C), located upstream of an array of GCs. Some integron GCs, called ORF-less GCs, contain no identifiable ORF with a small number shown to be involved in antisense mRNA mediated gene regulation. In this study, the promoter activity of ORF-less GCs, previously recovered from the oral metagenome, was verified by cloning them upstream of a gusA reporter, proving they can function as a promoter, presumably allowing bacteria to adapt to multiple stresses within the complex physico-chemical environment of the human oral cavity. A bi-directional promoter detection system was also developed allowing direct identification of clones with promoter-containing GCs on agar plates. Novel promoter-containing GCs were identified from the human oral metagenomic DNA using this construct, called pBiDiPD. This is the first demonstration and detection of promoter activity of ORF-less GCs from Treponema bacteria and the development of an agar plate-based detection system will enable similar studies in other environments.Integrons are genetic elements consisting of a functional platform for recombination and expression of gene cassettes (GCs). GCs usually carry promoter-less open reading frames (ORFs), encoding proteins with various functions including antibiotic resistance. The transcription of GCs relies mainly on a cassette promoter (P They consist of two main components; a functional platform and a variable array of GCs. The functional platform, located on the 5′ end of an integron, consists of an integrase gene (intI), and its promoter (PintI), an attI recombination site and a constitutive cassette promoter (PC) for the expression of GCs4. IntI is a site-specific tyrosine integrase that catalyses the insertion and excision of GCs via recombination mainly at attI and the attC, the latter located on circularised GCs. The integrase gene; intI, is normally transcribed in the opposite direction to GCs within an integron ron Fig. . Howeverron Fig. , and havattC recombination site7. The proteins encoded by GCs are diverse in which most GCs are either have no known homologues in the database or predicted to encode hypothetical proteins, while the remaining showed homology to proteins associated with antibiotic resistance, virulence, metabolism and etc.8. When a GC is excised from integron, it forms a non-replicative mobile genetic element, which can be a substrate for integrase mediated recombination between attI (on the integrons) and attC (on the circular GC). This directionality of recombination is favoured over attC:attC recombination, resulting in the usual insertion of a newly integrated GC immediately next to the PC promoter in the first position of the GC array, ensuring maximal expression11.The second part of an integron is an array of GCs. Each usually contains a single promoterless open reading frame (ORF) and an intI12. In the absence of stress, the transcriptional repressor LexA binds to PintL and prevents the transcription of intI. The SOS response is activated upon the accumulation of single-strand DNA (ssDNA), generated during DNA damage, DNA repair, transformation, conjugation and certain antibiotic exposure e.g. trimethoprim and fluoroquinolones15. RecA recognises ssDNA and polymerises into RecA nucleofilaments, which induce autocleavage of LexA, releasing PintI from repression and allowing intI transcription16. By controlling the expression of IntI, bacteria can reshuffle their GCs at the precise moments of need (stress), generating genetic diversity and rapid adaptation to selective pressures, thereby avoiding accumulation of random recombination events that could be deleterious to the host cell19.The expression of integron integrases is controlled via the SOS response; there is a LexA-binding site located in the PC promoter. The level of expression of GCs varies depending on the distance from PC, as the strength of expression decreases when GCs are located further from PC20. This ensures that a recently acquired GC will be immediately expressed. There are also some GCs that contain their own promoters, ensuring constitutive expression of their genes regardless of the PC promoter and their position within the integron array; examples include cmlA1 (chloramphenicol resistance), qnrVC1 (quinolone resistance), ere(A) (erythromycin resistance) and many of the GCs encoding toxin-antitoxin (TA) systems24.As most of the GCs do not contain a promoter, their expression usually relies on the P29. In our previous study on the detection of integron GCs in the human oral metagenome, we found 13 ORF-less GCs out of 63 identified GCs (20%)29. ORF-less GCs have been shown to encode regulatory RNAs, for example the trans-acting small RNA (sRNA)-Xcc1, encoded by the ORF-less GC of a Xanthomonas campestris pv. campestris integron, which is involved in regulation of virulence30. Whilst promoter activity of ORF-less GCs has been discussed, this has not been experimentally demonstrated8.Integron GCs have been identified from environments such as soils, marine sediments, seawater and more recently from human oral metagenomesin silico analysis to identify promoter sequences in the GCs identified in our previous study on the oral metagenome. Promoter activity was experimentally determined by cloning the selected GCs upstream of the gusA reporter gene and measuring β-glucuronidase enzyme activity. Furthermore, we devised a GC-based promoter detection strategy utilising PCR and subsequent cloning between divergently orientated reporter genes. With this system, the successful cloning of amplicons from promoter-containing GCs can be visualised directly on agar plates, allowing the direct isolation of GC PCR amplicons with promoter activity from metagenomic DNA.In this study, we performed 29. Five GCs were chosen for experimental expression analysis. GC TMB4 (amplified with primers targeting intI and attC) was selected as it is ORF-less and located in the first position of the integron array29. ORF-less GCs MMU23 and MMB37 were selected as they had the highest overall score predicted by BPROM promoter prediction software  gene on pCC1BAC-lacZα-gusA  that is responsible for the transcription of most genes during growth in both E. coli and Treponema spp.33, while PFla is recognised by sigma factor 28 (σ28), involved in the expression of flagella-related genes in motile bacteria34. This will determine the limitations of our assay in recognising promoters associated with different types of sigma factors.As BPROM predicted putative promoter sequences on both strands, promoter activity of the selected GCs was determined by directionally cloning upstream of a promoterless β-glucuronidase  downstreaming from PC and the presence of two PC in class 2 integrons (PC2A and PC2B), could result in a significantly higher expression of GCs37. As the promoter activities of the TMB4 GC, located in the first position of GC array, were confirmed in the previous section, another two constructs (TMB4 PC promoter and TMB4 PC-GC constructs) were therefore constructed to determine the synergy effect between PC and the ORF-less promoter GC. As the TMB4 PC promoter was not identical to the PC of T. denticola integron38, the PC of another integron GC; TMB129, which was identical to it39, was also included.Previously, coupling PC and TMB4 PC-GC showed promoter activities on both directions. However, coupling promoter GC in the first position (TMB4 PC-GC) did not significantly increase the expression of reporter genes, compared to the presence of only TMB4 PC  . To verify the utility of pBiDiPD, and also to detect novel GCs containing promoter sequences in the human oral metagenome, integron GCs were amplified with SUPA4-NsiI/SUPA3-NheI and MARS5-NsiI/MARS2-NheI primers29, and cloned into pBiDiPD. The clones with GCs containing a promoter on the sense strand showed blue fluorescence when visualised under UV light, reflecting the activity of β-glucuronidase enzymes catalysing MUG to yield the blue-fluorescent 4-methylumbelliferyl. Clones with promoter activity on the antisense strand resulted in blue colonies as a result of β-galactosidase enzymes catalysing X-Gal into a blue insoluble pigment 5,5′-dibromo-4,4′-dichloro-indigo , described in materials and methods, 23 different GCs with promoter activities were identified -prevent-host-death protein (antitoxin), BrnT (toxin)-BrnA (antitoxin), VapC (toxin)-AbrB/MazE/SpoVT family protein (antitoxin), RelE/ParE family (toxin)-XRE transcriptional regulator (antitoxin). The second group contained ORF-less GCs, which could be found in 7 samples, all reported in the previous study, except sample MMU-Pro-53. Most of the samples (14 out of 23 GCs) showed the promoter activity only on the sense strand. Samples with promoter activity only on the antisense strand were MMU-Pro-6, MMU-Pro-63, and MMU-Pro-65, while 6 out of 23 GCs exhibited promoter activity on both strands.41. Therefore, it is important to understand the diversity of GCs and how their expression is controlled. Even though ORF-less GCs have been found in the previous studies43, their functions have not been fully understood. Our findings here confirmed the promoter activities of ORF-less GCs from Treponema spp., which could be important for the expression of other GCs in integrons.Integrons are important disseminators of antimicrobial resistance genes in which more than 130 distinct GCs carrying antimicrobial resistance genes, covering most classes of antibiotics, have been identifiedE. coli surrogate. Nucleotide sequence analysis suggested that these GCs were likely to be derived from Treponema spp., therefore, the ability of E. coli to utilise T. denticola promoter sequences was determined by including the experimentally verified T. denticola promoter, PTdTro31 which showed high activity on both sense and antisense strands, providing confidence that E. coli could be used. However, as no promoter activity was detected from PFla, it suggested that our enzymatic assay cannot detect promoters associated with σ28 from Treponema spp., which could be due to an inability for the E. coli host to recognise the Treponema σ28 promoter sequence. Therefore, constructs with no promoter activity in our enzymatic assay could also carry Treponema promoters associated with other sigma factors that cannot be recognised by the E. coli host like the σ28 PFla promoter.In this study, we determined promoter activity from GCs isolated by PCR from metagenomic DNA by measuring promoter activity from multiple GC containing constructs. As the ORF-less GCs were recovered from the oral metagenome, there is little information regarding the original host. Therefore, we chose to test the promoter activities by using an in vitro, as shown by the activity on the sense strand of the MMB37 and both strands of the TMB4. A study on the Vibrio integron, containing a 116-cassette array, showed that most of the GCs are transcribed44. Therefore, ORF-less GCs could be responsible for transcription of the other GCs not transcribed by PC.Promoter activities of the ORF-less GCs were confirmed and quantified by using a β-glucuronidase enzymatic assay. This is the first time that the promoter activity of ORF-less GCs has been demonstrated C and TMB4 PC + GC were therefore included in the assay to determine whether having a promoter GC at the first position could help promote