Δtet-AG, a complex with a SEP3 splice variant, SEP3Δtet, which is largely dimeric and SEP3–AG tetramer. SEP3 and SEP3–AG share numerous bound regions, however each complex bound unique sites, demonstrating that protein identity plays a role in DNA-binding. SEP3–AG and SEP3Δtet-AG share a similar genome-wide binding pattern; however the tetrameric form could access new sites and demonstrated a global increase in DNA-binding affinity. Tetramerization exhibited significant cooperative binding with preferential distances between two sites, allowing efficient binding to regions that are poorly recognized by dimeric SEP3Δtet-AG. By intersecting seq-DAP-seq with ChIP-seq and expression data, we identified unique target genes bound either in SEP3–AG seq-DAP-seq or in SEP3/AG ChIP-seq. Seq-DAP-seq is a versatile genome-wide technique and complements in vivo methods to identify putative direct regulatory targets.The MADS transcription factors (TF), SEPALLATA3 (SEP3) and AGAMOUS (AG) are required for floral organ identity and floral meristem determinacy. While dimerization is obligatory for DNA binding, SEP3 and SEP3–AG also form tetrameric complexes. How homo and hetero-dimerization and tetramerization of MADS TFs affect genome-wide DNA-binding and gene regulation is not known. Using sequential DNA affinity purification sequencing (seq-DAP-seq), we determined genome-wide binding of SEP3 homomeric and SEP3–AG heteromeric complexes, including SEP3 MCM1 (Saccharomyces cerevisiae), AGAMOUS , DEFICIENS (Antirrhinum majus) and Serum response factor (Homo sapiens), provide a key example of a TF family that is present in almost all eukaryotes and binds a highly conserved DNA sequence called a CArG-box (CC-’Adenine-rich’-GG) as an obligate dimer (Transcription factors (TF) often act cooperatively by forming heteromeric complexes, with proper oligomerization patterns thought to be a key component of gene regulation . How olite dimer . In seedte dimer . The cante dimer . HoweverA. thaliana, the MADS TFs, APETALA1 (AP1), APETALA3 (AP3), PISTILLATA (PI), AGAMOUS (AG) and the SEPALLATA clade  act combinatorially to specify organ identity. The first whorl sepal identity is determined by the tetrameric AP1-SEP complex, petal identity is specified by AP1-SEP-AP3-PI, stamen identity by AP3-PI-SEP-AG and carpel identity by tetrameric SEP-AG. These unique MADS complexes exhibit slightly altered DNA binding specificity, resulting in the expression and/or repression of different sets of target genes  experiments also suggested that tetrameric complexes are able to access certain sites, which dimeric complexes do not bind, although these experiments are based on only a few sequences   ,22, provin vivo methods, which will sample all possible physiological complexes containing the target TF. Using this method, the genome-wide binding of the SEP3 homomeric complex, the SEP3–AG heterotetramer and the SEP3Δtet-AG heterodimer were determined. These data show that SEP3 and SEP3–AG share many binding sites, but that SEP3–AG is able to access additional genomic regions. Comparing SEP3–AG and SEP3Δtet-AG demonstrates that tetramerization results in a preference for specific CArG-box intersite spacing as well as globally increasing in DNA-binding affinity. Comparing seq-DAP-seq with ChIP-seq and expression data for AG and SEP3 allowed the identification of putative in vivo direct targets of the SEP3–AG complex. Thus, seq-DAP-seq acts as both an alternative and as a complementary method to ChIP-seq for identifying genome-wide binding sites and putative target genes for the SEP3–AG MADS TF complexes. More broadly, these studies demonstrate the efficacy and simplicity of seq-DAP-seq in determining genome-wide binding of specific heteromeric complexes and identifying a large percentage of potential regulated in vivo targets.We developed a modified DAP-seq method for MADS TFs we term sequential DAP-seq (seq-DAP-seq). Using sequential purification based on multiple tags, we specifically isolate heteromeric MADS TF complexes prior to DNA-affinity purification. This facilitates the identification of binding sites corresponding to a single TF complex as opposed to Δtet (At1g24260.3), a SEP3 splice variant with deletion of amino acids 161–174 and AG (AT4g18960.1) coding sequences were polymerase chain reaction (PCR) amplified and cloned into modified pTnT vectors (Promega) to generate the following C-terminal-tagged constructs: SEP3–3×FLAG, SEP3Δtet -3×FLAG and AG-5×MyC.SEP3 (At1g24260.2), SEP3In vitro protein production was carried out using TnT® SP6 High-Yield Wheat Germ Protein Expression System (Promega L3260) according to the manufacturer's instructions. For single protein DAP-seq, i.e. SEP3, 2 μg input plasmid DNA was used in a 50 μl TnT reaction. For seq-DAP-seq, equal molar amounts of purified plasmid DNA (around 2 μg per construct) were used as input in a 50 μl TnT reaction. For protein complex purification, the 50 μl TnT reaction producing SEP3–AG or SEP3Δtet-AG was combined with 50 μl IP buffer ) and mixed with 20 μl anti-FLAG magnetic beads (Merck Millipore). Following 1 h incubation at room temperature, the anti-FLAG magnetic beads were immobilized, and washed three times with 100 μl IP buffer. Protein complexes were eluted with 100 μl IP buffer supplemented with 200 μg/ml 3×FLAG peptide (Merck Millipore). The eluted protein was then immobilized on anti-c-Myc magnetic beads (Thermo Scientific) and washed three times with 100 μl IP buffer in order to isolate homogeneous SEP3–AG or SEP3Δtet-AG complexes. In order to verify successful TnT protein production, Western blots of the samples after each immunoprecipitation step were run. The purified protein complex . The reaction was incubated for 90 mins and then washed six times using 100 μl IP buffer. The bound DNA was heated to 98°C for 10 min and eluted in 30 μl EB buffer . The eluted DNA fragments were PCR amplified using Illumina TruSeq primers for 20 cycles, and purified by AMPure XP beads (Beckman). Due to the higher elution efficiency in our hands using FLAG versus Myc peptides, the anti-FLAG immunoprecipitation step was performed first for all heteromeric complexes. The libraries were quantified by qPCR using NEBNext Library Quant Kit for Illumina following manufacturer's instructions. Libraries with different barcodes were pooled with equal molarity, and sequenced on Illumina HiSeq (Genewiz) with specification of paired-end sequencing of 150 cycles. Each library obtained between 10 and 20 million reads. All DAP-seq and seq-DAP-seq experiments were performed in two to four replicates . The first CArG box was predicted to be strongly bound with a score of 0.87 based on transcription factor flexible model (TFFM), with 1.0 being the highest score. Two additional CArG boxes were identified at 45–46 bp from the first site with low scores (0.006 and 0.017). The CArG box spacing was modified with 5 bp deletions or additions between CArG boxes. For EMSAs in Figure EMSA were performed as described . The 96-KNU WT sequence: GGTAAGAGAAACATAGAAACCTTCCATGTTTGGCAATTTCATCTTGGAACTTGATTCACTCTCTCTTGTCTTCTTTGTGCATCACAAGAACAACAACCATGTTTGGCAATTTCATCTTGGAACTTACTCTCTCTTGTCTTCTTTGTGCATCACAAGAACAACAAAKNU -5: AGAGGGTAAGAGAAACATAGAAACCTTCCATGTTTGGCAATTTCATCTTGGACTCTCTCTTGTCTTCTTTGTGCATCACAAGAACAACAAATAKNU -10: GAAAGAGGGTAAGAGAAACATAGAAACCTTCCATGTTTGGCAATTTCATCTTGGAAGTCAGCTTGATTCACTCTCTCTTGTCTTCTTTGTGCATCACAAGAACAACAKNU +5: AGAGAAACATAGAAACCTTCCATGTTTGGCAATTTCATCTTGGAAGTCAGTAGCTCTTGATTCACTCTCTCTTGTCTTCTTTGTGCATCACAAGAACAACKNU +10: AAACATAGAAACCTTKNU WT sequence (96 bp), and the modified probes were constructed without deleting the extremity bases, thus the size ranged from 86 bp (−10 probe), 91 bp (−5 probe), 101 bp (+5 probe) to 106 bp (+10 probe) (For EMSAs in 0 probe) .http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The first nucleotide of each read was trimmed with Trimmomatic , devoid of the mitochondrial and the chloroplast genomes, using –X 2000 and –dovetail option. Only the reads reported at a single position (grep –v ‘XS:i:’) with a maximum of two mismatches with the reference genome (grep -e ‘∧@’ -e ‘XM:i:[012][∧0–9]’) were retained. The duplicate reads were removed using the samtools rmdup program.Read quality was analysed with FastQC  with –f BAMPE, -g 120000000 –q 0.0001 –call-summits and the input DNA as control.For each sample, peaks were called using MACS2 (Δtet-AG), consensus peaks between replicates were defined using the Multiple Sample Peak Calling (MSPC) package (cutoff = 10−4) , split into several peaks if needed and the peak widths were then normalized to ± 200 bp around the peak maximum. For all the resulting peaks, a mean normalized coverage was computed as the mean of the normalized read coverage for each replicate.For each DAP experiment  . MSPC usΔtet-AG) were determined according to the following procedure: peaks were considered common if at least 80% of two peaks overlapped with <50% of either peak non-overlapping. Peaks with more than 50% non-overlapping were considered new peaks. These values were chosen empirically based on visual inspection of the peaks in the Integrated Genome Browser (https://www.R-project.org) and the ggplot library  and TFFM were determined on the 600 best peaks (judged according to their averaged coverage). PWM were generated by the meme-suite, using meme-chip  with optet al. (n ≤ Smax) among all dimeric sites in the positive set of bound regions and in the background set. Here, n and Smax, respectively, stand for the considered spacing and the maximal spacing.The computational method to plot intersite spacing was described in Stigliani et al. . The scrN) shown in Figure The normalized enrichment , ing SEP3 . Qualitying SEP3 . Genes wet al.   