypertrophy (56). The non-EU RNA-seq data suggests elevated epigenetic-related gene expression during overload reflects greater mRNA stability; the mechanism underlying the change in mRNA stability in muscle during hypertrophy deserves further investigation. We identified key genes that are actively transcribed in muscle and enriched in myonuclei during in vivo muscle growth, such as Runx1 and Ankrd1. RUNX1 regulates muscle mass (59) and participates in ribosome biogenesis (83), as well as interacts with MYC (60, 61). ANKRD1 associates with titin's N2A element, a major mechanosensory and signaling hub in skeletal muscle (84,85). It also locks titin to the thin filament, regulates passive force, and protects the sarcomere from mechanical damage (86). Some evidence suggests ANKRD1 inhibits TNFα-induced NFκB signaling (87) and affects androgen receptor signaling (88) in myogenic cell culture. Given these functions, ANKRD1 may be critical for successful muscle hypertrophy. Ankrd1 was not among the most differentially expressed genes in the total RNA dataset but emerged in the nascent and myonuclear RNA-seq as the most highly responsive gene to mechanical overload. This mismatch highlights the utility of evaluating transcriptional dynamics and myonuclear-specific gene expression for understanding muscle adaptation. Furthermore, Runx1 and Ankrd1 are typically upregulated after muscle denervation (59,87), suggesting a compensatory response to counteract atrophy in that condition.
MYC protein localizes in myonuclei during loading-induced skeletal muscle growth (15,27). Genetic Myc induction recapitulates diverse aspects of the loading response in muscle fibers. These changes include downregulation of Reverbα, Reverbβ, and Myh2, along with increased Rpl3. Published ChIP-seq data in myogenic cells (77) as well as in silico transcriptional regulator analysis (37) using our myonuclear RNAsequencing data corroborates the association of MYC with Reverbβ and Rpl3, along with numerous other genes. The ChIP-seq also revealed a potential interaction with Ankrd1 (77) that did not emerge in our muscle-specific MYC overexpression experiment. Lower levels of Reverbs during overload could have implications for circadian regulation and metabolism in muscle fibers (80); this is salient since MYC is exercise-responsive and exercise shifts the circadian rhythm in skeletal muscle (89)(90)(91)(92). Perhaps an exercise-mediated shift in muscle circadian rhythm is controlled by MYC, but more work is needed in this area, especially with respect to resistance exercise. Altered Rpl3 by mechanical overload and MYC expression could facilitate muscle-specific growth via ribosome specialization (66,93). MYC is also known to be a potent driver of ribosome biogenesis in muscle (21,22). We previously reported that ribosome biogenesis increases in total and nascent RNA pools following 72 h of synergist ablation in the mouse (20,23,25,94). Thus, MYC appears central to the regulation of rRNA synthesis and ribosome assembly, processes hypothesized to be necessary for sustained hypertrophy in response to loading (95)(96)(97). MYH2 protein and oxidative fiber proportion increases after prolonged muscle overload (98). MYC-mediated and early Myh2 downregulation during overload may relate to an acute glycolytic preference during rapid hypertrophy (38).
The induction of MYC in instances of attenuated muscle plasticity such as aging, where MYC activity may be blunted during hypertrophy (15), could restore adaptive potential and increase muscle mass. Future experiments involving simultaneous muscle-specific inducible knockout of c-Myc and its several analogous family members (99), myonuclear MYC ChIP-seq, and MYC protein-protein interactome analysis during overload will provide more granular insight on the MYC regulatory network during muscle hypertrophy. Since differentiated myofibers can sustain high levels of oncogene expression without tumor formation, we suggest that MYC in muscle fibers induced by loading is a core component of rapid yet functional adaptive remodeling. Collectively, our data are a rich resource for understanding transcriptional dynamics and MYC regulation during the onset of loading-induced muscle fiber growth.

Animals and animal procedures
All animal procedures were approved by the University of Kentucky and the University of Arkansas IACUC. Mice were housed in a temperature and humidity-controlled room, maintained on a 14:10-h light-dark cycle, and food and water were provided ad libitum throughout experimentation. Animals were sacrificed via a lethal dosage of sodium pentobarbital injected intraperitoneally or CO 2 asphyxiation followed by cervical dislocation.
Female C57BL6/J mice were obtained from the Jackson Laboratory for Experiments 1 to 3. Female HSA-rtTA +/− -;TRE-H2B-GFP +/− (HSA-GFP) mice were generated as previously described by us (19,52) for Experiment 4. The TRE-H2B-GFP mouse was originally obtained from the Jackson Laboratory (005104, bred to homozygosity by our laboratory) (100). For Experiment 5, male HSA-rtTA +/− -;TRE-Myc +/− (HSA-Myc) were generated by crossing homozygous HSA-rtTA mice (52) with heterozygous TRE-Myc mice (101); HSA-rtTA littermate mice were used as controls. All mice were genotyped as described (52,102). HSA-GFP mice were treated with lowdose doxycycline (0.5 mg/ml doxycycline in drinking water with 2% sucrose) for 5 days to label myonuclei with GFP. HSA-Myc and littermate control mice were treated with doxycycline water overnight, and this water was replaced with unsupplemented water for 12 h prior to being euthanized. All mice were at least 2 months of age at the time of experimentation.
For Experiments 1 to 4, synergist ablation overload of the plantaris was performed as described (19,34). Synergist ablation involves removal of 50% of the gastrocnemius-soleus complex while under anesthesia, followed by ambulatory cage behavior for 72 h. Sham surgery (control condition) ACCELERATED COMMUNICATION: Myc in muscle during hypertrophy involved all the steps of synergist ablation but without removal of muscle. For Experiments 1 to 3, mice were injected with EU 5 h prior to being euthanized (25). Briefly, mice were given an intraperitoneal injection of 2 mg of EU (Jena Biosciences) suspended in sterile PBS. For Experiment 4, myonuclei were isolated via FANS (19). Plantaris muscles were harvested immediately after being euthanized. Muscle was minced and homogenized via Dounce in a sucrose buffer with RNAse inhibitors. After straining through 40 μm filters, the nuclear suspension was pulsed with propidium iodide to label DNA. GFP+/PI+ myonuclei were sorted using FANS into TRIzol LS for RNA isolation.