the expression of downstream GCs. However, the presence of promoter sequences in TMB4 GC did not significantly increase the expression of gusA. The lack of additive promoter activity can be explained by more competition for enzymes involved in transcription such as RNA polymerases (RNAP) or sigma factors to be available for transcription from each promoter, resulting in a lower transcriptional level45. Another, not mutually exclusive possibility is transcriptional interference (TI) between the four promoters on the TMB4 Pc + GC construct. We have experimentally shown promoter activity of TMB4 PC and TMB4 GC constructs on both strands, indicating convergent TI is a possibility.For the TMB4 GC (ORF-less GC in the first position), it was initially hypothesised that the promoter could help increase the expression of the downstream GCs. The constructs of TMB4 PC is in intI, which is convergent to the integron integrase promoter PIntI downstream , therefore, it could result in lower TI with PintI, resulting in a higher expression of intI and subsequent catalysis of recruitment of a new GC to the first position  in the second GC), i.e. comparing the data for TMB4 PC and TMB4 PC + GC. Therefore, we hypothesised that promoter-containing GCs could act as a genetic clutch, where the expression of the original first GC is partially disengaged from the PC promoter and replaced by the one on the ORF-less promoter containing GC , located in the third position, is expressed by PC and the promoter GC. Therefore, allowing bacteria to survive in the presence of both drugs.A genetic clutch within an integron can be of benefit to bacteria when they are exposed to multiple environmental stresses such as two different antibiotics simultaneously. The first resistance gene  ORFs, including the PIN toxin and ribbon-helix-helix antitoxin domains, which were shown to contain their own promoter. Sample SSU17 and MMU23, which showed no promoter activity, can be considered as a control; illustrating that not all of GCs amplified from the oral metagenome exhibited promoter activity within our assay.As the other ORF-less GC MMU23 showed no promoter activity it may have other functions or carry a promoter that can be recognised in its native host but not in lacZα-gusA plasmid, developed for the enzymatic assay, also had potential to be used for the detection of promoter activity in either direction from GCs. To verify the application of pCC1BAC-lacZα-gusA plasmids as promoter detection system, integron GCs were amplified from the human saliva metagenome by using SUPA3-SUPA4 and MARS2-MARS5 primers, which were the only two primer pairs that were verified for the amplification of integron GCs from the oral metagenome29. After cloning the amplified GCs between both reporter genes, two groups of GCs were identified with promoter activities: ORF-less GCs and TA-containing GCs. By detecting 7 clones containing ORF-less GCs with promoter activity it further supported that one of the functions of ORF-less GCs in integrons is to provide promoter activities.The pCC1BAC-52. TA systems normally encode a stable toxin and a labile antitoxin53, therefore TA cassettes have to carry their own promoters to ensure their expression. These were found in SCIs of Treponema spp., such as the HicA-HicB TA-containing GC in the fourth position within the GC array (Accession number NC_002967) in the SCI from T. denticola38. As most of the GCs amplified with our primers were homologous with Treponema spp., these TA-containing GCs should be present in our oral metagenome and were detected by our pBiDiPD based on their promoter activities.TA-containing GCs are abundant in sedentary chromosomal integrons (SCIs), which were suggested to have a role in preventing random deletion of GCs and stabilising the large arrays SCIsTwo of the GCs, SSU-Pro-9 and MMU-Pro-18, were similar to the MMB3 and MMB37 GCs, respectively, which were shown by the β-glucuronidase enzyme assay to have promoter activity on the sense strand. The phenotypes of SSU-Pro-9 and MMU-Pro-18 colonies also showed only a blue fluorescence phenotype, reflecting the promoter activity on the sense strand, which corresponded with the enzymatic assay results of MMB3 and MMB37.Treponema ORF-less integron GCs were experimentally demonstrated by using a robust β-glucuronidase enzyme assay, confirming that one of the functions of ORF-less GCs is to provide promoters for the expression of ORF containing GCs, in addition to expression from PC. This could be extended to ORF-less GCs from other bacteria, which should be determined further. The dual reporter plasmid; pBiDiPD, was developed for the direct visualisation of clones containing gene cassettes with promoter activity on agar plates. This can be applied as a detection system for promoter activity for any other DNA fragments.To summarise, the promoter activities of the 29, were predicted for putative promoter sequences by using the web-based software BPROM in the Softberry package39.All of the ORF-less GCs and some of the GCs containing ORFs, identified in the previous studytet(M)-gusA plasmid54. The selected GCs were amplified from the pGEM-T easy vector containing the GC amplicon from a previous study29, as shown in Supplementary Fig. To determine the promoter activity of the selected GCs, the constructs were initially cloned into the EcoRI and KpnI restriction sites on pUC19-PgusA, new constructs were prepared based on a low copy number CopyControl™ pCC1BAC™ vector  as it will be maintained in E. coli cell as one plasmid per cell and enable us to control the plasmid copy number to be similar between each construct. The construct was designed to contain two reporter genes, β-galactosidase lacZα and β-glucuronidase gusA genes . The backbone of pCC1BAC was amplified with pCC1BAC-delLacZ-F1 and pCC1BAC-delLacZ-R1, and the amplified products were treated with a Kinase-Ligase-DpnI (KLD) enzyme mix, following the instructions from the manufacturer. The KLD-treated product was then transformed into E. coli α-Select Silver Efficiency competent cells  following the instructions from the manufacturer. The pCC1BAC-delLacZ plasmid was then extracted from E. coli by using QIAprep Spin Miniprep Kit , following the manufacturer’s instructions.Due to a significant difference in the plasmid copy number in some constructs of the pUC19-GC-nes Fig. . As lacZlacZα reporter gene was amplified from the pUC19 vector  with LacZ-F1 and LacZ-R1 primers. For gusA reporter gene, it was amplified from pUC19-Ptet(M)-gusA with gusA-F1 and gusA-R1 primers. A bidirectional terminator, modified from lux operon, was added to LacZ-F1 and gusA-R1 primers, resulting in two bi-directional terminators flanking the lacZα-gusA reporter genes55. This was done to prevent transcriptional read-through from the promoter in the plasmid backbone and to also prevent promoters from the inserts interfering with the expression of genes on the plasmid backbone. The lacZα and gusA amplicons were digested with NsiI restriction enzymes  and ligated together by using T4 DNA ligase . The lacZα-gusA ligated product was directionally cloned into the pCC1BAC-delLacZ plasmid by digesting them with AatII and AvrII restriction enzymes and ligated together, resulting in pCC1BAC-lacZα-gusA plasmid.The gusA constructs by using primer listed in Supplementary Table NsiI and NheI and directionally cloned into a pre-digested pCC1BAC-lacZα-gusA plasmid, then transformed into E. coli α-Select Silver Efficiency competent cells.The selected GCs were amplified from each pUC19-GC-gusA, following the protocol described previously with some modifications56. The overnight cultures of E. coli containing the reporter constructs were prepared in LB broth supplemented with 12.5 µg/mL chloramphenicol. The OD600 of each overnight culture was measured. An aliquot of 1 mL of the overnight culture was centrifuged at 3000 × g for 10 min and discarded the supernatant. The cell pellets were incubated at −70 °C for 1 hr and resuspended in 800 µl of pH 7 Z buffer  and 8 µl of toluene. The mixture was transferred to a 2 ml cryotube containing glass beads (150–212 μm in diameter)  and vortexed twice for 5 min each with an incubation on ice for 1 min in between. The glass beads were then removed by centrifugation at 3000 × g for 3 min. One-hundred microliters of cell lysate were mixed with 700 µl of Z-buffer, then incubated at 37 °C for 5 min. One-hundred sixty microliters of 6 mM ρ-nitrophenyl-β-D-glucuronide (PNPG) was then added to the reaction and incubated at 37 °C for 5 min. The reactions were stopped by adding 400 µl of 1 M Na2CO3 and centrifuged at 3000 × g for 10 min to remove cell debris and glass beads. The absorbance of the supernatant was measured with a spectrophotometer at the wavelength of 405 nm. Three biological replicates of the β-glucuronidase enzymatic assay were performed. The β-glucuronidase Miller units were calculated from57The β-glucuronidase enzymatic assay was performed to measure the promoter activity based on the expression of lacZ-gusA) to the other constructs were performed by using ordinary one-way ANOVA with either Dunnett’s post-hoc test (to compare each construct with a negative control) or Bonferroni’s post-hoc test (to compare constructs between themselves). The groups with statistically significantly difference from the control had the p-value of less than 0.05.The average and standard deviation of β-glucuronidase concentration were calculated from three biological replicates, which were used for the columns and error bars in Fig. 29. The human oral metagenomic DNA was previously extracted from the saliva samples collected from 11 volunteers in the Department of Microbial Diseases, UCL Eastman Dental Institute29. The ethical approval for the collection and uses of saliva samples was obtained from University College London (UCL) Ethics Committee (Project number 5017/001). The written informed consents were obtained from all volunteers prior to the collection of saliva samples. All procedures performed in this study were in accordance with the ethical guidelines and regulations from the UCL Ethics Committee.The integron GCs were amplified from the human oral metagenome by using as described previously with SUPA4-NsiI-SUPA4-NheI and MARS5-NsiI-MARS2-NheI primerslacZα-gusA plasmid. The ligated products were transformed into E. coli α-Select Silver Efficiency competent cells by heat shock. Cells were spread on LB agar supplemented with 12.5 µg/mL chloramphenicol, 80 µg/mL X-gal, 50 µM IPTG, and 70 µg/mL 4-methylumbelliferyl-β-D-glucuronide (MUG). After incubation at 37 °C for 18 hr, the colonies with β-galactosidase activity from lacZ was detected by blue-white screening on the agar plate, and the β-glucuronidase activity from gusA was visualisation under UV light. Colonies exhibiting either activity were selected and subcultured on fresh agar plates. The inserts were amplified by colony PCR using lacZ-F2 and gusA-F2 primers and sequenced by sequencing service from Genewiz .The amplified products were purified and digested with NsiI and NheI and ligated with the pre-digested pCC1BAC-http://www.mbio.ncsu.edu/bioedit/bioedit.html). The contigs from sequencing reactions were combined by using CAP contig function in the software58. The sequences were then matched to the nucleotide and