For each SEP3 or AG-regulated gene, the genic region was defined using 3 kb upstream of the transcription start site and 1 kb downstream of the transcription termination site. A gene was considered bound if a ChIP-seq or a DAP-seq peak overlapped (without restriction). When dealing with genes regulated by SEP3 and AG, the directionality of the regulation was considered and only genes positively or negatively by both regulators were kept Figure .in vitro transcription-translation produced proteins. SEP3-FLAG, SEP3-FLAG/AG-Myc and SEP3Δtet-FLAG/AG-Myc complexes were purified by immunoprecipitation using either anti-FLAG or anti-FLAG and anti-Myc magnetic beads  using PWM and TFFM, a Hidden Markov Model-based TFBS modeling method, which accounts for dinucleotide dependencies Figure  30,38).,38.30,38s Figure  with AURy Figure . In cont dataset . This isΔtet-AG, the tetrameric form had more bound regions and a greater proportion of mapped reads in the peaks than the dimeric form . KNU is important for the termination of the floral meristem and acts by directly repressing the floral meristem maintenance gene, WUSCHEL  and the second site, with two adjacent non-canonical CArG boxes (CTTCTTTGTG and TCTTCTTTGT), to be more weakly bound based on TFFM binding site prediction. We generated DNA fragments with varied spacings between the KNU promoter CArG box binding sites and examined the effect on SEP3–AG and SEP3Δtet-AG interactions by EMSA. SEP3–AG tetramer binding was very sensitive to CArG-box intersite distance, exhibiting a clear preference for 45–46 ± 10 bp spacing, with almost no tetramers bound for 45–46 ± 5 bp spacing .,14.in viith SEP3 . Focusinith SEP3 . This suith SEP3 . The higGene ontology analysis of regions bound in both ChIP-seq and seq-DAP-seq for SEP3–AG and regulated by either AG, SEP or both shows a strong enrichment in genes important for floral meristem determinacy, flower development, organ identity, carpel development and gynoecium development . OverallHECATE1, SHATTERPROOF2, SUPERMAN and SPOROCYTELESS , NAC DOMAIN CONTAINING PROTEIN 98 and ATP-BINDING CASSETTE B11 (ABCB11)  . Thus, tin vivo binding data, particularly with respect to the differences in specificity of complexes containing some of the same partners. For example, determining whorl-specific MADS complex activity requires multiple ChIP-seq and RNA-seq datasets to identify both specific binding sites and putative direct regulatory targets for a unique MADS complex. Seq-DAP-seq has the benefits of a single purified TF complex and direct mapping to genomic DNA, thus providing a powerful tool to determine potential genome-wide binding sites of specific TF complexes as shown here for the SEP3–AG heterotetramer.Selection and discrimination of genomic binding sites by MADS TF complexes is fundamental to understanding how the family is able to regulate diverse processes including floral organ identity programs. The protein–protein interactions of the MADS TF family are characterized by promiscuous heterodimerization and tetramerization patterns. This is particularly true for the SEP clade, which exhibit extensive interactions with other MADS TFs and act as drivers of oligomerization ,49. The MADS TFs bind DNA as obligate dimers, however, the MIKC family of plant MADS TFs are able to tetramerize. How tetramerization may contribute to DNA binding has been difficult to quantify genome-wide, although at specific loci it has been shown to be critical for gene regulation . Here wein vivo and in vitro experiments. Modeling TFBS using the strongest bound regions yields virtually identical DNA-binding motifs, highlighting the limitation in simple models to fully capture the complexity of DNA-binding required for proper target gene regulation. TFBS models that distinguish between TFs of the same family and account for the added specificity due to complex formation are becoming increasingly recognized as important to understanding TFBS syntax . This suggests that seq-DAP-seq is able to capture a large portion of in vivo binding sites. Previous studies have shown that many in vivo bound regions by SEP3 and AG do not contain CArG box-like sites. Indeed, the sites bound in ChIP-seq but not bound in seq-DAP-seq have overall weaker bound sites based on TFFM analysis, suggesting these sites may require or are dependent on additional factors for recognition by the SEP3–AG complex  or seq-DAP-seq (e.g. KNU) specific targets. Further study of seq-DAP-seq specific genes including NAC DOMAIN CONTAINING PROTEIN 98, GMI1 and ABCB11 will be required to determine whether these are bonafide in vivo direct targets of the SEP3–AG complex. However, the known SEP3–AG target, KNU, is present in our seq-DAP-seq data but not bound in SEP3 or AG ChIP-seq data, suggesting that seq-DAP-seq is able to identify additional regulated genes of the SEP3–AG complex. GO term analysis of seq-DAP-seq bound regions and AG regulated genes suggests additional metabolic processes including abscisic acid and secondary metabolite processes may be regulated by the SEP3–AG TF complex and warrant further investigation.In order to identify direct targets and reconstruct GRN, combining expression and binding data is essential. Extensive work cataloging the stage specific and domain specific expression profiles of DE genes during flower and floral organ development has been performed using WT, inducible systems and loss-of-function mutants ,33,50,51Δtet-AG heterodimer. Applying the same protocols to other floral organ MADS complexes will likely reveal unique interspacing and organ specific target genes for the different floral organ quartets. The data presented here helps explain MADS TF binding specificity and demonstrates the importance of tetramerization for DNA-binding and site selection. As many TFs do not act alone but form larger complexes required for DNA binding and gene regulation, the seq-DAP-seq method will enable the exploration of the complicated binding syntax of heteromeric TF complexes in a simplified and elegant in vitro system.The seq-DAP-seq protocol described here provides a powerful tool to interrogate the binding of TF complexes to genomic DNA. By exploiting unique C-terminal epitope tags in the seq-DAP-seq experiments, we were able to investigate unique heteromeric MADS complexes and to unambiguously identify the genomic DNA binding sites specific for each complex. We used this novel methodology to address the question of how complex formation by MADS TFs impacts DNA binding specificity and affinity genome-wide for the SEP3 homocomplex, SEP3–AG heterotetramer and SEP3https://genome.ucsc.edu/s/ArnaudStigliani/MADS. Scripts can be downloaded from https://github.com/Bioinfo-LPCV-RDF/MADS_analysis.The RNA-seq and seq-DAP-seq datasets are available under the GEO accession numbers GSE150605 and GSE150528, respectively, and a session link for the UCSD genome browser is available at gkaa729_Supplemental_FilesClick here for additional data file.


Mesendodermal formation during early gastrulation requires the expression of lineage-specific genes, while the regulatory mechanisms during this process have not yet been fully illustrated. TATA box-binding protein (TBP) and TBP-like factors are general transcription factors responsible for the transcription initiation by recruiting the preinitiation complex to promoter regions. However, the role of TBP family members in the regulation of mesendodermal specification remains largely unknown.We used an in vitro mesendodermal differentiation system of human embryonic stem cells (hESCs), combining with the microarray and quantitative polymerase chain reaction (qRT-PCR) analysis, loss of function and gain of function to determine the function of the TBP family member TBP-related factor 3 (TRF3) during mesendodermal differentiation of hESCs. The chromatin immunoprecipitation (ChIP) and biochemistry analysis were used to determine the binding of TRF3 to the promoter region of key mesendodermal genes.−/−) cells, while the TRF3 deficiency crippled the expression of the mesendodermal markers. The reintroduction of TRF3 into the TRF3−/− hESCs rescued inhibited mesendodermal differentiation. Mechanistically, the TRF3 binding profile was significantly shifted to the mesendodermal specification during mesendodermal differentiation of hESCs based on the ChIP-seq data. Moreover, ChIP and ChIP-qPCR analysis showed that TRF3 was enriched at core promoter regions of mesendodermal developmental genes, EOMESODERMIN, BRACHYURY, mix paired-like homeobox, and GOOSECOID homeobox, during mesendodermal differentiation of hESCs.The mesendodermal differentiation of hESCs was confirmed by the microarray gene expression profile, qRT-PCR, and immunocytochemical staining. The expression of TRF3 mRNA was enhanced during mesendodermal differentiation of hESCs. The TRF3 deficiency did not affect the pluripotent marker expression, alkaline phosphatase activity, and cell cycle distribution of undifferentiated hESCs or the expression of early neuroectodermal genes during neuroectodermal differentiation. During the mesendodermal differentiation, the expression of pluripotency markers decreased in both wild-type and TRF3 knockout (TRF3These results reveal that the TBP family member TRF3 is dispensable in the undifferentiated hESCs and the early neuroectodermal differentiation. However, it directs mesendodermal lineage commitment of hESCs via specifically promoting the transcription of key mesendodermal transcription factors. These findings provide new insights into the function and mechanisms of the TBP family member in hESC early lineage specification. The human embryonic stem cells (hESCs), with the ability of self-renewal and differentiation to derivatives of three germ layers, are a unique model to study human early development , 38, 58.Drosophila melanogaster [Drosophila [Caenorhabditis elegans [Xenopus [Xenopus [In eukaryotes, the transcription initiation is a key step in the control of gene expression, which requires the assistance of a large number of GTFs to form a preinitiation complex (PIC) . Typicalnogaster ; TRF2, bosophila , Caenorh elegans , 35, Xen[Xenopus , zebrafi[Xenopus , chick [[Xenopus , mouse [[Xenopus , and hum[Xenopus , 56; and[Xenopus . TRF3 ha[Xenopus , 33 and [Xenopus , 29. In [Xenopus . TRF3 is[Xenopus . However−/−) and rescue, and molecular analysis approaches, we reported a previously unrecognized role of the vertebrate-specific general transcription factor TRF3 and its global regulatory function in the human ME commitment. Our findings provide new insights into the specific role of the TBP family member during the hESC early lineage commitment and uncover the novel mechanism that “GTFs” can switch the hESC state to the early specific lineage by shifting its binding profile.In the present study, using the in vitro ME differentiation model of hESCs, combining with the TRF3 knockout  was cultured in mTeSR1 media  on Matrigel  coated dishes as previously reported , 31, 32.