RNA isolation and sequencing
For Experiments 1, 2, 3, and 5, plantaris RNA was isolated using TRI Reagent (Sigma-Aldrich). Tissue was homogenized using beads and the Bullet Blender Tissue Homogenizer (Next Advance) or the Fisher Bead Mill (Fisher). Following homogenization, RNA was isolated via phase separation by addition of bromochloropropane or chloroform and then by centrifugation. The aqueous phase was transferred to a new tube and further processed on columns using the Direct-zol Kit (Zymo Research) (23). For Experiment 4, RNA was isolated by Norgen. Experiments 1, 4, and 5 utilized Poly-A enrichment. For Experiments 2 and 3, RNA was depleted of rRNA using the NEBNext rRNA Depletion Kit (New England Biolabs) prior to isolation of EU-and non-EU-labeled RNA. A total of 4 × 1 μg RNA reactions were used per sample for rRNA depletion and pooled for the EU pulldown. The EU-and non-EU-labeled RNA fractions were isolated using the Click-iT Nascent RNA Capture kit (ThermoFisher) per the manufacturers protocol. cDNA libraries were constructed using NEBNext Ultra# II RNA Library Prep kit with NEBNext Multiplex Oligos for Illumina (New England Biolabs). EU pulldowns were unsuccessful for one sham experiment, so sequencing for that group was n = 2. Library preparation for Experiment 4 was low input and utilized the SMARTer pico kit (TaKaRa), as described by the National Genomics Infrastructure at SciLifeLab. RNA for Experiments 1, 2, 3, and 5 were sequenced by Novogene on an Illumina HiSeq using 150 bp paired-end sequencing, as we have done previously (79,103). Experiment 4 was sequenced by the SciLifeLab on a NovaSeq 6000 (150 bp paired-end).

Transcriptomic analyses
Raw counts from RNA-seq were used as inputs into R (Version 4.1.0) or Partek Flow. Alignment was performed using STAR with mmu39. After filtering low-expressed genes, DESeq2 (Version 1.34.0) was used for normalization and differential analyses of RNA-seq data to identify differentially expressed genes (DEGs) with pairwise comparisons (104). DEGs were identified with a false discovery rate (Benjamini-Hochberg method) adjusted p-value <0.05. DEGs with a log2 fold change (Log2FC) over 1 and adj. p < 0.05 were used for downstream functional analysis in Experiments 1 to 4. A foldchange cut-off was not used for Experiment 5. Kyoto Encyclopedia of Genes and Genomes and Reactome (https:// reactome.org/) were utilized for pathway analysis. We utilized clusterProfiler (Version 4.4.1), ReactomePA, ggplot2 (3.15), and ConcensusPathDB (105) with mouse as the reference organism.

Digital cell sorting with CIBERSORTx
CIBERSORTx (https://cibersortx.stanford.edu/) is a machine learning method that enables prediction of cell type proportions from bulk tissue analysis using single cell RNAseq data (42). We used skeletal muscle single-cell data from 10 days muscle regeneration data from Oprescu et al. (43). The datasets (10X Genomics) were reanalyzed with Seurat, and cell clusters were identified with a resolution of 0.8 (106). Normalized gene expression matrices of individual cells were used to create a signature matrix of all cell types using default settings, and cell proportions were predicted by CIBERSORTx with 1000 permutations.

Transcriptional regulator analysis using Lisa
Lisa was run according to recommended procedures (37). In brief, DEG lists (adj. p < 0.05) from Experiments 4 and 5 were input into the online graphical user interface. Output files were downloaded and the strength of MYC regulation was determined by ranking of regulatory potential in H3k27ac ChIP-seq files. The Cauchy combination p-value test was used to determine overall influence of MYC.

Data availability
Raw data are available in Gene Expression Omnibus GEO213406, and all processed data are provided in Supporting information and online webtools.
Supporting information-This article contains supporting information.