protein database by using BlastN and BlastX (version 2.8.0) from the National Centre for Biotechnology Information (NCBI), respectively59. ORF finder (https://www.ncbi.nlm.nih.gov/orffinder) and BlastP (version 2.8.0) were used for the identification of ORFs in each GC60. The criteria for the sequence analysis of integron GC were the same as described in the previous study29. Artefactual PCRs were discounted by detecting the consensus R′ (1 R) core sites [GTTN1N2N3N4] and the complementary R″ (1 L) core sites [N′4N′3N′2N′1AAC] of attC located downstream from the attC forward primers and upstream from the attC reverse primers, respectively, where N and N′ are complmentary nucleotides and the number indicated their positions (Supplementary Table 62. While the conserved complimentary GTT/AAC triplet in each core site was essential, we accepted 50% non-complementarity within N1–N4. Two additional criteria for the verification of GCs detected with pCC1BAC-lacZα-gusA were included. Any clones containing incomplete GCs, caused by digestion at internal NsiI and NheI restriction sites on the GCs, were excluded from the dataset. Also chimeric inserts, which were the ligation products between digested amplicons, were also excluded.DNA sequences were visualised and analysed by using BioEdit version 7.2.0 (29. The first and second letters represented the forward primer and reverse primer used in the amplification. The third letter represents the source of the human oral metagenomic DNA which is U for the United Kingdom. This was followed by term “Pro”, indicating the presence of promoter activity, and the number of the clone. For instance, SSU-Pro-1 stands for the first clone amplified from the UK oral metagenome by using SUPA3 and SUPA4 primers. The sequences of these GCs were deposited in the DNA database with the accession number from MH536747 to MH536769.The promoter-containing GCs were named as described in the previous studySupplementary information


Histone modifications play a significant role in the regulation of transcription and various biological processes, such as development and regeneration. Though a few genomic (including DNA methylation patterns) and transcriptomic studies are currently available in switchgrass, the genome-wide distribution of histone modifications has not yet been studied to help elucidate gene regulation and its application to switchgrass improvement.me2 and active- H4K12ac). In this study, most of the histone binding was in non-genic regions, and the highest enrichment was seen between 0 and 2 kb regions from the transcriptional start site (TSS). Considering the economic importance and potential of switchgrass as a bioenergy crop, we focused on genes, transcription factors (TFs), and pathways that were associated with C4-photosynthesis, biomass, biofuel production, biotic stresses, and abiotic stresses. Using quantitative real-time PCR (qPCR) the relative expression of five genes selected from the phenylpropanoid-monolignol pathway showed preferential binding of acetylation marks in AP13 rather than in VS16.This study provides a comprehensive epigenomic analyses of two contrasting switchgrass ecotypes, lowland (AP13) and upland (VS16), by employing chromatin immunoprecipitation sequencing (ChIP-Seq) with two histone marks  contains supplementary material, which is available to authorized users. In eukaryotes, gene expression is a complex, coordinated process orchestrated by transcription factors (TFs) and chromatin proteins. As a result, chromosomal DNA is either compacted or relaxed to repress or activate the transcription process, respectively. At the whole-genome level, genetic and epigenetic factors together regulate gene expression. The accessibility of DNA sequences and chromatin sites associated with nucleosome packaging is known to determine transcription efficiency. Epigenetic factors such as DNA methylation, histone modifications, and chromatin remodelors play a significant role in the regulation of gene expression and various biological processes, such as development , regenerTranscription factors are important in gene regulation; they bind to cis-regulatory sequences in the promoter region of their target genes to fine-tune gene expression . In planPanicum virgatum L.) is a native perennial and warm-season grass, widely used for biofuel, livestock feed, erosion control, and wildlife habitat. Carbon sequestration by switchgrass, a C4 plant with high cellulosic conversion rates and drought tolerance, facilitates its use as a biofuel directly or by ethanolic conversion, making it a valuable, natural renewable energy resource [me2, and an activation mark, H4K12ac) utilized in this study have been studied and reported previously in several plant species [ac is primarily localized in active coding regions of the genome and therefore creates binding sites for regulatory factors to promote transcription [Switchgrass  and lowland (AP13) switchgrass ecotypes.Switchgrass has two main ecotypes, lowland and upland, with distinct morphological and genetic characteristics and specific geographical niches . Two impme2 and H4K12ac), two conditions , and three replicates resulting in (2 X 2 X 2 X 3) a total of 24 samples. Of significance is that this current study on global ChIP-seq analyses utilized the same leaf samples that were collected, stored (− 80 °C), and used for RNA-seq analyses in our previous studies [The two contrasting, heterozygous, tetraploid genotypes (2n = 4x = 36) utilized in this study (AP13 and VS16) were derived from two ecotypes of switchgrass: the lowland cultivar, Alamo and the upland cultivar, Summer, respectively. The leaf materials were obtained from our collaborator and co-author on this manuscript Malay Saha at the Noble Research Institute . Both AP13 and VS16 actively grow in summer, and hence the conditions (29/22 °C day/night temperatures and a 16 h photoperiod) considered in the greenhouses of the Noble Research Institute were uniform. Fully expanded leaves were randomly collected and pooled from five plants of one-month-old clonal ramets, cryopreserved in liquid nitrogen, and stored at − 80 °C. This experiment included two genotypes (AP13 and VS16), two marks (H3K9 studies . Since oac) [me2) [All the lyophilized leaf samples were ground to a fine powder in liquid nitrogen, collected in frozen 50 mL falcon tubes . Next, 25 ml (mL) of cold nuclear isolation buffer containing 1% formaldehyde, and 20 μl (μl) of proteinase inhibitor  were added. The samples were incubated for 5 min at room temperature. Glycine (2 M) was used to terminate the crosslinking reaction. The lysate was filtered before nuclei pelletization at 3000 g for 10 min at 4 °C. The pellet was resuspended in cold nuclear isolation buffer . An equal volume of 15% Percoll solution was added to the lysate, and the nuclei were pelleted again at 3000 g for 5 min at 4 °C. The pellet was then mixed in nuclear lysis buffer. This lysate was then sonicated using a Soniprep 150  for 6 pulses with 15 s between each pulse. The sheared DNA fragments collected after sonication ranged between 100 and 350 bp when visualized by agarose gel electrophoresis. ChIP dilution buffer was then added to the nuclear lysate to attain 10X dilution, and chromatin samples were then divided equally into two tubes. One of the samples was incubated with rabbit polyclonal antibody in a 20:1 mixture that specifically adhered to the N-terminus of histone subunit H4 acetylated at lysine 12 (H4K12ac) . Also, tc) [me2) . Twenty https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA373877).The quality, purity, and size of the immunoprecipitated DNA samples were determined by an AATI Fragment Analyzer . The ChIP-Seq libraries (101 bp paired-end) were prepared by using Illumina TruSeq ChIP Sample Preparation kit . The NGS data generated in this study was submitted to the SRA section of NCBI with the project number PRJNA373877 (http://hannonlab.cshl.edu/fastx_toolkit) to filter bases with less than the Phred33 score of 30 and to remove sequences with less than 50 bases. The quality trimmed reads were mapped to the AP13 v1.1 reference genome, Panicum virgatum, , using bowtie2 (v2.2.6) [me2 and H4K12ac were identified using Spatial Clustering for Identification of ChIP-Enriched Regions  [Sequence quality was assessed using FastQC (v0.11.2)  and qual(v2.2.6)  with defR; v1.1)  for bothR; v1.1)  based onac) and inactive (H3K9me2) modifications from ChIP-Seq analysis. The primers were designed using the TaqMan qPCR primer design tool , and the primer pairs used to amplify the ChIP-enriched regions are presented in Additional file To quantitatively measure the amplification of ChIP-DNA, quantitative real-time (RT)-PCR (qPCR) validation was performed with an ABI 7500 real-time PCR . The five genes selected here belong to the phenylpropanoid pathway that represents the active H4K122ac and iWe used three replicates to test the repeatability of qPCR validations. The universal cons7 primer was used as a constitutive control to determine normalized expression for all samples. Primer efficiency was determined by using 2-ΔΔCT method , where Δme2 and H4K12ac modifications for AP13, respectively. We also obtained 120 million and 108 million clean 101 bp paired-end reads marked respectively with H3K9me2 and H4K12ac for VS16. Average mapping rates of 82.32% (H3K9me2) and 81.20% (H4K12ac) in AP13 and 73.42% (H3K9me2) and 68.43% (H4K12ac) in VS16  as a positive control through the Integrative Genomics Viewer (IGV) [me2 and H4K12ac in both AP13 and VS16, numerous peaks were identified in non-genic regions (over 40%), and less than 40% of peaks were in genic regions  and H4K12ac  modifications and VS16 for H3K9me2  and H4K12ac  modifications has been observed in this study.Overall distributions of H3K9er (IGV)  Fig. 1)me2 and Hme2, while 18,311 (AP13) and 18,000 (VS16) gene-associated peaks were marked with H4K12ac (Table me2 and H4K12ac) in AP13 and VS16, respectively  and 3918 (VS16) gene-associated peaks were marked with H3K9me2 and H4K12ac) and TF binding, the peak distances to nearest genes were binned into 5 kb intervals  , over 18me2 in both genotypes (AP13/VS16), were methyl groups (186/159), methyl CpG-Binding domains (73/69), acetyltransferase groups (36/39), oxidase/desaturase groups (14/16), and chromatin remodeling factors (21/41). Similarly, genes marked with H4K12ac and their corresponding counts for methyl groups, methyl CpG-Binding domains, acetyltransferase groups, oxidase/desaturase groups, and chromatin remodeling factors were 196, 69, 39, 14 and 21 in AP13 and 135, 95, 68, 16 and 41 in VS16, respectively  and two key pathway enzymes; i.e., phosphoenolpyruvate carboxylase (PEPC), that catalyzes PEP and carbonic anhydrase (CA), and two transporter genes viz., tonoplast dicarboxylate transporter (DIT) and bile acid sodium symporter/pyruvate transporter (BASS) were identified as marked in both AP13 and VS16 for both modifications, whereas thiophosphate isomerase was only identified in VS16 for both modifications (Table me2 and H4K12ac in both the genotypes were eight key photorespiratory enzymes: 2-oxoglutarate (2-OG), glutamate (GLU), 3-phosphoglycerate (3-PGA), ribulose 1,5-bisphosphate carboxylase/oxygenase (RuBisCo), glutamine (GLN), dicarboxylate transporter (DIT), catalase (CAT), and