−/− hESCs were generated using CRISPR/Cas9  technology. The gRNA containing the sequence 5′-ACGTGCTCACGGTCAACGAG-3′ targeting the first exon of TRF3 genome, which was generated with an online tool kit “CHOPCHOP” (http://chopchop.cbu.uib.no/), was constructed into a target vector pSpCas9(BB)-2A-Puro (PX459)  [+/+). Forty-eight hours after nucleofection, puromycin (1 μg/ml) was supplemented to screen the cells containing PX459-gRNA-TRF3 vector. Once the cells amplified into 70–80% confluency, the hESCs were digested into single cells with Accutase  and 1000 cells were re-plated into a 10-cm dish, supplemented with 5 μM rho-associated coiled-coil kinase (ROCK) inhibitor Y-27632 . About 2 weeks later, the single cell was propagated into a clone, which is called single cell-derived clone (single clone). Single clones were picked, and two TRF3−/− clones of hESCs (TRF3−/−-1 and TRF3−/−-2) were confirmed by Sanger sequencing using primers listed in Additional file TRF3Addgene)  and namehttp://chopchop.cbu.uib.no). The primers for Sanger sequencing were listed in Additional file The off-target site was predicted online with an online tool kit “CHOPCHOP”  for 6 h, and then, the media were changed. Forty-eight hours post-transfection, infected cells were selected with puromycin (1 μg/ml) for 2 days to generate the TRF3 overexpression cell lines with TRF3−/−-1 and TRF3−/−-2 hESCs (TRF3−/−-1+3Flag-TRF3 and TRF3−/−-2+3Flag-TRF3) and negative control ones (TRF3−/−-1+vector and TRF3−/−-2+vector).The human TRF3 cDNA  was cloned into pCDH-EF1-3×Flag-MCS-T2A-Puro, modified from pCDH-EF1-MCS-T2A-Puro , and named as pCDH-EF1-3×Flag-TRF3-T2A-Puro. The plasmid was verified by Sanger sequencing, and the vector was served as negative control. The viral package was performed with HEK-293FT cells  after transfection of plasmids  with lipofectamine 2000  following the manufacturer’s instructions. For the viral infection, TRF3Undifferentiated hESCs were stained using Alkaline Phosphatase (ALP) substrate kit III  following the manufacturer’s instructions as previously described . ImmunocThe cells were handled as described previously . BrieflyUndifferentiated hESCs were prepared, fixed with 70% ethanol, and stained with 50 μg/ml PI (propidium iodide) as previously described . Cell cyTotal RNA was extracted with an RNAprep pure Micro Kit  and reverse transcribed with ReverTra Ace reverse transcriptase  following the manufacturer’s instructions. Quantitative PCR was carried out and analyzed by the ViiA™ 7 Real-Time PCR System  with SYBR Green Q-PCR Master Mix . The qRT-PCR primers were listed in Additional file Sample preparation was carried out as reported , 62. In +/+) during the ME differentiation at day 0, day 1, day 2, and day 3  using Rneasy Mini Kit . The RNA samples were reverse transcribed, in vitro transcripted, and fragmented to fragmented and labeled amplified RNA (aRNA) using GeneChip 3′ IVT Express Kit . The biotinylated aRNA was then hybridized to Affymetrix Gene Chip . Twelve raw data files generated from the Affymetrix scanner passed data quality control and were further performed with RNA normalization through the Affymetrix expression console. All chips were normalized using the Robust Multi-array Average method implemented in Partek Genomics Suite 6.5 software. The signature mesendodermal, endoderm, and ectoderm genes were picked up, and the ratio was calculated by normalized signal value at each time point versus the values at day 0. The hierarchical average linkage clustering analysis was performed by using Cluster version 3.Total RNA was extracted from three replicates of wild-type H1 hESCs . Then, the clean reads were mapped to hg19 human reference genome with Bowtie2 software [p value. Annotation of peak sites to gene features and the peak coverage picture plots were performed using the ChIPseeker R package [https://www.broadinstitute.org/igv/). Motif analyses were applied by MEME SUITE [http://www.cytoscape.org/) according to the gene ontology (GO) analysis results.ChIP experiments were carried out with a Simple ChIP™ Enzymatic Chromatin IP Kit  following the manufacturer’s instructions. Protein-DNA complexes were immunoprecipitated with anti-FLAG® M2 affinity gel  or Anti-FLAG M2 Magnetic Beads , and mouse IgG-agarose . The ChIP-qPCR analysis was performed as previously described . Brieflysoftware . Peak casoftware , and pea package . The Big package , and theME SUITE . QuantitME SUITE . FunctioME SUITE . Gene rep < 0.05 was considered statistically significant.The data were presented as mean ± SEM. Statistical significance of differences was estimated by one-way analysis of variance (ANOVA) followed with Bonferroni’s multiple analysis for qRT-PCR data from the ME differentiation samples of hESCs, and by two-way ANOVA followed with Tukey’s multiple comparison for the analysis of other qRT-PCR, Western blot, and ChIP-qPCR data. To explore the potential regulators for the ME differentiation of hESCs, the hESCs were induced into the ME differentiation as reported recently  followin−/−-1 and TRF3−/−-2) from wild-type H1 hESCs (TRF3+/+) by CRISPR/Cas9  had different indels. The TRF3−/−-1 cell line had 1 “T” insertion while the TRF3−/−-2 cell line had 23 bp and 4 bp deletions at each allele   Fig. c, d; the−/− hESCs was similar as that of the TRF3+/+ control hESCs  and the early ME stage (differentiation day 1). As shown in Fig. SCs Fig. ” sectionEOMES, T, MIXL1, and GSC. Genome browser screenshots of ChIP-seq showed the enhancement of TRF3 on the promoter region of EOMES, T, and MIXL1 at ME D1 compared with those at the undifferentiated status (ME D0) , TRF3 showed low and unchanged enrichment of TRF3 at ME D1 versus that at ME D0  Fig. b. Interentiation , 41, 57.−/− cells  the depletion of TRF3 does not affect the hESC self-renewal; (ii) during the mesendodermal differentiation, TRF3 deficiency suppresses the mesendodermal differentiation but does not affect the neuroectodermal differentiation of hESCs; (iii) the global binding profile of TRF3 shifts from the genes mediating hESC maintenance and growth to the genes determining cell fate during the ME specification; and (iv) TRF3 directly binds to the core promoters of key ME signature genes, −/− cells does not affect the expression of self-renewal and pluripotent markers  most of the reads locate in the proximal promoter region (promoter ≤ 1 kb) Fig. b, which EOMES, T, and MIXL1 locus, the genome browser screenshots reveal that TRF3 can bind to the first exon and one intron at ME D0 and shift to the promoter and exon region of GSC locus at ME D1  and cis-acting elements (DNA sequences) . The exp−/− cells. Although the Sanger sequencing and ME differentiation analysis of TRF3 knockout and rescue experiments confirm the deficiency of TRF3 and its crucial role in promoting the expression of ME lineage genes, it would be better to confirm the protein level of TRF3 in the TRF3−/− hESCs. Considering CRISPR/Cas9 has been used in the precise knockin in mouse embryos and hPSCs [As the lack of qualified TRF3 antibodies, we could not validate the absence of TRF3 protein in the TRF3nd hPSCs , buildinT, EOMES, MIXL1, and GSC) to initiate ME differentiation of hESCs. These findings uncover a unique role of TRF3 in the early lineage fate decision of hESCs and a novel mechanism of the global gene regulatory network shift directed by a general transcription factor. These findings, together with other reports [Our study reveals a novel mechanism of human ME lineage commitment, i.e., the general transcription factor TRF3 specifically recognizes the TATA box within the promoter regions of ME genes, especially the four key transcription factors : the expression of pluripotency markers  during the ME differentiation. n = 3 each. *p < 0.05, **p < 0.01, ***p < 0.001 as indicated. (b): the expression of neuroectodermal markers  during the ME differentiation. n = 3 each. *p < 0.05, compared with the corresponding values in undifferentiated cells. n.s., no significant difference compared with the corresponding values in undifferentiated cells. (c): qRT-PCR analysis of TRF3 during the neuroectodermal differentiation process of TRF3+/+ hESCs. (d): qRT-PCR analysis of pluripotency markers . n = 3 each. (e): qRT-PCR analysis of neuroectodermal markers . n = 3 each. Data are presented as mean ± S.E.M.Additional file 6: Figure S3. Reintroduction of TRF3 into TRF3-/--1 and TRF3-/--2 hESCs do not affect the self-renewal and the expression of ME genes in undifferentiated status. Reintroduction of TRF3 into TRF3-/-- hESCs does not affect the self-renewal and the expression of ME genes in the undifferentiated status. (a): Cell morphology and ALP activity of TRF3-/--1, TRF3-/--1+Vector, TRF3-/--1+3Flag-TRF3, TRF3-/--2, TRF3-/--2+Vector, TRF3-/--2+3Flag-TRF3 cells. Scale bar = 100 μm. (b): qRT-PCR analysis of pluripotency markers (OCT4 and NANOG) in TRF3+/+, TRF3-/--1+Vector, TRF3-/--1+3Flag-TRF3, TRF3-/--2+Vector, TRF3-/--2+3Flag-TRF3 hESCs. n = 3 each. (c): qRT-PCR analysis of ME genes  in TRF3+/+, TRF3-/--1+Vector, TRF3-/--1+3Flag-TRF3, TRF3-/--2+Vector, TRF3-/--2+3Flag-TRF3 hESCs. n = 3 each.Additional file 7: Figure S4. ChIP analysis of TRF3 at undifferentiated status (ME D0) and ME D1. (a): The mean peak counting frequency flanking TSS in the undifferentiated status (ME D0) and ME D1. (b): Genome browser screenshots of ChIP-seq for pluripotency markers (OCT4 and NANOG) and neuroectodermal genes  in the undifferentiated status (ME D0) and ME D1.Additional file 8: Figure S5. Amino acid sequence analysis of TRF3 proteins among human, mouse and zebrafish. (a): Amino acid sequence analysis of TRF3 protein. (b): Sequence conservation analysis of TRF3.


Vibrio comprises protease-producing bacteria. Proteases transform polypeptides into shorter chains for easier utilization. They also function as a virulence factor in pathogens. The mechanism by which protease genes are regulated in Vibrio parahaemolyticus, an emerging world-wide human pathogen, however, still remains unclear. Quorum sensing is the communication system of bacteria. OpaR is the master quorum-sensing regulator in V. parahaemolyticus. In the present study, quantitative reverse transcriptase-polymerase chain reaction and protease gene promoter-fusion reporter assays revealed that OpaR represses seven protease genes—three metalloprotease genes and four serine protease genes—which are involved in environmental survival and bacterial virulence. Furthermore, the electrophoresis mobility shift assay demonstrated that OpaR is bound directly to the promoter region of each of the seven protease genes. DNase I footprinting identified the sequence of these OpaR-binding sites. ChIP-seq analyses revealed 435 and 835 OpaR-binding sites in the late-log and stationary phases, respectively. These OpaR-binding sequences indicated a conserved OpaR-binding motif: TATTGATAAAATTATCAATA. These results advance our understanding of the protease regulation system in V. parahaemolyticus. This study is the first to reveal the OpaR motif within V. parahaemolyticus in vivo, using ChIP-seq, and to provide a database for OpaR direct regulon.Proteases play a key role in numerous bacterial physiological events. Microbial proteases are used in the pharmaceutical industry and in biomedical applications. The genus  Vibrio parahaemolyticus is a gram-negative bacterium that inhabits marine and estuarine environments. Most cases of seafood-borne gastroenteritis are caused by V. parahaemolyticus  , TDH-related hemolysin (TRH), adhesion factors, extracellular proteases, and type III secretion system effectors  . V. paraffectors . Two typswarming .Vibrio parahaemolyticus expresses extracellular metalloproteases—PrtV, VppC, and VPM—and extracellular serine proteases—VPP1, VpS37, and PrtA  is a cell–cell communication system in which bacteria use the production and detection of extracellular chemicals, known as autoinducers, to monitor cell population density ; moreoveolyticus . In low olyticus , whereasolyticus . OpaR anolyticus .Vibrio spp. Hemagglutinin/protease (HAP) is a well-known virulence factor of V. cholerae , which is a tdh– and trh– strain. We present a conserved OpaR-binding motif and propose that OpaR directly represses seven protease genes of V. parahaemolyticus.Nutrient intake is an essential part of the bacterial life cycle—pathogens ingest nutrient molecules from host cells. A certain minimum level of protease is indispensable for the survival and growth of organisms; however, our understanding of the regulation of protease genes in Vibrio parahaemolyticus can be found in multiple cell types, including free-swimming forms and colony formations attached to inert surfaces such as shellfish  and stationary (OD600 4) phases to perform ChIP-seq assays. FLAG-OpaR was bound to the genome and cross-linked by anti-FLAG beads; the genome was sheared in 300-bp fragments, and immunoprecipitated DNA was purified and sequenced. Reads were obtained from independent ChIP-seq assays in late-log and stationary phases. The input control of ChIP-seq was the VP93 genome without anti-FLAG antibody enrichment. All reads were mapped to the VP93 genome. Our ChIP-seq data  revealed the presence of 435 and 835 OpaR-binding sites in late-log and stationary phases, respectively; there were 432 overlapping OpaR-binding sites between the two phases , and aphA by OpaR has been reported in V. parahaemolyticus   . From geely LuxR  and HapRely LuxR . We obseely LuxR —indicateserine protease and degS. OpaR-binding peaks appeared on the upstream of lytM, mcp02, M6 protease, and protease II; however, one OpaR-binding peak presented at the terminator region and overlaid the translational stop site of the periplasmic protease. Predicted OpaR-binding sites in the promoters of lytM, mcp02, M6 protease, serine protease, and degS were found not only in the late-log phase but also in the stationary phase, whereas the OpaR occupancy in the promoter of protease II and terminator of periplasmic protease was only found in the stationary phase. Normalized reads per million (RPM) values [total reads/mapped reads (millions) x region length (bp)] represent the binding strength of OpaR-binding peaks. In addition, the binding length of OpaR ranged from 177 bp (degS) to 372 bp (lytM) (The distribution of OpaR-binding peaks relative to the seven protease genes are shown in p (lytM) .600 at every growth stage , Mcp02 , M6 protease , Serine protease , DegS , Protease II , and Periplasmic protease  in the late-log phase (p = 0.0060) and DegS  (We used qRT-PCR to measure the relative transcript levels of seven protease genes in wild-type VP93 and mutant og phase . Moreove 0.0018) .luxAB reporter assay was conducted. We amplified the aforementioned putative OpaR-binding sequences of each protease gene and cloned them into the promoter-less vector pSAluxAB. Bioluminescence was measured for the wild-type VP93 and the mutant ΔopaR strains at late-log and stationary phases. The results revealed that OpaR represses the bioluminescence expression for protease genes through OpaR-binding sites in the late-log phase, including lytM , mcp02 , m6 protease , serine protease , degS , protease II , and periplasmic protease  , protease II , and periplasmic protease  were regulated by OpaR in the stationary phase (To determine the role of these OpaR-occupied positions of protease genes from  0.0094) . Howeverry phase .lytM, m6 protease, and protease II promoter regions  was performed to evaluate the potential of the direct binding of OpaR to the promoters of the protease genes. We amplified the DNA fragments as the probes (P1 and P2) for each target gene, and the positions of EMSA probes are listed in  regions , and onl regions . Indeed,romoters . These b of OpaR . OpaR dilytM, m6 protease, protease II, degS  is an extracellular serine protease that was promoted by OpaR, as reported in a previous study . As showprotease , serine tease II , 1992—anII  (Difco) at 35°C. Escherichia coli strains were grown in Luria–Bertani (Difco) medium at 35°C. XL1-Blue  was inserted after the translational start site of e vector , to yieljugation . The conFLAG-OpaR/VP93 strain culture was collected at every growth stage by centrifugation. Total proteins of the FLAG-OpaR/VP93 strain were separated using 12.5% SDS-PAGE gels and transferred to PVDF membranes . The membrane was blotted with anti-FLAG antibody as the primary antibody. The secondary antibody is an anti-rabbit IgG HRP-linked antibody. Immobilon Western Chemiluminescent Horseradish Peroxidase Substrate  was added, and signals were detected using a chemiluminescence image system, MutiGel-21 .600 3) and stationary (OD600 4.2) phases. Next, the bacterial culture was incubated with 1% formaldehyde for 20 min at 25°C, and cross-linking was ceased using 0.5 M glycine. Bacterial cells were then harvested by centrifugation, washed three times with Tris-buffered saline buffer, and resuspended in a lysis buffer  containing a protease inhibitor cocktail (Sigma). The lysed cells were sonicated using Bioruptor Pico® (Diagenode) for 20 cycles. Cell debris was removed by centrifugation; supernatant DNA fragments ranged from 300 bp to 500 bp. Anti-FLAG antibody beads (Sigma) were incubated with supernatant DNA fragments overnight at 4°C. After the ChIP reaction, samples were washed five times with a radioimmunoprecipitation assay buffer  and twice with Tris-buffered saline buffer. ChIP DNA was eluted in an elution buffer  at 65°C for 30 min to de-cross-link. Samples were treated with RNase A and proteinase K  (E. coli BL21 (DE3) to express OpaR with an N-terminal fusion tag. E. coli BL21 (DE3) with pET28a–opaR was grown in 200 mL Luria–Bertani broth supplemented with 25 μg mL–1 kanamycin at 37°C. When OD600 reached 0.4–0.6, 1 mM isopropyl β-D-1-thiogalactopyranoside was used to induce His6-OpaR protein expression at 30°C for 4 h. The cells were then collected and disrupted using the Constant Cell Disrupter System , and nickel–nitrilotriacetic acid chromatography  was used to purify His6-OpaR. His6-OpaR purity was confirmed using sodium dodecyl sulfate polyacrylamide gel electrophoresis. Proteins were quantified using the method described by Bradford (Hisescribed . BrieflyNovagen) . The cloBradford .ΔopaR. Cells were mixed with RNAprotect Bacteria Reagent (Qiagen) and centrifuged at 5000 g at 4°C for 15 min. Total RNA was isolated using TRIzol. Reverse transcription–PCR (RT-PCR) was performed using HiScript I Reverse Transcriptase (Bionovas Biotechnology), according to the manufacturer’s instructions, and real-time quantitative reverse-transcriptase (qRT-PCR) was performed using SYBR Green Master Mix  on a BioRad CFX96 system. Relative expression values were determined using 2–(ΔCt Target–ΔCt Reference). Ct is the fractional threshold cycle, and the reference was 16S ribosomal RNA gene. The specific primer sets used for qRT-PCR are listed in We collected the bacterial cultures from different growth phases of VP93 and periplasmic protease were amplified using specific primer sets (SmaI and XbaI restriction sites. These plasmids were transformed into VP93 and ΔopaR by using the Gene Pulser Xcell Electroporation System (BioRad). Luciferase activity was measured by adding n-decanol [final concentration 0.001% (vol/vol)] as the substrate to 2 mL bacterial cultures. Luminescence was detected on a Spectrofluorometry F-2500 device  and is reported as specific light units .The promoter regions of the six OpaR-regulated protease genes and one 3’ end region of mer sets . The promer sets , with Sm4)2SO4, 5 mM dithiothreitol, 1% (wt/vol) Tween 20, and 150 mM KCl] at 25°C for 30 min. In competition analyses, the protein–DNA complex was mixed with 100 ng unlabeled fragment. These samples were separated using 6% native polyacrylamide gel electrophoresis in 0.5 × Tris-borate-EDTA buffer and transferred by electroblotting to a positively charged nylon membrane. Finally, immunological treatment and chemiluminescent signal detection were performed according to the instructions for the second generation DIG Gel Shift Kit .The specific primer sets used to amplify EMSA probes are listed in TM LIZ 600 Size Standard (Applied Biosystems). The samples were analyzed using the ABI 3730xl DNA Analyzer (Applied Biosystems), and the results were analyzed using Peak Scanner Software v1.0 (Applied Biosystems).The DNase I footprinting assay was performed using the method of t-test with a two-tailed distribution. P < 0.05 or P < 0.01 indicated statistical significance.Statistical differences between the three samples were measured using Student’s GSE122479).The ChIP-seq data have been submitted to the National Center for Biotechnology Information’s Gene Expression Omnibus (GEO) database (accession number: S-CC and C-YL designed the study. S-CC performed the experiments and data analyses. C-YL supervised and coordinated the project. S-CC wrote the preliminary draft of this manuscript and C-YL revised the manuscript. Both authors have reviewed and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Although considerable progress has been made towards annotating the noncoding portion of the human and mouse genomes, regulatory elements in other species, such as livestock, remain poorly characterized. This lack of functional annotation poses a substantial roadblock to agricultural research and diminishes the value of these species as model organisms. As active regulatory elements are typically characterized by chromatin accessibility, we implemented the Assay for Transposase Accessible Chromatin (ATAC-seq) to annotate and characterize regulatory elements in pigs and cattle, given a set of eight adult tissues.Overall, 306,304 and 273,594 active regulatory elements were identified in pig and cattle, respectively. 