glutamate synthase (GS) (Table me2 and H4K12ac in both genotypes.Marked by H3K9me2 and H4K12ac include hydroxycinnamoyl-CoA:shikimate hydroxycinnamoyl transferase (HCT), cinnamyl alcohol dehydrogenase (CAD), phenylalanine ammonia-lyase (PAL), 4-coumarate: CoA ligase (4CL), Cinnamate 4-hydroxylase (C4H), and caffeoyl-CoA 3-O-methyltransferase (CCoAMOT)  stress-related genes are highlighted , drought tolerance (9), salinity tolerance (8), cold tolerance (3), flood tolerance 352), heavy metal tolerance (57), and disease resistance (277) in both genotypes  between the two genotypes (Table Zea mays), CYCLOIDEA (CYC) from snapdragon (Antirrhinum majus), as well as PROLIFERATING CELL NUCLEAR ANTIGEN FACTOR1 (PCF1) and PCF2 (TCP) family, heat shock TFs, Plant AT-rich Sequence and Zinc-binding Protein (PLATZ) TF family protein, and the Basic Helix-Loop-Helix (bHLH) family of TFs.In total, 653 and 650 significantly enriched peaks were annotated as TFs and were methylated and acetylated, respectively, in AP13, while 431 and 579 significantly methylated and acetylated peaks, respectively, were annotated as TFs in VS16. Among these, 17 TFs were differentially marked (H3K9me2 and H4K12ac). Among these, five genes  from the phenylpropanoid–monolignol pathway, differentially marked with histone modifications, were selected to confirm the expression level by qPCR. The significant (P < 0.05) variations, in the relative expressions, of all five genes indicated that they were more marked in AP13 than in VS16. Further, for both H3K9me2 and H4K12ac in these two genotypes the levels of expression were higher for CAD and HCT, while lower for CCoAOMT  [me2) and activation (H4K12ac) was performed using ChIP-Seq analyses in two diverse switchgrass genotypes (AP13 and VS16) belonging to two ecotypes. The percentage of mapped reads to the reference genome in rice ranged between 91.70–98.90% [me2 as wells as 81.20 and 68.43% for H4K12ac in AP13 and VS16, respectively, which are attributed to the genotype-specific adaptations, variations in GC-content, repeat regions, genome duplications and ploidies [Histone modifications, such as H3K9n plants , 11–13. n plants  and othen plants , cotton n plants , and a dornutum) , in thisac than H3K9me2 in both genotypes, which is attributed to the activation of genotype-specific genes  and repressive (H3K27me3) histone modifications formed strong peaks within 1 kb downstream of the TSSs [Populus revealed that ~ 90% of ARK1 and ARK2 binding regions were within 1 kb of the TSSs [me3 distribution showed a clear peak around 300 bp downstream of the TSS in Arabidopsis [Arabidopsis H3K9me2 and H3K4me enrichment spanned around the entire gene body [With only slight variations between the two genotypes, genome-wide histone modification patterns were similar  of peaks associated with H4K12ac and H3K9me2 marks spanned 5 kb upstream and downstream from TSSs in AP13 when compared to VS16.Previous reports in rice  and humaA. thaliana [Populus, the majority of binding regions for all TFs analyzed have shown a general preference for binding near genes, and they were specifically enriched within 5 kb of the TSSs [Arabidopsis revealed that most peaks have been localized around 480 bp downstream of the TSSs. The shape and width of the H3K36ac peaks were independent of gene length [me2 and H4K12ac peaks were localized between 0 and 499 bp downstream, supporting the idea that peak position, shape, and length are independent of gene length. Transcription factors and DNA binding proteins were highly enriched in the cluster of genes associated with H3K27me3 in tissue-specific repression as reported in Arabidopsis (6). Genome-wide comparative analyses using two histone marks, H3K9ac and H3K4me3, in A. thaliana and A. arenosa revealed coordination between histone modifications and gene expression within and between these species, including allopolyploids [Recently, the preferential binding of TFs ~ 86%) between − 1000 to + 200 bp from a TSS has been identified in thaliana . In Poputhe TSSs . Histonethe TSSs . Analysie length . Similar% betweenac, H4K5ac, and H3K18ac) and two histone methylation marks (H3K4me3 and H3K4me2) and targeting five C4 genes , comparative ChIP-Seq analyses reported in maize, sorghum (Sorghum bicolor), and foxtail millet  revealed a role for histone modifications in light-specific and cell type-specific responses [me2 and H4K12ac) between AP13 and VS16 in five prominent C4-pathway genes  five photorespiration-related genes ; and, six monolignol pathway-related genes . Genes that mediate phenylpropanoid pathways play a key role in feedstock quality. A recent histone binding study in Eucalyptus grandis reported that about 8% of phenylpropanoid pathway genes were marked by H3K4me3 [Further, we narrowed our search to identify histone markings on genes related to C4-photosynthesis andphotorespiration pathways, and phenylpropanoid-monolignol pathway, as they play a key role in biomass and biofuel production . Using tesponses . Support H3K4me3 . In thisme2 and H4K12ac in VS16 and AP13, which may contribute to differing genetic potential of these two contrasting ecotypes (upland and lowland). Here, we identified H3K9me2 and H4K12ac modifications on different stress-related genes abundantly marked in both genotypes such as disease-resistance, heat tolerance, and flood tolerance genes. Our recent transcriptome analyses in switchgrass genotypes AP13 and VS16 identified over 200 significantly enriched stress-related genes [me2 and H4K12ac were genes that encode the leucine rich repeats (LRR)-family of proteins, NBS-LRR, NB-ARC, LRR-NB-ARC, and CC-NBS-LRR (disease resistance); the heat shock proteins (HSPs), DNAJ and DNAK (heat tolerance); and the subtilase family protein, LRR-transmembrane protein kinase and adenine nucleotide alpha hydrolase-like superfamily protein (flooding tolerance)  Table . Even thIt is important to validate differentially marked genes in switchgrass, a prominent biofuel crop, especially in regard to the phenylpropanoid–monolignol biosynthesis pathway. Previous reports suggested that over ten different genes play an important role in the phenylpropanoid–monolignol pathway in switchgrass –59, inclme2) and acetylation (H4K12ac) marks in two contrasting ecotypes of switchgrass identified similarities and disparities between AP13 and VS16 at the epigenome level. The distribution of significantly enriched peaks spanned 0–2000 bp around TSSs, and most of these peaks were observed in non-genic regions. Our comprehensive analysis of annotated peaks facilitated the identification of key regulatory marks, genes, and TFs involved in biosynthetic pathways, including C4 photosynthesis, photorespiration, phenylpropanoid–monolignol, as well as biotic and abiotic stresses.Our genome-wide screening for histone methylation Additional file 2:Figure S1. Number of H3K9me2 and H4K12ac binding regions in switchgrass genotypes AP13 and VS16. (PPTX 320 kb)Additional file 3:Figure S2. Heatmap of preferentially marked H3K9me2 and H4K12ac genes along with transcriptional activity 2 kb upstream and downstream from TSSs in switchgrass genotypes AP13 and VS16. (A) H3K9me2-AP13 (B) H3K9me2-VS16 (C) H4K12ac-AP13 and (D) H4K12ac-VS16. Genes were sorted according to their expression level from mRNA analysis. The histone modification intensities are displayed along with − 2 kb to + 2 kb regions around TSSs. (PPTX 1474 kb)Additional file 4:Figure S3. Peak width distributions of H3K9me2 and H4K12ac modifications in switchgrass genotypes AP13 and VS16. (A) H3K9me2-AP13 (B) H3K9me2-VS16 (C) H4K12ac-AP13 and (D) H4K12ac-VS16. The numbers 499 and 999 in peak width bin column refer to the number of peaks having a peak width between 0 to 499 and 500 to 999, respectively. (PPTX 369 kb)Additional file 5:Figure S4. List of epigenomic factors with H3K9me2 and H4K12ac modifications in switchgrass genotypes AP13 and VS16. (PPTX 325 kb)


The MAPK/ERK signaling pathway is an essential regulator of numerous cell processes that are crucial for normal development as well as cancer progression. While much is known regarding MAPK/ERK signal conveyance from the cell membrane to the nucleus, the transcriptional and epigenetic mechanisms that govern gene expression downstream of MAPK signaling are not fully elucidated.This study employed an integrated epigenome analysis approach to interrogate the effects of MAPK/ERK pathway inhibition on the global transcriptome, the active chromatin landscape, and protein–DNA interactions in 501mel melanoma cells. Treatment of these cells with the small-molecule MEK inhibitor AZD6244 induces hyperpigmentation, widespread gene expression changes including alteration of genes linked to pigmentation, and extensive epigenomic reprogramming of transcriptionally distinct regulatory regions associated with the active chromatin mark H3K27ac. Regulatory regions with differentially acetylated H3K27ac regions following AZD6244 treatment are enriched in transcription factor binding motifs of ETV/ETS and ATF family members as well as the lineage-determining factors MITF and SOX10. H3K27ac-dense enhancer clusters known as super-enhancers show similar transcription factor motif enrichment, and furthermore, these super-enhancers are associated with genes encoding MITF, SOX10, and ETV/ETS proteins. Along with genome-wide resetting of the active enhancer landscape, MEK inhibition also results in widespread SOX10 recruitment throughout the genome, including increased SOX10 binding density at H3K27ac-marked enhancers. Importantly, these MEK inhibitor-responsive enhancers marked by H3K27ac and occupied by SOX10 are located near melanocyte lineage-specific and pigmentation genes and overlap numerous human SNPs associated with pigmentation and melanoma phenotypes, highlighting the variants located within these regions for prioritization in future studies.These results reveal the epigenetic reprogramming underlying the re-activation of melanocyte pigmentation and developmental transcriptional programs in 501mel cells in response to MEK inhibition and suggest extensive involvement of a MEK-SOX10 axis in the regulation of these processes. The dynamic chromatin changes identified here provide a rich genomic resource for further analyses of the molecular mechanisms governing the MAPK pathway in pigmentation- and melanocyte-associated diseases.The online version of this article (10.1186/s13072-019-0297-2) contains supplementary material, which is available to authorized users. The mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK) pathway is an evolutionarily conserved signaling cascade that relays multiple signals from the plasma membrane to the nucleus to regulate cell growth, differentiation, proliferation, and survival. In MAPK/ERK pathways, ligand binding to a receptor tyrosine kinase at the plasma membrane leads to activation of the small GTPase RAS. This is followed by a cascade of complex protein interactions and phosphorylation events, including RAF phosphorylation and activation of MAPK kinase (MEK), which directly phosphorylates ERK and regulates its nuclear translocation and transcriptional activity , 2. MAPKOne approach to address this need is through analysis of epigenetic changes that arise from altered MAPK/ERK pathway signaling. Chromatin-mediated processes play crucial