71,478 porcine and 47,454 bovine regulatory elements were highly tissue-specific and were correspondingly enriched for binding motifs of known tissue-specific transcription factors. However, in every tissue the most prevalent accessible motif corresponded to the insulator CTCF, suggesting pervasive involvement in 3-D chromatin organization. Taking advantage of a similar dataset in mouse, open chromatin in pig, cattle, and mice were compared, revealing that the conservation of regulatory elements, in terms of sequence identity and accessibility, was consistent with evolutionary distance; whereas pig and cattle shared about 20% of accessible sites, mice and ungulates only had about 10% of accessible sites in common. Furthermore, conservation of accessibility was more prevalent at promoters than at intergenic regions.The lack of conserved accessibility at distal elements is consistent with rapid evolution of enhancers, and further emphasizes the need to annotate regulatory elements in individual species, rather than inferring elements based on homology. This atlas of chromatin accessibility in cattle and pig constitutes a substantial step towards annotating livestock genomes and dissecting the regulatory link between genome and phenome. Despite considerable progress to annotate protein-coding genes in livestock species, the vast majority of these genomes is noncoding and remains poorly characterized. Epigenomics techniques, such as chromatin immunoprecipitation followed by sequencing (ChIP-seq) and DNase I hypersensitive sites sequencing (DNase-seq), have been extensively employed to catalog functional elements in humans  and clasUltimately, genome-wide patterns of chromatin accessibility and compaction determine which genomic regions are available to cellular machinery, and are thereby intimately connected to the cell-specific gene expression patterns that determine identity and function. Controlled exposure of specific sites provides opportunities for transcription factors to bind their recognition motifs and regulate gene expression through further recruitment of proteins, such as RNA polymerases , 23. ConHere we implemented ATAC-seq to profile open chromatin in a set of cattle and pig tissues: subcutaneous adipose, brain , liver, lung, skeletal muscle, and spleen. This set of prioritized tissues are associated with a large number of qualitative phenotypic traits relevant to animal production, such as disease resistance, growth, and feed efficiency. Overall, about 300,000 accessible regions were identified in each species, yielding an epigenomic resource that will benefit agricultural genomics research and enable cross-species comparisons that will enhance knowledge of comparative epigenomics and transcriptional regulation.Using a modified ATAC-seq protocol, genome-wide chromatin accessibility was profiled in eight tissues derived from two adult male Hereford cattle and two adult male Yorkshire pigs: three brain tissues , liver, lung, spleen, subcutaneous adipose, and skeletal muscle Fig. a. With tSTMN4, which is involved in neuron projection development, is specifically marked by open chromatin at two sites in both cattle  informative reads  per sample Table . Normalitle Fig. d and pigtle Fig. e in all Several commonly used statistics were used to evaluate library quality Table  25, 26], 2625, 2For each tissue, peaks called from biological replicates were compared to evaluate consistency between replicates and identify accessible regions with high confidence. On average, 67.4 ± 17.8% (S.E.) of peaks from the replicate with fewer peaks called were also identified in the other replicate Table . Regionsp-value <1e-200), although most consensus peaks were intronic or intergenic , sequence content, and tissue-specificity. In cattle, consensus peaks averaged 616 bp in width Table  and coveA comparable mouse ATAC-seq dataset, which included libraries from two male replicates for all tissues except hypothalamus, was downloaded from the CNGB Nucleotide Sequence Read Archive (Project ID CNP0000198) and processed in the same manner as the pig and cattle data. Similar to cattle and pig, 254,076 consensus peaks were identified from mouse tissues  were only present in a single tissue Fig. b, suggesTo stringently identify open chromatin that was specific to a given tissue, only consensus peaks that did not overlap any peaks called from either replicate in any other tissue were considered tissue-specific. In sum, 71,479 peaks in pig, 47,454 peaks in cattle, and 116,700 peaks in mouse Fig. a were idp-value <1e-200)  Fig. a, tissue00) Fig. c. Althou00) Fig. d. HomeobAlthough extensive epigenetic divergence is expected between species, sequence similarity among cattle, pig, and mouse genomes well above the coding sequence fraction suggests that a significant portion of epigenetic control of transcription is likely under evolutionary constraint. Having observed similarities in motif enrichment in tissue-specific open chromatin between species, it was suspected that the sequence and accessibility of regulatory elements would also be constrained.Reasonably, portions of regulatory elements, such as the motifs that facilitate TF-DNA contacts, would be under selective pressure, especially those that connect tissue-specific transcription factors to conserved tissue-specific expression programs. To identify homologous regions that correspond to regulatory elements, the coordinates of consensus ATAC-seq peaks from each species were projected to the other two species using Ensembl Compara , which iAmong the consensus peaks identified in cattle, pig and mouse, 145,801 regions could be mapped to all three species. Of these, 13,735 were consistently accessible in the same tissue (at least one) in all three species, and 30,215 were accessible in at least one tissue in both cattle and pig  and were predominantly intronic and intergenic Fig. Se. For inies Fig. d. These ies Fig. .FOXG1 locus appear to be syntenically conserved across all three species; however, only one region – corresponding to the TSS of a human long non-coding RNA – could be mapped to all three species . Minced tissue was twice homogenized using the gentleMACS dissociator “E.01c Tube” program. Homogenate was filtered with the 100 μm Steriflip vacuum filter system, volume was brought up to 9.9 mL with sucrose buffer, and 1.1 mL DMSO was added to achieve a 10% final concentration. Preparations were aliquoted into cryovials and frozen at − 80 °C overnight in Nalgene Cryo 1 °C freezing containers, then stored at − 80 °C long-term.All necessary permissions were obtained for collection of tissues relevant to this study, following the Protocol for Animal Care and Use #18464, as per the University of California Davis Animal Care and Use Committee (IACUC). As described previously , two int2, 0.1% (v/v) IGEPAL CA-630), and centrifuged for 10 min at 500 rcf and 4 °C. Pellets were then resuspended again in ice-cold PBS for cell counting on a hemocytometer. Between 50,000 and 1,000,000 cells were aliquoted for library preparation, depending on tissue, success of previous library preparation attempts, and cell abundance in a given preparation ). Nuclear pellets were incubated with transposition mix for 60 min at 37 °C, shaking at 300 rpm. Transposed DNA was purified with the MinElute PCR Purification Kit  and eluted in 10 μL Buffer EB. Eluted DNA was added to 40 μL PCR master mix , a wrapper around Cutadapt (v1.12) , with thFor all reported statistics, standard error is also reported. Quality metrics were calculated as follows. The non-redundant read fraction (NRF) was calculated by dividing the number of nonduplicate uniquely mapping reads out of all mapped reads. The Fraction of Reads in Peaks (FRiP) score for each sample was calculated using the plotEnrichment function from the deepTools suite (v.3.2.0), given the broad peaks identified by MACS2. Finally, the synthetic Jensen-Shannon distance (sJSD) was calculated using the deepTools plotFingerprint function.For visualization, genome-wide ATAC-seq signal was normalized by RPKM in 50 bp windows using the bamCoverage function from the deepTools suite. Other deepTools functions were used along with the resulting bigwig files to generate  pairwiseTo obtain a single comprehensive set of consensus ATAC-seq peaks for each species, regions that were identified as peaks in both biological replicates of a tissue were first identified, and then collapsed. Specifically, for each tissue peaks from biological replicates were compared with BEDtools intersect v2.26.0)  to ident.26.0  toPeaks were categorized by position relative to features in the Ensembl annotations (v96) for each species using BEDtools intersect with default settings. Because many peaks overlap multiple features, peaks were first classified as TSS (within 50 bp), then as promoters (within 2 kb upstream of TSS), as transcription termination sites , as overlapping a 5′ untranslated region (UTR), as overlapping a 3′ UTR, as exonic, as intronic, and finally, if peaks did not overlap any of these features, they were considered to be intergenic.To determine if peaks were enriched near genomic features, peak locations were iteratively randomized 100 times using BEDtools shuffle, excluding “unmappable” genomic regions to avoid bias. Unmappable regions were empirically defined as any 500 bp window to which no read mapped in any library. Randomized peaks were also categorized by position relative to gene annotations, and compared to the localization of the actual peak set using a one-sample T-test (one-tailed).p-values, were reported.Regions of interest were evaluated for motif enrichment using the HOMER findMotifs.pl function (v4.8) , and theAll interspecies comparisons were based on the 46-mammalian Enredo-Pecan-Ortheus (EPO) multiple sequence alignment (MSA) available through Ensembl Compara (v99) . RegionsMus musculus was used as background, and functional annotation clustering was conducted on medium stringency for the following terms: GOTERM_BP_5, GOTERM_CC_DIRECT, GOTERM_MF_DIRECT, BIOCARTA, and KEGG_PATHWAY. For each gene set, the top four clusters were reported.Ensembl IDs were converted to external gene names using the BiomaRt package, and these were submitted to DAVID (v6.8) , 70 for Additional file 1 Figure S1. Correlation of ATAC-seq signal in select technical replicates ATAC-seqlibraries. A) Pearson correlation of genome-wide signal (RPKM) in 500 bp windows. B) PCA of Cortex A technical replicate libraries alongside all biological replicates. C) PCA of pig technical replicate libraries alongside all biological replicates. D) Signal of cattle cortex technical and biological replicates at the STMN4 locus. E) Signal of pig cerebellum and hypothalamus technical and biological replicates at the STMN4 locus. Figure S2. Correlation of ATAC-seq signal in biological replicates. Scatterplots showing Pearson correlation of normalized genome-wide signal in 500 bp windows between biological replicates for cattle and pig tissues. Figure S3. ATAC-seq signal at TSS. Heatmaps depicting normalized ATAC-seq signal in the proximity of TSS, including 2 kb upstream and downstream, with TSS sorted by signal intensity. Figure S4. PCA of normalized ATAC-seq signal in consensus open chromatin identified in pig and cattle tissues tissues. Principal components 1, 2 and 3 are included to better visualize clustering of tissues. Figure S5. Mouse open chromatin localization and differential accessibility. A) Distribution of mouse consensus open chromatin relative to the Ensembl gene annotation (v96). B) Distribution of consensus peak activity, ranging from tissue-specific (accessible in only one tissue) to ubiquitous . Consensus peaks that were accessible in a single tissue were further broken down by tissue. Figure S6. Conservation of open chromatin in individual tissues. Titles above bar plots indicate the species that consensus peaks were identified in, followed by the species to which the consensus peak coordinates were projected to evaluate accessibility conservation in the corresponding tissue. Figure S7. Characterization of conserved open chromatin. Proportion of all consensus peaks, promoter consensus peaks (within 2 kb upstream and 50 bp downstream of TSS), and intergenic consensus peaks that were identified in (A) cattle or (B) pig that demonstrated both conserved sequence and accessibility in the other two species. Number of tissues in which consensus peaks demonstrated conserved accessibility in (C) all three species or (D) only in cattle and pig. E) Distribution of consensus peaks with conserved accessibility in cattle, pig, and mouse, relative to the mouse gene annotation (Ensembl v96). Figure S8. Positional conservation of chromatin accessibility at the FOXG1 locus. For each species, consensus peaks, consensus peaks with conserved sequence , and consensus peaks with conserved accessibility are shown. Tracks show normalized ATAC-seq signal for each sample. Conserved promoter open chromatin is highlighted in green. Consensus peaks that appear to be syntenically conserved, relative to FOXG1, but which could not be mapped between species, are highlighted in grey. Figure S9. Bioanalyzer traces of cattle ATAC-seq libraries prior to size selection. Bioanalyzer traces were used to check for nucleosomal laddering. Size selection removed excess primer and fragments > 250 bp. Figure S10. Bioanalyzer traces of pig ATAC-seq libraries prior to size selection. Bioanalyzer traces were used to check for nucleosomal laddering. Size selection removed excess primer and fragments > 250 bp. Table S1. ATAC-seq library construction details. For each library constructed, rounds of PCR amplification, number of cells used as input, and concentration in the 150–250 bp range prior to size selection are indicated. Table S2. Functional annotation clustering of genes with conserved and global TSS accessibility. Genes with accessible TSS (± 50 bp) in all profiled tissues in all species were subjected to functional annotation clustering to identify enriched cellular functions. Top four clusters reported. Table S3. Functional annotation clustering of genes near conserved intergenic open chromatin. Genes that were closest (within 100 kb) to intergenic open chromatin that was conserved in all three species were subjected to functional annotation clustering to identify enriched cellular functions. Top four clusters reported. Table S4. ATAC-seq oligos used for PCR. Sequences have been previously described by Buenrostro et al., 2013. Primers 2A-2X contain variable barcodes which permit library pooling prior to sequencing, and which were used to demultiplex sequencing data.Additional file 2. Cattle ATAC-seq peaks. Genomic locations of ATAC-seq peaks that were called for each tissue in each replicate.Additional file 3. Pig ATAC-seq peaks. Genomic locations of ATAC-seq peaks that were called for each tissue in each replicate.