roles in the signaling cascades that govern development, cancer progression, and drug resistance –20; howehttps://omim.org/), in which abnormal neural crest/melanocyte development results in phenotypes that include hypopigmentation and enteric ganglia defects. SOX10 predominantly occupies distal (non-promoter) enhancer regions in melanocyte and melanoma genomes, colocalizes with MITF at enhancers, and interacts with BRG1 at melanocyte-specific enhancer regions [The SOX family member SOX10 plays a crucial role in neural crest and melanocyte development as well as human disease. SOX10 mutations are associated with the human neurocristopathies Waardenburg Syndrome (types 2 and 4) and PCWH  mutant melanoma cell line as a model system to evaluate genome-wide changes in gene expression, enhancer chromatin marks, and transcription factor occupancy in response to the MEK inhibitor AZD6244. AZD6244 treatment caused hyperpigmentation as well as widespread alterations in transcription and the active chromatin landscape that were associated with numerous pigmentation genes. Distinct transcriptional motifs underlying AZD6244-induced epigenome remodeling were discovered, including SOX, MITF, ETV, and ATF motifs. A novel cohort of super-enhancers was identified that was enriched in melanocyte lineage-specific transcription factor motifs. Extensive recruitment of SOX10 occurred throughout the genome under MEK inhibition, including increased interactions at H3K27ac-associated enhancers and super-enhancers, with notable binding of SOX10 at super-enhancers associated with AZD6244-upregulated pigmentation genes. Furthermore, many of these chromatin regions directly overlapped GWAS loci for human pigmentation phenotypes. These data provide a roadmap of enhancer activity in a BRAF(V600E) mutant cell line under MEK inhibition, suggest the chromatin regions identified in this study regulate pigment- and developmentally related programs of gene expression, and implicate SOX10 in the regulation of these responses to MEK inhibition.To assess the impact of MEK inhibition on the epigenome in melanoma cells, MEK inhibitor-responsive human 501mel cells (heterozygous for the BRAF(V600E) mutation) were treated with AZD6244 (MEK inhibitor) or DMSO (vehicle control). As expected, treatment of 501mel cells with AZD6244 for 48 h or 72 h eliminated ERK phosphorylation  were identified for 5526 genes following AZD6244 treatment compared to control cells  and downregulated (n = 2911) genes -adjusted To evaluate the broad chromatin changes underlying the cellular response to MEK inhibition, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) was performed for H3K27ac, an established histone modification mark of transcriptionally active regulatory elements such as promoters and enhancers , 29. Tren = 3359) using the Genomic Region Enrichment of Annotation Tool  showed ETV1, ETV4, ETV5, ATF3, and ATF4  of the 250 pigmentation genes that show significant expression changes under MEK inhibition  cells. In addition, examination of SOX10 binding at the 17,517 differentially enriched H3K27ac regions showed increased SOX10 binding under AZD6244 treatment, with greater SOX10 binding at regions with reduced H3K27ac  primarily occur in non-coding genomic regions , 82, 83.MAPK/ERK signaling is crucial for regulation of proliferation, differentiation, and pigmentation of melanocytes, and abnormal MAPK/ERK signaling leads to uncontrolled cell proliferation and melanoma tumorigenesis , 88. We Our analysis used H3K27ac, a marker of active regulatory DNA elements including enhancers, to reveal the epigenetic and transcriptional alterations underlying MEK inhibition. AZD6244 treatment resulted in a dramatic reduction in genomic H3K27ac levels, with over 20,000 regions completely lost, and reduced H3K27ac levels at 94% of the 17,517 enhancer regions with significant acetylation changes. We also examined super-enhancers, because of their predicted role in governing the expression of genes affecting cell development/identity as well as oncogenic pathways –32. We iSOX10, SOX4, SOX11, and MITF) or downregulated  in response to MAPK inhibition. These results correlate with previous studies showing that MITF, ETS1, and ETV1 are downstream effectors of MAPK signaling in normal melanocytes and in melanoma progression [SOX10, MITF, ETS1, ETV1, ETV4, and ETV5 are associated with super-enhancers, suggesting the possibility for autoregulation; future studies will be needed to determine the roles for each of these transcription factor candidates in regulating these enhancers and super-enhancers in melanocytes and melanoma. Importantly, our study also demonstrates that SOX10 directly binds to numerous H3K27ac-marked regions in 501mel cells under MEK inhibition, consistent with SOX10 acting as a downstream effector of the MAPK pathway.Enriched motif analysis of these enhancer and super-enhancer regions suggested that transcription factors with the capacity to bind ETV/ETS, ATF, SOX, E-BOX, and TCF motifs may play a role in regulation of these regions. Interestingly, several factors that bind to these overrepresented motifs are expressed in 501mel cells and either upregulated  and causes substantial bending of the DNA helix via binding to the minor groove, a characteristic shared by all SOX proteins , 103–106SOX proteins generally confer transcriptional regulation by co-binding with additional transcription factors , and canGWAS studies have identified over 800 SNPs associated with human pigmentation variation and melanocyte-related phenotypes –86. MostThis study reveals the extensive genomic responses that result from MEK inhibition in 501mel melanoma cells, including widespread remodeling of the active chromatin landscape, increased recruitment of SOX10 binding to the genome, and re-activation of melanocyte pigmentation and developmental transcriptional programs. Beyond specific mechanistic insight into the MEK inhibitor response in BRAF(V600E) mutant melanoma cells, this study provides a comprehensive genomic resource for future studies to identify potential novel loci for pigmentation and drug resistance in melanocytes and melanoma.2/95% humidified air. For shSOX10 knockdown, stable transfections of either a non-targeting shRNA or two independent shSOX10 constructs were performed in triplicate; then, the cells were subjected to puromycin selection for 2 weeks. All shRNA constructs  were purchased from Dharmacon . All cell lines were treated with DMSO (vehicle control) or 200 nM AZD6244 (MEK inhibitor) for 48 h  or 72 h . Western blots were performed according to standard protocols.The human melanoma cell line 501mel (kindly provided by Dr. Yardena Samuels) was cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS) and DMEM, respectively. The human melanoma cell line A375  and the mouse melanoma cell line B16F10 (ATCC) were cultured in DMEM medium supplemented with 10% FBS. All cell lines were maintained at 37 °C under 5% COhttp://genome.ucsc.edu/) using Burrows Wheeler Alignment , and using input genomic DNA as background control.SOX10 and H3K27ac ChIP-Seq assays were performed essentially as described previously , 117. Alnt , which was implemented in R as follows. MACS detected peaks that overlapped in at least two libraries, i.e., only replicated peaks, were retained by setting minOverlaps = 2 in the dba.count function of DiffBind, and all binding sites identified as overlapping were merged. In order to identify peaks with significant differences in read densities , we first obtained “consensus peaks” as follows: (1) overlapping peaks between DMSO and AZD6244 were merged and the largest 5′ to 3′ footprint was retained; (2) regions with no overlaps were also retained and included in the consensus peaks to enable analysis of peaks that are only present in one sample. Next, the number of reads within each experimental condition overlapping the consensus peaks was counted, and the overlapping reads from the corresponding genomic background (input) were subtracted. Then, contrasts were made between the treatment conditions and significantly differentially enriched regions were determined by implementing DESeq2 within DiffBind, which performs a normalization procedure based on trimmed mean of M values (TMM) using the overall depth of sequencing in each library (ChIP read counts minus input read counts and full library size) and calculates enrichment p values. High-confidence differentially enriched regions were obtained by correcting p values for multiple testing using the Benjamini–Hochberg procedure, and only sites with an FDR < 0.1 were retained for downstream analysis.After identifying ChIP-Seq peaks with MACS2, significantly differentially bound regions across samples were detected by quantification of H3K27ac ChIP-Seq tag density using the Bioconductor software package DiffBind ; http://http://younglab.wi.mit.edu/super_enhancer_code.html, [Super-enhancers were identified using the rank ordering of super-enhancers (ROSE) algorithm . Brieflhttp://homer.ucsd.edu/homer/index.html), with the default settings and whole genome as background [H3K27ac ChIP-Seq regions were scanned for both de novo and known motifs using the findMotifsGenome.pl functionality in HOMER . Further RNA-Seq read quality control and calculation of number of mapped reads were performed using RSeQC [501mel cells were harvested with TRIzol reagent  followed by RNA extraction using the Direct-zol™ RNA Miniprep Kit (Zymo Research). RNA quality was assessed by Bioanalyzer  and samples that passed the quality control metric (RIN > 6.5) were used for RNA-Seq. Paired-end poly-A enriched RNA-Seq libraries were generated by the NIH Intramural Sequencing Center (NISC) using an Illumina TruSeq small RNA kit from ~ 200 ng total RNA. The RNA samples were sequenced to generate at least 54 million 101-base read pairs for each individual library. Following quality filtering, RNA-Seq reads were mapped to the hg19 genome with STAR  and diffng RSeQC  and RSEMng RSeQC , respectThe 250 pigmentation genes identified in this study have been described in a previously published list of 650 genes that show visible integument pigment phenotypes in human, mouse, or zebrafish . The 250Additional file 1: Figure S1. MEK inhibition induces hyperpigmentation in mouse B16F10 and human A375 melanoma cell lines. Cells were treated with 200 nM AZD6244 for 72 h. Cell suspensions are shown above cell pellets, with equal numbers of cells present in samples with and without AZD6244 treatment. Note that the B16F10 cells are pigmented initially, and then exhibited a further darkening of pigmentation under AZD6244 treatment. Figure S2. Enriched biological processes identified by GREAT analyses on the 16,476 regions that were losing H3K27ac and the 1041 regions that were gaining H3K27ac under AZD6244 treatment. Figure S3. Effect of MEK inhibition on super-enhancers in 501mel cells. A) Distribution of H3K27ac ChIP-Seq signal at enhancers (input-subtracted reads per million per enhancer regions) in AZD6244-treated 501mel cells. Enhancers are ranked by decreasing H3K27ac ChIP-Seq signal; the gray dashed line demarcates the boundary between super-enhancers (red) and typical enhancers (gray). Of the six super-enhancer-associated transcription factors with motifs enriched in 501mel super-enhancer regions  persisted as super-enhancer-associated under AZD6244 treatment. B) Box-and-scatter plots of super-enhancer signal density (rpm/bp) in control (SE_DMSO) and AZD6244 treated (SE_AZD) 501mel cells. Significance of the difference between distributions determined using a two-tailed t test. ***p value ≤ 0.001. Figure S4. SOX10 shows increased genome-wide binding under AZD6244 treatment, including recruitment to H3K27ac-marked regions. The numbers of SOX10 binding sites from SOX10 ChIP analysis throughout the genome and in relation to H3K27ac-marked regions in 501mel cells are shown, both under control (DMSO-treated) and AZD6244-treated conditions. Figure S5. Significantly enriched transcription factor motifs identified under SOX10 ChIP-Seq peaks. Enriched motifs are shown for (A) control (DMSO-treated) conditions and (B) AZD6244-treated conditions. Figure S6. Transcription factors that regulate melanocyte lineage and pigmentation processes undergo significant expression changes under AZD6244 and are associated with SOX10-bound super-enhancers. UCSC genome browser tracks are shown for the transcription factors MITF, ETS1, ZEB2, SNAI2, and TFAP2A. Colored tracks indicate control and AZD6244-treated regions with H3K27ac binding (pink and gray), with differentially acetylated H3K27ac levels (orange and brown), super-enhancers (light and dark green), and SOX10-ChIP genomic binding . Note the novel recruitment of SOX10 to the super-enhancer regions associated with these genes resulting from AZD6244 treatment . Figure S7. Partial reduction of SOX10 protein using shRNA knockdown in 501mel cells does not prevent the ability of AZD6244 to induce pigmentation. We performed stable knockdowns in triplicate experiments using two different shSOX10 constructs (shSOX10 #2 and shSOX10 #5), and these cells were then subjected to either DMSO or AZD6244 treatment for 72 h. Western blotting for SOX10, phospho-ERK and GAPDH (loading control) showed that each shSOX10 induced a partial knockdown of SOX10 protein, and AZD6244 treatment reduced phosphorylated ERK levels. Visual inspection of the cells indicated that while all cells under DMSO treatment remained unpigmented (indicated by “−” sign), pigmentation was induced under AZD6244 treatment for all cells (indicated by “+” sign), including in the context of shSOX10 treatment. NT = non-targeting.Additional file 2: Table S1. Genes exhibiting significantly different expression changes in 501mel cells in response to AZD6244 treatment. Table S2. The 250 pigmentation genes with differential expression in 501mel cells under AZD6244 treatment. Genes associated with H3K27ac loss or gain regions by GREAT analysis, or with SOX10 binding sites under AZD6244 are also indicated. Table S3. Top 5 significantly enriched transcription factor motifs identified by de novo motif enrichment analysis of genomic regions that were losing or gaining H3K27ac in 501mel cells in response to AZD6244 treatment. Table S4. The 799 super-enhancers identified in 501mel cells treated with DMSO only, and the 583 super-enhancers identified in 501mel cells treated with AZD6244. Table S5. Top enriched biological processes from GO analyses of 1080 genes associated with super-enhancers in control (DMSO-treated) 501mel cells. Table S6. Super-enhancer-associated pigmentation genes with significant expression changes following AZD6244 treatment. Table S7. GWAS loci associated with pigmentation and/or melanoma phenotypes that overlap with H3K27ac regions and/or with SOX10 binding sites.


Bidirectional gene promoters affect the transcription of two genes, leading to the hypothesis that they should exhibit protection against genetic or epigenetic changes in cancer. Therefore, they provide an excellent opportunity to learn about promoter susceptibility to somatic alteration in tumors. We tested this hypothesis using data from genome-scale DNA methylation (14 cancer types), simple somatic mutation (10 cancer types), and copy number variation profiling (14 cancer types). For DNA methylation, the difference in rank differential methylation between tumor and tumor-adjacent normal matched samples based on promoter type was tested by the Wilcoxon rank sum test. Logistic regression was used to compare differences in simple somatic mutations. For copy number alteration, a mixed effects logistic regression model was used. The change in methylation between non-diseased tissues and their tumor counterparts was significantly greater in single compared to bidirectional promoters across all 14 cancer types examined. Similarly, the extent of copy number alteration was greater in single gene compared to bidirectional promoters for all 14 cancer types. Furthermore, among 10 cancer types with available simple somatic mutation data, bidirectional promoters were slightly more susceptible. These results suggest that selective pressures related with specific functional impacts during carcinogenesis drive the susceptibility of promoter regions to somatic alteration. BRCA1 and TP53 [Approximately 10% of human genes have bidirectional promoters , where aand TP53 ,7. Neverand TP53 ,3. In moBidirectional promoters can be classified into two types: 1) a bidirectional promoter between two genes that code for protein called coding/coding (C/C) bidirectional promoters; and (2) a bidirectional promoter with one protein coding and one noncoding gene called coding/noncoding (C/N) bidirectional promoters. The incomplete characterization of functional noncoding transcripts puts noncoding/noncoding bidirectional promoters outside the scope of this work. Bidirectional promoters are also enriched for CpG islands, with approximately 80% of these promoters containing a CpG island . Functio a bidirePSENEN and U2AF1L4 are co-expressed from a small, common promoter in multiple cell types, and mutations in the promoter affect the transcription of both genes [PSENEN a component of the γ-secretase complex required for Notch signaling [U2AF1L4 a pre-mRNA splicing factor [SIRT3 and PSMD13 has been confirmed [SIRT3 regulates the mitochondrial response to stress [PSMD13 plays a role is degrading abnormal proteins [Given that in many contexts bidirectional promoters directly affect the transcription of two genes, genetic mutations or epigenetic changes that affect the promoter region could have twice the impact they might have in single gene promoters. These impacts could be particularly deleterious, given the enrichment for important functions that genes with this arrangement exhibit. Therefore, it has been suggested that adverse changes might be selected against more robustly than in single gene promoters . An examth genes . Neverthignaling , and U2Ag factor . Thus, monfirmed . Howeverproteins . To dateproteins . This suproteins , suggestde novo methylation in cancer. However, the study used an unpublished dataset of an unknown sample size and relied on methylated CpG island amplification/representational difference analysis (MCA/RDA) to identify differentially methylated CpG islands, a technique that does not have the broad coverage and sensitivity of more recent methylation microarrays. Although we are not aware of a study of somatic mutation in bidirectional promoters per se, somatic mutation density as it relates to chromatin accessibility has been studied [The only study we are aware of to test the hypothesis that alterations to bidirectional promoters are selected against in carcinogenesis investigated it in the case of DNA methylation changes in cancer . That wo studied . It was  studied  and thatTo test the hypothesis that bidirectional promoters are protected from somatic alteration in the process of carcinogenesis, we compared differential methylation across 14 cancer types and 710 matched samples, somatic mutation across 10 cancer types and 2473 samples, and copy number alteration across 14 cancer types and 6763 samples in C/C and C/N bidirectional gene promoters to single gene promoters. This work comprises the largest and most comprehensive examination of differential methylation, somatic mutation, and copy number alteration in bidirectional promoters in cancer to date.We tested the hypothesis that the mean rank of differential methylation between tumor and tumor-adjacent normal samples is different between single gene and either C/C or C/N bidirectional promoters to indicate if a greater change in methylation was observed in one promoter type compared to the other using a two-sided Wilcoxon rank sum test. Overall and irrespective of promoter type, there is a tendency towards increased methylation of promoters in tumor samples compared to tumor-adjacent normal tissue. However, for each of the 14 cancer types examined , the chaTo control for the effect of G/C content on the results, we restricted the promoter regions to only those intersecting CpG islands as annotated in the UCSC Genome Browser . The resWe examined the odds of simple somatic mutations (SSMs) occurring in bidirectional vs. single gene promoters using 12 datasets covering 10 cancer types . For mosWe next investigated the association of copy number alteration to bidirectional vs. single gene promoters using the same 14 cancer types used to study changes in DNA methylation . We compp = 2.22 × 10−1), although the result is not statistically significant. C/N bidirectional promoters have even greater odds of intersecting chromosomal fragile sites , although it is still not statistically significant.Past work has also suggested an association between copy number alteration and chromosomal fragile sites, which tend to break more frequently under the stress of replication . TherefoIt has also been shown that the breakage frequency of chromosomal fragile sites is negatively correlated with CpG island density. Given that bidirectional promoters tend to have a higher percentage of CpG islands than single gene promoters, we compared the odds of a region of copy number variation intersecting a C/C or C/N bidirectional promoter to the odds for a single gene promoter only for promoters with CpG islands. For all cancers, there was a reduced odds of somatic copy number change for C/C bidirectional promoters compared to single gene promoters, even after restricting to only those regions with CpG islands , and mosp = 6.45 × 10−2).To extend our investigation, we also considered the enrichment of genes with bidirectional promoters vs. single gene promoters in the Catalog of Somatic Mutations in Cancer (COSMIC) cancer Gene Census , downloaTo assess the relationship between accessible chromatin and promoter type, we compared the odds of C/C or C/N bidirectional promoters intersecting DNAse hypersensitive sites (DHSs) to those of single gene promoters intersecting DHSs. We obtained DHS data from the Roadmap Epigenomics Project for four tissues: breast, pancreas, ovary, and placenta ,23. In ehttp://www.webgestalt.org/) [p-value of at most 0.01. Consistent with previous work, we found that genes with C/C bidirectional promoters are enriched for chromatin organization, DNA repair genes, metabolic processes, and other functions previously identified  (lt.org/) ,25. We uentified . NotablyPast research indicated that bidirectional promoters may not have any particular protection against changes in methylation in cancer . HoweverFor several cancers, either C/C or C/N bidirectional promoters