While footprinting analysis of ATAC-seq data can theoretically enable investigation of transcription factor (TF) binding, the lack of a computational tool able to conduct different levels of footprinting analysis has so-far hindered the widespread application of this method. Here we present TOBIAS, a comprehensive, accurate, and fast footprinting framework enabling genome-wide investigation of TF binding dynamics for hundreds of TFs simultaneously. We validate TOBIAS using paired ATAC-seq and ChIP-seq data, and find that TOBIAS outperforms existing methods for bias correction and footprinting. As a proof-of-concept, we illustrate how TOBIAS can unveil complex TF dynamics during zygotic genome activation in both humans and mice, and propose how zygotic Dux activates cascades of TFs, binds to repeat elements and induces expression of novel genetic elements. Footprinting analysis allows genome-wide investigation of transcription factor (TF) binding on chromatin. Here the authors developed a framework termed TOBIAS aimed at identifying footprints of chromatin-associated proteins from ATAC-seq accessibility profiles and apply it to zygotic development datasets. While ATAC-seq can uncover accessible regions where TFs might bind, reliable identification of specific TF binding sites (TFBS) still relies on chromatin immunoprecipitation methods such as ChIP-seq. However, ChIP-seq methods require high input cell numbers, are limited to one TF per assay, and are further restricted to TFs for which antibodies are readily available. Therefore, it remains costly, or even impossible, to study the binding of multiple TFs in parallel.Epigenetic mechanisms governing chromatin organization and transcription factor (TF) binding are critical components of transcriptional regulation and cellular transitions. In recent years, rapid improvements of pioneering sequencing methods such as ATAC-seq (Assay of Transposase Accessible Chromatin)2, Zscan43, and other homeobox-containing TFs4. However, due to the limitations of ChIP-seq, the exact genetic elements bound and regulated by different TFs during PD remain to be fully discovered. Consequently, the global network of TF binding dynamics throughout PD remains mostly obscure.Current limits to the investigation of TF binding become particularly apparent when investigating processes involving a very limited number of cells, such as preimplantation development (PD) and zygotic genome activation (ZGA) of early zygotes. Integration of multiple omics-based profiling methods have revealed a set of key TFs that are expressed at the onset of and during ZGA including Dux5 has emerged as an alternative means, which can overcome some of the limitations of ChIP-based methods. DGF is a computational analysis of chromatin accessibility assays such as ATAC-seq, which employs DNA effector enzymes that only cut accessible DNA regions. Similarly to nucleosomes, bound TFs hinder cleavage of DNA, resulting in defined regions of decreased signal strength within larger regions of high signal—known as footprints6 ts6 Fig. .Fig. 1Th8. The influence of Tn5 transposase bias in the context of ATAC-seq footprinting has, however, only been described very recently10 and still represents an uncertainty during discovery of true footprints. Besides the identification of footprints, comparing footprints across biological conditions remains challenging as well. While there have been efforts to estimate differential TF binding on a genome-wide scale12, investigation of epigenetic processes often requires more in-depth information on the individual differentially bound TFBS and genes targeted by these TFs. Furthermore, many footprinting methods suffer from performance issues due to missing support for multiprocessing, inflexible software architecture, and the use of non-standard file-formats. These obstacles complicate the assembly of different tools for advanced analysis workflows. Consequently, despite its compelling potential, these issues have rendered footprinting on ATAC-seq cumbersome to apply to biological questions. Essentially, a comprehensive framework enabling large-scale ATAC-seq footprinting is missing.Surprisingly, although this concept shows considerable potential to survey genome-wide binding of multiple TFs in parallel from a single experiment, DGF analysis is rarely applied when investigating TF binding mechanisms. The skepticism towards DGF has been driven by the discovery that enzymes used in chromatin accessibility assays  are biased towards certain sequence compositions, an effect which has been well characterized for DNase-seqHere, we describe TOBIAS , a comprehensive computational framework that we created for footprinting analysis Fig. . TOBIAS To validate the results of the TOBIAS method, we utilized 217 paired ChIP-seq/ATAC-seq datasets across four different cell types . Here, the ChIP-seq peaks represent the true binding sites for each TF, which we used for validating the accuracy of the binding sites predicted by footprinting  utilizes a dinucleotide weight matrix (DWM)13 to estimate the background bias of the Tn5 transposase , we collected four popular tools for ATAC-seq footprinting  and compared these to the individual TOBIAS framework features where applicable. While we found that some functionalities are overlapping between tools, we found a substantial set of features, such as differential footprinting for more than two conditions, to be exclusively covered by TOBIAS  displayed highest activity at the 2–4C stage and strongly decreased thereafter, suggesting that factors within these clusters are likely involved in ZGA initiation. We set out to classify these TFs, and observed a high overlap with known maternally transferred transcripts17 , explaining their activity before ZGA onset. Importantly, DUX4 and DUXA, which are amongst the earliest expressed TFs during ZGA18, were also contained in these clusters. Additional TFs included HOXD1, which is known to be expressed in human unfertilized oocytes and preimplantation embryos19 and ZBTB17, a TF mandatory to generate viable embryos20. Cluster 6 (n = 67) displayed a particularly prominent 8C specific signature, that harbored well-known TFs involved in lineage specification such as PITX1, PITX3, SOX8, MEF2A, MEF2D, OTX2, PAX5, and NKX3.2. Furthermore, overlapping TFs within Cluster 6 with RNA expression datasets ranging from the germinal vesicle to cleavage stage2, 12 additional TFs  were confirmed to be exclusively expressed within this time frame. Taken together, these data show that TOBIAS reliably uncovers massively parallel TF binding dynamics at specific timepoints during early embryonic development.Two clusters of TFs . In contrast to the expression values, TOBIAS predicted SALL4 to have the highest activity in 2C, with decreasing activity in 8C, which is in line with the presence of maternal SALL4 in the zygote. Comparing this change to all TF changes between 2C and 8C (log2 fold-changes estimated from TOBIAS activity scores), we find that SALL4 is at the 7th percentile of all changes ranked from decreasing to increasing, which is consistent with the degradation of the protein after the 2C stage. These data show that TOBIAS can provide significant insight into TF activities, in particular for those where determining their expression patterns alone does not suffice to explain when they exert their biological function.For example, within the non-correlated cluster 13 TFs were identified, which are of putative maternal origin23). By integrating the TOBIAS scores from human and mouse  and humans (4C–8C)  and other POU-factors.Next, we wanted to investigate specific differentially bound TFs, not only across the whole timeline, but also between individual conditions. We therefore utilized the differential TF binding plots created by TOBIAS, and chose to focus on the cellular transition initiated at and following ZGA, which corresponds to the transition between 2C and 4C in mice . Conceivably, this observation, together with the finding that murine Obox factors share the same motif as RHOX-factors in humans, suggests that Obox TFs might function similarly to RHOX-factors during ZGA. Altogether, the TOBIAS-mediated TF clustering based on TFBS overlap allows for quantification of target-similarity and divergence of TF function between motif families.Throughout the pairwise comparisons, we observed that TFs from the same families often display similar binding kinetics within species, which is not surprising since they often possess highly similar binding motifs Fig. ; right. 2, there is still a poor understanding of how Dux regulates its primary downstream targets, and consequently its secondary targets, during this process. We therefore applied TOBIAS to identify Dux binding sites utilizing an ATAC-seq dataset of Dux overexpression (DuxOE) in mESC2.Throughout the investigations of human and mouse development we became particularly interested in the Dux/DUX4 TF, which TOBIAS predicted to be one of the earliest factors to be active in both organisms Fig. . InteresDuxOE, indicating that Dux renders previously accessible chromatin sites associated with pluripotency inaccessible.As expected, the differential TF activity predicted by TOBIAS showed an increase in activity of Dux and Obox TFs, as well as Hltf, which was already highlighted to be common between mouse and human ZGA Fig. . InteresDuxOE. In comparison to existing bias correction methods, we found TOBIAS to be better at uncovering this footprint between Control and DuxOE conditions . Consistently, Tdpoz1 expression levels are highest at 2C and decrease thereafter, indicating that Tdpoz1 is likely a direct target of Dux during PD both in vitro and in vivo. Footprinting scores also directly correlated with ChIP-seq peaks for Dux in the Tdpoz1 promoter  are not annotated to genes Fig. , raisingces Fig. , which wces Fig. ; LTR. Inces Fig. ; LINE1 iIn conclusion, TOBIAS predicted the locations of Dux binding in promoters of target genes, and could propose how Dux initiates TF-activation networks and induces expression of repeat regions. Importantly, these data further show that TOBIAS predicts any TFBS with increased binding, not only those limited to annotated genes, which aids in uncovering novel regulatory genetic elements.To the best of our knowledge, this is the first application of a DGF approach to visualize gain and loss of individual TF footprints in the context of time series, TF overexpression, and TF-DNA binding for a wide-range of TFs in parallel. Importantly, we found that these advances could in large part be attributed to the framework approach we took in developing TOBIAS, which enabled us to simultaneously compare global TF binding across samples and quantify changes in TF binding at specific loci. The modularity of the framework also allowed us to apply a multitude of downstream analysis tools to easily visualize footprints and gain even more information about TF binding dynamics as exemplified by the prediction of the Dux TF-activation network.29. Therefore, the integration of ATAC-seq footprinting and RNA-seq is an important step in revealing additional information such as classification of TFs into repressors and activators, as well as the kinetics between expression and binding.The power of this framework to handle time-series data becomes especially apparent when integrating the TOBIAS-based prediction of TF binding with RNA-seq data from the same timepoints. For instance, TOBIAS predicted that the maternally transferred TF SALL4 is active in 2C, while its gene expression pattern alone suggests later activation. While SALL4 was one of the TFs with the largest decrease in binding from 2C to 8C, it is, however, also worth noting that since TFs have different baseline activities, large changes between timepoints can also arise from very low activity scores. Although the scores are normalized towards global TF activity, differences in the quality of footprinting (due to sample-specific biases) can also influence the prediction of differential TF binding between conditions, and this should be considered as a limitation of this method. In this context, it is tempting to speculate that TFs for which footprinting scores are low, even though their RNA expression is high, might act as transcriptional repressors, because footprinting relies on the premise that TFs will increase chromatin accessibility around the binding site. In support of this hypothesis, recent investigations have suggested that repressors display a decreased footprinting effect in comparison to activatorsDux expression30. Therefore, our findings suggest a kinetics driven model in which Dux not only initiates ZGA but also regulates its own termination by a temporally delayed negative feedback loop. How exactly this feedback loop could be controlled remains to be determined.In the context of TF target prediction, we showed that TOBIAS could identify almost all known Dux targets. In addition to coding genes, our analysis disclosed novel Dux binding sites and significant footprint scores at LINE1 encoding genomic loci, which appear to be activated at the 4C/8C stage. This finding is especially interesting because a recent study has shown that LINE1 RNA can interact with Nucleolin and Kap1 to repress Despite the striking capability of DGF analysis, some limitations and dependencies of this method still remain. Among these is the need of high-quality TF motifs for matching footprint scores to individual TFs with high confidence. In other words, while the binding of a TF might create an effect that can be interpreted as a footprint, without a known motif, this effect cannot be matched to the corresponding TF. It also needs to be noted that footprinting analysis cannot take effects into account that arise from heterogeneous mixtures of cells wherein TFs are bound in some cells and in others not. Therefore, if not separated, the classification of differential binding will be an observation averaged across many cells, possibly masking subpopulation effects. Recent advances have enabled the application of ATAC-seq in single cells, but this generates sparse matrices, rendering footprinting approaches on single cells elusive. However, we speculate that by creating aggregated pseudo-bulk signals from large clustered single-cell ATAC datasets, DGF analysis might also become possible in single cells.In conclusion, we present TOBIAS as the first comprehensive software that performs all steps of DGF analysis, natively supports multiple experimental conditions and performs visualization within one single framework. Although we utilized the process of PD as a proof of principle, the modularity and universal nature of the TOBIAS framework enables investigations of various biological conditions beyond PD. We believe that continued work in the field of DGF, including advances in both software and wet-lab methods, will validate this method as a resourceful tool to extend our understanding of a variety of epigenetic processes involving TF binding.31 and aligned with STAR v2.6.0c32  to either the mouse or human genome using Mus_musculus.GRCm38 