appear to be somewhat more susceptible to simple somatic mutations in cancer compared to single gene promoters, and our results suggest this result is driven by differences in the nucleotide content of the different promoter types. This result is somewhat surprising, because bidirectional promoters tend to be more active and accessible then single gene promoters, and previously, Polak, et al. linked such accessibility to a lower somatic point mutation density in cancer . This coFor most cancers, both C/C and C/N bidirectional promoters have lower odds of intersecting regions of somatic copy number variation than single gene promoters. After controlling for differences in G/C content, this result is only clear for C/C bidirectional promoters. This is interesting, because bidirectional promoters are more likely to intersect chromosomal fragile sites and thus may represent selection against change in copy number for regions with bidirectional promoters in most tumors, although this enrichment in chromosomal fragile sites was not statistically significant. However, not all chromosomal fragile sites break with the same probability. There is a negative correlation between breakage frequency and CpG island density . NeverthIn the past, it has been noted that genes with bidirectional promoters include genes causally relevant to cancer. However, we did not find that genes with a C/C bidirectional arrangement had higher odds of being known causal cancer genes, with reference to COSMIC’s cancer gene census. Nevertheless, this may be the case for genes with C/N bidirectional promoters . Concordant with past work, we did find that genes with C/C bidirectional promoters are enriched for chromatin organization, DNA repair, and metabolism functions . Genes wThis work comprised the largest analysis yet performed of genetic and epigenetic alterations to bidirectional promoters in cancer. We showed that genes with bidirectional promoters exhibit robust protections from changes in DNA methylation and copy number alteration, supporting the hypothesis that bidirectional promoters are protected, relative to other promoters, from these changes. Given that these results were only robust for C/C bidirectional promoters, it is not necessarily directly related to their bidirectional arrangement. It may be that genes with certain functions tend to be arranged in this way, and it is their function that causes the selection against change. In any case, these results suggest that the bidirectional promoter arrangement is enriched for genes that stay active, even in cancer, a finding which needs further confirmation and study. They further suggest that cancer cells require normal function from many genes with bidirectional promoters, which could lead to susceptibility to synthetic lethality involving some gene pairs that involve genes with bidirectional promoters. We also demonstrated that, in a number of cancers, genes with bidirectional promoters tend to accumulate a greater number of simple somatic mutations, possibly driven by their higher G/C nucleotide content. Furthermore, we defined a subclass of bidirectional promoters, which include one noncoding gene in the pair, and showed that in terms of their protection again change in cancer, they share some properties with other bidirectional promoters, although they are not enriched for the same functions that many other genes with bidirectional promoters share.It has long been understood that selection for somatic alterations plays a critical role in carcinogenesis, but the complex landscape of mutations across cancers makes it difficult to understand the underlying process and why some mutations and not others get selected, or even which mutations may play a more important role in disease progression. In this work, we take another step forward in understanding this complex process, demonstrating how multi-layered constraints might affect selection, as tumor cells must still remain viable. Furthermore, we provide evidence that bidirectional promoters are an important genomic architecture that is protected from somatic alteration in addition to the germ line, as has been noted previously. Finally, our results suggest that when somatic alterations do occur in bidirectional promoters, particular notice should be paid, and the functional consequences of both genes in the pair should be considered.We defined a region as a bidirectional promoter if it fell between the TSSs of genes on opposite strands that are within 1000 bp of each other and extended this region to include 200 bp downstream of each TSS. We restricted our definition to exclude promoters with overlapping genes. Bidirectional promoters were then identified by querying the annotables package for R, which includes annotations for the GRCh37 version of the human genome obtained through Ensembl Biomart . We thenp-value for differential methylation based on a linear model of the M-values (which have a distribution more amenable to linear models [DNA methylation profiles were created by The Cancer Genome Atlas (TCGA)  using Ilr models ) for theSimple somatic mutation (SSM) data were obtained through the International Cancer Genome Consortium’s Data Portal . We down2 (n/2) and n is the mean copy number for a segment.Somatic copy number alteration data were downloaded through the Genomic Data Commons (GDC) Data Portal . These d


KLF1, are common within certain populations of the world. Heterozygous missense mutations in KLF1 mostly lead to benign phenotypes, but a heterozygous mutation in a DNA-binding residue (E325K in human) results in severe Congenital Dyserythropoietic Anemia type IV (CDA IV); i.e. an autosomal-dominant disorder characterized by neonatal hemolysis.Mutations in the transcription factor, Klf1−/− genetic background. Nuclear translocation of wild type KLF1 results in terminal erythroid differentiation, whereas mutant KLF1 results in hemolysis without differentiation. The E to K variant binds poorly to the canonical 9 bp recognition motif (NGG-GYG-KGG) genome-wide but binds at high affinity to a corrupted motif (NGG-GRG-KGG). We confirmed altered DNA-binding specificity by quantitative in vitro binding assays of recombinant zinc-finger domains. Our results are consistent with previously reported structural data of KLF-DNA interactions. We employed 4sU-RNA-seq to show that a corrupted transcriptome is a direct consequence of aberrant DNA binding.To investigate the biochemical and genetic mechanism of CDA IV, we generated murine erythroid cell lines that harbor tamoxifen-inducible (ER™) versions of wild type and mutant KLF1 on a Since all KLF/SP family proteins bind DNA in an identical fashion, these results are likely to be generally applicable to mutations in all family members. Importantly, they explain how certain mutations in the DNA-binding domain of transcription factors can generate neomorphic functions that result in autosomal dominant disease.The online version of this article (10.1186/s12864-019-5805-z) contains supplementary material, which is available to authorized users. Klf1−/− mice die in utero from severe anemia  was kept constant and the [protein] was altered in different reactions. The [protein] at which 50% is bound represents the KD. Curves were fit individually using Specific Binding with Hill Slope. Averaged KD and its standard error are reported.Electromobility shift assays were performed as previously described with some modification . Bindingwww.graphpad.com. Alignments of human and mouse KLF1 proteins was performed using the program Geneious version R7 [http://easeq.net. Proportional Venn Diagrams were produced using EulerAPE [swapaa, tug and minimize functions within the OpenMM dynamics environment. Structural images were then prepared in ChimeraX.Statistics analysis was performed with GraphPad Prism version 6.04 for Windows, GraphPad Software, La Jolla California USA, rsion R7 . Densityrsion R7 , availabEulerAPE . ModelinEulerAPE . StructuEulerAPE , 35. MutAdditional file 1:Figure S1. Inducible cell lines to study human CDA type IV. Full length western blot of nuclear extracts from cell lines generated in this study as shown in Fig. Additional file 2:Figure S2. Central enrichment of identified PWM. CentriMo analysis of PWM (motif) identified by MEME is found closest to the summit of ChIP-seq peaks for KLF1-E339K-ER (A) and KLF1-ER (B). (JPG 302 kb)Additional file 3:Figure S3. 4sU-labeled RNA Enrichment. Validation by qRT-PCR of 4sU-RNA isolation from total RNA. Samples used for RNA-seq analysis were validated for enrichment of primary transcript (4sU-labeled) relative to mature Hprt (A) and E2f2 (B) transcripts. (JPG 305 kb)Additional file 4:Figure S4. Gene Set Analysis of KLF1-E339K-ER DEGs. Gene signatures from the analysis of K1-E339K-ER cells versus K1 were tested using the gene set analysis toolkit on the KEGG pathway using Entrez IDs Additional file 6:Table S2. K1-E339K-ER4sU-RNA-seq DEGs. Gene expression changes following activation of KLF1-E339K-ER compared to K1 (Klf1−/−) cells. Differentially Expressed Genes (DEGs) using a false discovery rate (FDR) of 0.05 with the treat method employing a minimum log-fold-change of 2. (XLSX 1979 kb)


DNase-seq and ATAC-seq are broadly used methods to assay open chromatin regions genome-wide. The single nucleotide resolution of DNase-seq has been further exploited to infer transcription factor binding sites (TFBSs) in regulatory regions through footprinting. Recent studies have demonstrated the sequence bias of DNase I and its adverse effects on footprinting efficiency. However, footprinting and the impact of sequence bias have not been extensively studied for ATAC-seq.Here, we undertake a systematic comparison of the two methods and show that a modification to the ATAC-seq protocol increases its yield and its agreement with DNase-seq data from the same cell line. We demonstrate that the two methods have distinct sequence biases and correct for these protocol-specific biases when performing footprinting. Despite the differences in footprint shapes, the locations of the inferred footprints in ATAC-seq and DNase-seq are largely concordant. However, the protocol-specific sequence biases in conjunction with the sequence content of TFBSs impact the discrimination of footprint from the background, which leads to one method outperforming the other for some TFs. Finally, we address the depth required for reproducible identification of open chromatin regions and TF footprints.We demonstrate that the impact of bias correction on footprinting performance is greater for DNase-seq than for ATAC-seq and that DNase-seq footprinting leads to better performance. It is possible to infer concordant footprints by using replicates, highlighting the importance of reproducibility assessment. The results presented here provide an overview of the advantages and limitations of footprinting analyses using ATAC-seq and DNase-seq.The online version of this article (10.1186/s13059-019-1654-y) contains supplementary material, which is available to authorized users. These tissue- and developmental stage-specific regulatory elements reside in nucleosome-free, accessible regions of the genome that are hypersensitive to nuclease attack [The discovery and characterization of e attack . Digestie attack , 3 and we attack , 5 or the attack . A more e attack . Both prTranscription factors (TFs) bound at CREs are major regulators of gene expression . As protThe efforts to assay bound sites genome-wide via TF footprinting have come under scrutiny by studies demonstrating that DNase I cleaves the underlying DNA in a non-uniform manner, where sequence composition dictates the cleavage propensities  , 29. ThiRecent efforts have explored the feasibility of TF footprinting with ATAC-seq , 25, 30;Early ATAC-seq libraries generated with the original protocol have large numbers of reads mapping to mitochondrial DNA (mtDNA) that need to be discarded, which severely impact the final library depth . For an p value < 2.2e−16), with a slightly higher odds ratio for the modified protocol (13.15 vs. 10.75). This improved agreement at the open chromatin region level, albeit moderate, provided further support that avoiding detergent lysis increases the concordance between ATAC-seq and DNase-seq.To adequately quantify the protocol-related differences of ATAC-seq vs. DNase-seq, we also generated a single-hit DNase-seq library in K562 cells and compared this alongside three other publicly available single-hit DNase-seq datasets , a factor with a well-known, high information content binding site with substantial available ChIP-seq data including in HEK293 cells , see the “We next investigated the individual contributions of bias modeling and replicates to this increased concordance and accuracy. The contribution of the replicates comes from the application of IDR as mentioned above, which creates a systematic way to find relevant cutoffs for the footprint score. To elucidate the contribution of bias, we first trained CTCF footprint models in the combined ATAC-seq and DNase-seq replicates, as outlined above, but using a uniform background, which is equivalent to no bias correction see The “” sectionPrevious studies that inferred cell type-specific TF binding site annotations from DNase footprint data typically used very large datasets (with hundreds of millions of reads per cell type) , 17. To To elucidate whether the previous observations would also apply more generally beyond CTCF, we conducted the footprinting analysis on other factors. The limited availability of ChIP-seq data in HEK293 cells motivated an experimental setup to learn the footprint shapes in K562 cells, where ChIP-seq data is more abundant , for all TFs in both experimental settings, we calculated the area under the receiver operating characteristic curve (AUC), ranking candidate sites by FLR and considering those that overlap ChIP-seq peaks to be true binding sites. In order to assess how the performance is linked to the relationship between the footprint and background models, the Pearson correlations between these 2 models  for each TF were calculated and compared to the AUCs. The AUCs negatively correlated with the footprint-background model similarities in both ATAC-seq and DNase-seq datasets Fig. c, suggesAs the background component is derived directly from sequence bias and given our previous observation that DNase-seq bias correction shows a stronger positive effect compared to ATAC-seq bias correction, we once again explored the role of bias more explicitly. In particular, two of three factors for which ATAC-seq outperformed DNase-seq, MEF2A, and STAT1 had the lowest DNase I cleavage propensities  over their motif regions, among all assayed factors Fig. d, whereaDNase-seq has been widely used to assay open chromatin regions and TF footprints. The emergence and increasing use of ATAC-seq necessitate a systematic comparison of the two methods, especially for TF footprinting. Here, in a comparative setting, we have shown that although the two methods have distinct sequence biases and generate different footprint shapes for the same TF, the sites they identify as bound are largely in agreement. However, the sequence content of TFBSs combined with protocol-specific sequence biases impacts footprinting efficiency for some TFs, leading to larger differences for these factors and making one method preferable to the other.There are opposing views on the library depth required for TF footprinting. Whereas some studies require at least 200 million reads , others Although the sequence bias of DNase I is well characterized, there is still no consensus about the benefits of bias correction for TF footprinting. Whereas some studies report increased accuracy upon bias correction , others k-mer with eight meaningful positions is used to correct ATAC-seq data [Recent studies have proposed several Tn5 bias correction methods, and in order to rule out that this observation resulted from our 6-mer-based approach  and Dulbecco’s modified Eagle’s medium (DMEM), respectively, both complemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin.DNase-seq experiments were conducted on 50 million cells as previously described , with thhttp://broadinstitute.github.io/picard/). Further processing was performed to filter out contigs as well as the Y and mitochondrial chromosomes and retain only the reads that aligned concordantly as a pair within the expected fragment length range (38–1500 bp).ATAC-seq experiments were performed on 50,000 cells for the K562 samples and 100,000 cells for the HEK293 samples, following the published protocol  but incrLibrary complexity and saturation were calculated using the preseq program , using tIn order to find open chromatin regions, peak calling was performed on the processed DNase-seq and ATAC-seq datasets using JAMM , with paWhere replicates were available, peaks in agreement between the two replicates were found using the irreproducible discovery rate (IDR) pipeline . SpecifiFor K562 ATAC-seq datasets, where replicates were not available, reads of the modified dataset with no lysis buffer were randomly subsetted to match the library depth of the original protocol with 10 min lysis, and peaks were called using JAMM as described above, with the addition of the -e auto parameter for automatic estimation of a minimum fold enrichment. These K562 ATAC-seq peak sets were used to infer signal to noise ratios by calculating log2.The sequence bias of the Tn5 transposase was calculated in the form of 6-mers, similar to the previous calculations of DNase bias . To thisThe Tn5 dimer cleaves the plus and minus strands with a 9-bp offset, and consequently, most studies analyzing ATAC-seq datasets employ a correction where the reads that align to the plus strand are shifted by + 4, and the reads that align to the minus strand are shifted by − 5 bases, to update the read start sites to represent the center of this 9-bp core sequence. However, we focus on the actual 5′ ends of the reads, as these are the cleavage sites, akin to the DNase I cut sites. Our first reason for this is illustrated in Additional file The average Tn5 transposition propensity in a candidate binding site of a given transcription factor was calculated by retrieving and counting all 6-mers associated with the site (without flanks). The counts were multiplied by the Tn5 transposition propensities of the associated 6-mers, summed and normalized by the total number of 6-mers in the site. The same calculation was applied for DNase, using the previously calculated DNase cleavage propensities per 6-mer .−6), we found the corresponding TFBS scores and reported the one closest to the lowest score in each motif set and the associated FPR as an empirical p value. This demonstrated that all our sets included significant motif matches, with the lowest empirical p value being 5 × 10−5 , where each value corresponded to the cleavage/transposition probabilities at a given nucleotide. For the footprint component, these probabilities were found by computing the aggregate DNase or ATAC-seq signal (from the 5′ ends of the reads) around the TFBSs that overlap ChIP-seq peaks for that factor and re-estimating the signal via expectation maximization. For the background component, the probabilities were calculated as the signal that would be expected solely due to the protocol-specific bias values, given the sequences around the candidate TFBSs (parameter Background = “Seq”). As we had previously not observed a distinct difference in performance, the background was kept fixed and not re-estimated (parameter Fixed = T). Once both components were learned, footprint scores were calculated for all candidate TFBSs, as the log odds of footprint vs. background . To learn footprint models without bias correction, our method was applied as described above, but with a uniform, fixed background model that assumes equal cleavage probabilities at each nucleotide.Transcription factor footprinting was performed with a site-centric method from our lab as previously described . SpecifiThe IDR strategy was applied here as well where replicates were available, to find reproducible footprints. To this end, candidate TFBSs with positive FLRs in both replicates were chosen and ranked by FLR. IDR analysis was performed with the same parameters as explained for peak calling, where FLR values replaced signal intensities. Again, the number of sites that passed the stringent 0.01 IDR threshold was noted. Finally, TFBSs were ranked by the average FLR from the two replicates and truncated according to the IDR result. This led to the reproducible FLR-IDR footprints.Footprint model AUCs  were calculated by fourfold cross-validation. Briefly, the data was split into four parts, and for TFBSs in each part, FLR was calculated using footprint and background models learned from the other three parts. TFBSs were ranked by FLR, and those intersecting ChIP-seq peaks were labeled as the true positives. The AUCs obtained from the four parts were averaged to obtain the final value. Similarly, sensitivity and specificity measures were also obtained using models trained on three out of four parts of the data and tested on the remaining part.Correction of Tn5 sequence bias in K562 ATAC-seq data with the seqOutBias  softwareAdditional file 1:Figure S1. Read count correlations of all K562 datasets and comparison of K562 ATAC-seq datasets generated with the original (10 min lysis) and modified (no lysis buffer) protocols. Figure S2. Read count correlations of all HEK293 datasets. Figure S3. Analysis of reproducible peaks in HEK293 cells. Figure S4. Library complexity and saturation plots for HEK293 ATAC-seq datasets. Figure S5. Analysis of footprint models learned in HEK293 datasets and assessing the effects of bias correction on footprinting performance. Figure S6. The relevance of the learned footprint models as illustrated by model similarity across datasets and concordance between footprint scores and ChIP-seq signals. Figure S7. Analysis of NRF1 footprints. Figure S8. Method and TF-specific footprinting efficiency. Figure S9. 6-mer bias correction strategy for ATAC-seq datasets. Table S1. General statistics of the ATAC-seq datasets generated in the study. Table S2. Descriptions, accession codes and final read counts for the utilized DNase-seq datasets and libraries generated by Tn5 transposition of deproteinized genomic DNA. Table S3. Scheme for ATAC-seq library comparisons for JAMM-IDR peak calls or FLR-IDR footprint calls. Table S4. ChIP-seq peaks used in the analysis. Table S5. PWM IDs used for genome-wide motif searches. (PDF 3299 kb)