or Homo_sapiens.GRCh38 versions from Ensembl33. Accessible regions were identified by peak calling for each sample separately using MACS2 (parameters: --nomodel --shift -100 --extsize 200 --broad)34. Peaks from each sample were merged to a set of union peaks across all conditions using bedtools merge. Each union peak was annotated to the transcriptional start site of genes (GENCODE35) in a distance of −10000/+1000 from the gene start using UROPA36.Raw sequencing fastq files were assessed for quality, adapter content and duplication rates with FastQC v0.11.7, trimmed using cutadapt31 and aligned with STAR v2.6.0c32  to either the mouse or human genome using Mus_musculus.GRCm38 or Homo_sapiens.GRCh38 versions from Ensembl33.Raw reads were assessed for quality, adapter content and duplication rates with FastQC v0.11.7, trimmed using cutadapt37. All reads longer than 15 nucleotides were aligned versus the mouse genome version mm10, keeping just unique alignments  by using the STAR mapper32. Read deduplication was done by Picard (http://broadinstitute.github.io/picard/).Raw sequencing files in fastq format were quality assessed by Trimmomatic by trimming reads after a quality drop below a mean of Q15 in a window of five nucleotides38, the JASPAR PBM HOMEO collection and Hocomoco V1139 databases. We further included the human ARGFX_3 motif from footprintDB40 which originates from a HT-SELEX assay41. In addition to the Dux/Dux4 motifs of JASPAR and Hocomoco, we also included two TF motifs for Dux/DUX4 created using MEME-ChIP42 with standard parameters on the ChIP-seq peaks of28 (GSE87279).TF motifs were downloaded from JASPAR CORE 201843. Hocomoco motifs were likewise linked to genes through the provided HGNC/MGI annotation. Due to the redundancy of motifs between JASPAR and Hocomoco, we further filtered the TF motifs to one motif per gene, preferentially choosing motifs originating from mouse/human, respectively. For each TOBIAS run, we created sets of expressed TFs as estimated from RNA-seq in the respective conditions. This amounted to 590 motifs for the dataset on human preimplantation stages, 464 motifs for the dataset on mouse preimplantation, and 459 for the DuxOE dataset.JASPAR motifs were linked to Ensembl gene ids by mapping the provided Uniprot id to the Ensembl gene id through biomaRt17. Entrez gene ids were converted to Ensembl gene ids using biomaRt43 and subsequently matched to available TF motifs as previously explained.Maternal genes for human and mouse were downloaded from the REGULATOR databasehttp://hgdownload.cse.ucsc.edu/goldenpath/mm10/database/rmsk.txt.gz). Overlap of Dux sites to individual repeat elements was performed using bedtools intersect. The sum of overlaps were counted per repeat class (LINE1/LTR).Repeat elements for mm10 were downloaded from UCSC (44. Individual gene views were generated by loading TOBIAS output tracks into IGV version 2.6.245 or using the TOBIAS PlotTracks module, which is a wrapper for the svist4get visualization tool46. TF networks were drawn with Cytoscape version 3.7.147. Heatmaps of genomic signal density were generated using Deeptools version 3.3.048. All other figures, such as footprint plots, volcano plots and motif clustering dendrograms were generated by the TOBIAS visualization modules.All TF-score heatmaps were generated by R Version 3.5.3 and ComplexHeatmap package version 3.6Details on the TOBIAS algorithms and framework setup are found in the Supplementary Methods part Details on the validation and comparison of TOBIAS to existing methods for bias correction and footprinting are found in the Supplementary Methods part Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


Complex biochemical reactions take place in the nucleus all the time. Transcription machines must follow the rules. The chromatin state, especially the three‐dimensional structure of the genome, plays an important role in gene regulation and expression. The super enhancers are important for defining cell identity in mammalian developmental processes and human diseases. It has been shown that the major components of transcriptional activation complexes are recruited by super enhancer to form phase‐separated condensates. We summarize the current knowledge about super enhancer in the 3D genome. Furthermore, a new related transcriptional regulation model from super enhancer is outlined to explain its role in the mammalian cell progress. As a regulatory element on the genome, the formation of super enhancers depends on specific chromatin states and spatial structures. A large number of regulatory molecules gather in the chromatin regions where the super enhancers are located. The molecules in these regions are very dense, forming huge transcriptional activation complexes, which interact with each other to construct phase‐separated condensates. Super enhancer‐mediated transcriptional activation depends on spatial proximity rather than a rigid bridge. There are molecular exchanges between the super enhancer‐associated condensate and the surroundings. Inactive Pol II is recruited and phosphorylated Pol II is excreted. Therefore, we propose a super enhancer‐mediated transcription mode to pave the way for future research. Since the discovery of Mendelian, gene regulation models are continuously developing.In 2013, Young and colleagues came up with the concept of the super enhancer (SE), as the region in the embryonic stem (ES) cells where Oct4, Sox2 and Nanog co‐bind,Moreover, the chromatin phase‐separated condensate is also detected at the locus of SEs, providing us with a new direction for research. There is an increasing amount of evidence suggesting that the transcriptional activation of SEs does not depend on rigid construction. Here, the structural and functional basis of SEs in the 3D genome is discussed. A new working model has been proposed and used to explain some biological progress.2There are two basic conditions for gene activation, accessible chromatin and active transcription machines. In eukaryotes, the chromatin structure of the SE plays an important role.2.1Young and colleagues found that H3 lysine 27 acetylation (H3K27ac) is the label for super ehnancers,Mohd‐Sarip proved that acetylated lysine residues are recognized by SWI/SNF, an important component of the chromatin remodelling complex.If chromatin is modified by inert epigenetic modification, such as H3K9 methylation, SEs will be destroyed. Indeed, H3K9 methylation can be recognized by heterochromatin protein 1 (HP1α), a common chromatin silence marker, which may cause the formation of heterochromatin.With advances in microscopic imaging, chromatin phase‐separated condensates were observed in vitro.2.2The modification of DNA, especially DNA methylation, also has effects on the function of SEs. Generally, the GC ratio of SEs is much higher than that of typical enhancers. Hypomethylating agents, that is inhibitors are shown to reduce the activity of super enhancers.The chromatin of the SE is highly accessible. Many studies have observed that the transcriptional regulatory regions identified by DNase hypersensitivity almost coincide with the locus of the super enhancer.3Previous studies have shown that chromatin modification at a site can alter the surrounding nucleosome dynamics, causing activation or silencing of nearby genes.Genomic loci are found to contact each other frequently. For instance, evaluating the three‐dimensional (3D) conformation of the HoxB locus in mouse ESCs revealed that, the architectural protein CTCF, essential for the formation of chromatin loops and enhancer‐promoter interactions, mediates the correct folding of the genome.Using high resolution in situ Hi‐C, researchers found that in mammals and insects the genome contains more TADs than we realized, most of which are smaller than what we detected with the previous Hi‐C.During genetic engineering operations, ectopic expression of cis‐regulatory elements and promoters can form TAD, but this is not a super enhancer. Similarly, in addition to maintaining the boundaries of the TADs, CTCF is also involved in the formation of sister chromatids. The functionality of the SE is included in the TADs. Super enhancers are cis‐regulatory elements that regulate gene expression. Indeed, they are a part of the genome. Super enhancers’ formation is based on specific genomic states Figure .844Super enhancers have a stronger transcriptional activation capacity. The density of proteins close to the SEs is much higher in the genome, suggesting a frequent interaction Figure . A growi4.1Super enhancers were originally thought to be aggregates of core transcription factor binding motifs.Analyses of the complexes in the region of SEs can help us find new master transcription factors. Combined with the IP‐MS data of Sox2 and related bioinformatics data, Ding et al, identified Tex10 as an important transcription factor for the establishment and maintenance of ESC.4.2The mediators and coactivators are thought to bind together with enhancers.Many studies have shown that the mediator has extensive interaction with coactivators.4.3It is shown that epigenetic enzymes are present in the chromatin complex.4.4In addition to proteins, RNAs also participate in the formation of SE‐associated complex. Long non‐coding RNA (lncRNA) is not distributed randomly in the genome but has an obvious overlap with the super enhancer.4.5It has been reported that more and more molecules are components of SEs. Because of the large volume and high degree of integration in the SE‐related complex, almost all molecules that have similar locations are inevitably included. The nuclear matrix protein SAFA  activates immune‐related SEs to establish a cellular antiviral defence line after virus invasion.Due to the limitations in technology, it is not possible to enrich all components collected by SEs through experiments as a result of one or a few factors, but the increasing number of different data indicates that more and more molecules may be involved in this biological process Figure . These m5The transcription‐activated complexes recruited by the SEs, as the very large functional polymers that have about 10‐fold molecular density than the typical enhancer, need a stable structure to maintain their conformation in a physiological state. Advances in biochemistry have often led to alternative theories. The discovery of the chromatin undergoing liquid‐liquid phase separation (LLPS) under physiologic conditions provides us with new insights.In 2018, a report by Sabari showed that MED1 and Brd4 were important components of phase‐separated condensates.Of course, there is an extensive molecular exchange between the phase‐separated condensates and their environment. Young and colleagues proved that hypophosphorylated Pol II entered the mediator condensates preferentially through the IDR at the C‐terminal domain of the large subunit. Once phosphorylated by cyclin‐dependent kinases (CDKs), this incorporation was reduced and Pol II was released as a result of transcriptional activity.6Scientists have shown that the workspace of the SE is a loop.However, this theory cannot explain the formation of chromatin phase‐separated condensate. From another point of view, the length of time that transcription factors and PolII remained on the chromatin was measured in seconds, using fluorescence recovery after photobleaching (FRAP) and single‐molecule experiments.7Based on previous publications, we propose a SE‐mediated transcription mode to pave the way for future research Figure . First, 8A gene can express in different cells, playing different roles with different conditions of chromatin.Super enhancers are involved in many biological processes. The overactivation of proto‐oncogene is mostly related to the abnormality of SEs. The rapid rate of cell replication further accelerates the disordered assembly of chromatin.In vitro, the transformation of cell fate has also been shown to be associated with SEs. During the somatic cell reprogramming, histone modification‐related enzymes are recruited by core transcription factors and are pulled to the SEs’ region. As a result, the old SEs were broken. In the presence of Myc, chromatin becomes looser and the region of new SEs becomes exposed. Initially, established SEs began to play a weak role.9Since the establishment of molecular biology, scientists have long sought the rules governing molecules in the nucleus. It is generally accepted that gene activation requires two basic conditions, open chromatin and active transcription machinery. More and more researchers have devoted the transition model through the latest technology.Furthermore, we optimized knowledge related to SEs. Previous theories have suggested that there are rigid physical connections between SEs and promoters. This structure acts as a bridge, sending activation signals downstream. However, this contradicts the latest progress in molecular biology. We modified the original model and propose that the SE activates downstream genes through spatial proximity. According to our model and previous research, the pattern of how SEs can regulate biological processes was also put forward in order to provide references for the following research.More systematic analysis through multiple omics can help us grasp the main contradiction. The fate of cells is determined by core transcription factors. As a major binding region of the core transcription factors, SEs play an important role in determining cell identity. The expression of a gene is regulated by many molecules at the same time. Core transcription factors can be used to define SEs. Conversely, SEs can also be used to identify undefined core transcription factors, enriching the knowledge of transcription regulation.The research on SEs can also solve problems in economic production and human life. In large animals, for example, understanding muscle‐specific SEs can help us to screen and breed high‐value livestock. Hearts from genetically modified pigs could be transplanted into baboons and that can be kept alive.From the foregoing discussion, a basic trend emerges. The study of cell fate determination is becoming more and more systematic and refined. Systematic analysis allows us to better understand the state of the cell at a macro level, and refined regulatory mechanisms are essential to increase knowledge about the genome. As the functional core obtained from multiple omics, SEs must play more roles in cell processes. Understanding related regulatory mechanisms associated with SEs could help us solve more biological problems. The study of SEs will bring another leap forward in life science.All authors have reviewed the final version of the manuscript and given approval for its publication. This manuscript has not been published, in whole or in part, and is not under consideration for publication elsewhere. The authors have no conflicts of interest to declare.Juqing Zhang and Jinlian Hua designed the study and wrote the manuscript. Mingzhi Liao and Yaqi Zhou analysed the data. Wei Yue and Xingqi Chen made instructions and corrections to the language and content of the article.


Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic multisystem disease exhibiting a variety of symptoms and affecting multiple systems. Psychological stress and virus infection are important. Virus infection may trigger the onset, and psychological stress may reactivate latent viruses, for example, Epstein–Barr virus (EBV). It has recently been reported that EBV induced gene 2 (EBI2) was upregulated in blood in a subset of ME/CFS patients. The purpose of this study was to determine whether the pattern of expression of early growth response (EGR) genes, important in EBV infection and which have also been found to be upregulated in blood of ME/CFS patients, paralleled that of EBI2. EGR gene upregulation was found to be closely associated with that of EBI2 in ME/CFS, providing further evidence in support of ongoing EBV reactivation in a subset of ME/CFS patients. EGR1, EGR2, and EGR3 are part of the cellular immediate early gene response and are important in EBV transcription, reactivation, and B lymphocyte transformation. EGR1 is a regulator of immune function, and is important in vascular homeostasis, psychological stress, connective tissue disease, mitochondrial function, all of which are relevant to ME/CFS. EGR2 and EGR3 are negative regulators of T lymphocytes and are important in systemic autoimmunity. Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic multisystem disease characterized by at least six months of fatigue and various other symptoms, including headache, sore throat, muscle pain, joint pain, muscle weakness, post-exertional malaise, sleep abnormalities, with secondary anxiety and depression . AlternaPsychological stress is common in ME/CFS, as it is in other autoimmune diseases, for example, ulcerative colitis, rheumatoid arthritis, psoriasis, asthma, etc. Such emotional aspects are believed to also underlie relapses and flare-ups which may precipitate hospital admission and the use of immune-modifying treatments in a variety of autoimmune diseases.I previously reported that Epstein–Barr virus (EBV) induced gene 2 (EBI2) was upregulated in peripheral blood mononuclear cells (PBMC) of 12 of 31 (38%) patients with ME/CFS and that these patients appeared to have a more severe disease phenotype and lower levels of EBV nuclear antigen 1 (EBNA1) IgG , is upreThe immediate early genes (IEG) and early growth responses 1–3 (EGR1–3) have been found to be upregulated in blood of a subset of patients with ME/CFS , in mostWe have previously reported that EGR1–3 and EBI2 were upregulated in PBMC of ME/CFS patients as compared with normal blood donors . These gEBI2 mRNA was found to be upregulated in 12 of 31 (38%) ME/CFS patients and none of the controls ,6,7, thiUpregulation of EGR1, EGR2, and EGR3 occurred in ME/CFS patients with a raised level of EBI2 mRNA , suggestThe cellular as distinct from viral IEG response, was first discovered in cultured cells as those genes that were induced in response to growth factors and mitogenic agents, and were later shown to occur in most cell types in response to a variety of stimuli. The cellular IEG response is a rapid and transient increase in mRNA encoding the IEGs, independent of protein synthesis, and requiring only post-translational modification of pre-existing molecules. It is assumed that the cellular IEG response regulates downstream responses by controlling expression of early target genes. Cellular stimuli which trigger an IEG response include raised glucose, hypoxia, UV irradiation, mechanical stimulation, hormones (including insulin), growth factors, cytokines, and infection with a wide variety of viruses . IEGs arThis rapid cellular response to biological or environmental signals is critical for survival and adaptation of an organism ,10,11. IThe early growth response (EGR) family is comprised of four members (EGR1–4) which are C2H2-type zinc finger proteins and function as transcriptional regulators. EGRs1–4 are highly homologous within and between species, particularly within the region containing three C2H2 zinc finger DNA binding domains, suggesting possible overlap in targets and function of the EGR proteins. In contrast, the N-terminal regions of the EGR proteins differ significantly. EGR1–3 have a domain of interaction with transcriptional co-repressors, NAB1 and NAB2, suggesting that these proteins can mediate transcriptional repression, as well as negative regulation of the EGR proteins themselves .Early growth response (EGR) genes have an extremely broad and diverse influence on many aspects of human physiology and disease. Significant themes which have been identified are the immune response and its relevance in systemic autoimmunity; vascular homeostasis; psychological stress and secondary physiological responses; stress-related mood disorders and schizophrenia; drug reward, withdrawal and relapse; connective tissue physiology and disease; and cancer biology. These aspects are summarized in EGR1 is the best characterised among the EGR gene family. It is an 80kDa multifunctional transcription factor which upregulates genes involved in cell growth, cell cycle regulation, differentiation, mitogenesis, and tumor suppression. EGR1 is a marker of neuronal activity which allows mapping of brain activation following a specific behavioural, pharmacological or environmental event . InductiThere are well documented sex differences in expression of EGR1 in different parts of the rat brain ,74,75. TRegarding EGR1 gene targets, the nine-nucleotide long sequence GCGG/TGGGCG was originally identified as the EGR1 recognition sequence ,80. Subs4 annotated genes, 8552 (53.9%) contained at least one EGR1 binding region within 3 kb of their transcription start site (TSS), indicating that in diverse human cells, EGR1 binds a huge number of genes and potentially regulates a very large gene expression profile. Functional analysis of genes containing at least one EGR1 binding site, has revealed enrichment of pathways related to growth factor signalling and general intracellular signalling such as Ras and MAPK, also regulating EGR1 expression itself. Molecular functions include chromatin binding, transcription factors, guanyl-nucleotide exchange factor activity, and serine/threonine kinase activity, revealing that EGR1 controls every level of signal transduction, from second messenger to transcription factor. Cellular localization of the EGR1-bound gene products ranges from chromatin to the cell membrane. These data suggest that EGR1 regulates cell-cell communication through regulation of a vast array of genes [The Encyclopedia of DNA Elements (ENCODE) project included EGR1 as part of the tier 1 chromatin immunoprecipitation followed by deep sequencing (ChIP-seq), which provided vast information on EGR1 target genes . Among 1of genes .EGR2 expression is associated with the onset of myelination in the peripheral nervous system and hindbrain development , and EGREGR2 is a cell growth suppressor. EGR2 is likely to be a mediator of PTEN growth-suppressive signalling , in whicEGR2 also suppresses the c-Jun NH2-terminal protein kinase (JNK)-c-Jun pathway, which is required for proliferation and death . TherefoThe immune mechanisms for maintenance of tolerance to self-antigens and prevention of autoimmunity include anergy, apoptosis, and function of regulatory T cells ,99. BothNull mutation of each of EGR2 and EGR3 results in death in 100% and approximately 40% of mice, respectively, during the perinatal period ,108, whiDouble knockout (DKO) mice, which were deficient in both EGR2 and EGR3 in B and T lymphocytes, developed early onset, lethal systemic autoimmune syndrome with multi-organ lymphocyte infiltration, high titre autoantibody to dsDNA and histones, along with severe glomerulonephritis . These mIn EGR2 CKO mice, EGR3 may compensate for the lack of EGR2 function as the phenotype of these mice is milder than that of EGR2/3 DKO mice. EGR3 CKO mice, however, do not develop autoimmune symptoms , likely Low circulating TGF-β1 is a consistent finding in humans with systemic lupus erythematosus (SLE) and is correlated with disease activity . PBMC frThe NZM2410 mouse strain is a cross between the lupus-prone New Zealand white (NZW) (lupus prone) and New Zealand black (NZB) lineages. NZM2410 mice develop early progression of anti-nuclear antibody (ANA) severe lupus gomerulonephritis at an early age. In NZM2410 mice, the Sle1b locus mediates ANA production and the Ly108 gene of the signalling lymphocytic activation molecule (SLAM) family is the strongest candidate gene at this locus . The Ly1The EGR genes 1–3 are critically important in EBV infection. A GC-rich sequence in the −99/−91 EBV C promoter (Cp) region was identified as essential for oriPI-EBNA1-independent, as well as oriPI-EBNA1-dependent, activity of the promoter. This region contains overlapping binding sites for SP1 and EGR1. Results suggest that SP1 is a positive regulator and EGR1 is a negative regulator of Cp activity in B lymphoid cell lines. Bound EGR1 prevents binding of the positive regulator, SP1 .Following the acute phase, Epstein–Barr virus (EBV) establishes latency in memory B lymphocytes  and otheHost EGR1 transcription is induced following EBV lytic cycle activation, through the action of Zta . ActivatExpression of EGR1, as well as EGR2 and EGR3, has been shown to occur upstream of EBV BRLF1 and BZLF1 following cross-linking of the B lymphocyte receptor in Burkitt lymphoma cells .EGR1 expression has been shown to correlate with the transformed phenotype and type of viral latency in EBV+ lymphoid cell lines . EGR1 exThere is a significant overlap among the pathological processes affected by EGR gene abnormalities and those associated with ME/CFS  leads to increased EGR1 mRNA in multiple brain locations, and EGR1 is critical in encoding the chronic behavioural effects of stress . In ME/CEGR1 regulates matrix production in various connective tissues and is often involved in abnormal production of extracellular matrix in fibrotic conditions . ConnectEGR1 binding sites occur in promoters of several important mitochondrial genes and increases upon skeletal muscle contraction. In addition, changes in intracellular calcium modify mitochondrial phenotype, in part via the involvement of EGR1 ,42,160. Finally, stress-related mood disorders, schizophrenia, drug-related aspects, and cancer, in which EGR1 plays important roles, have no counterparts in ME/CFS.In conclusion, ME/CFS is a multisystem disease which can be triggered by infection with any of a range of viruses, including EBV and other herpesviruses such as HHV6, parvovirus B19, and others . The preI have previously proposed that upregulation of the EBI2 gene could be used to identify an Epstein–Barr virus induced subtype of ME/CFS which may have increased severity. Although the EGR genes are upregulated in some ME/CFS patients, the issue of whether they can be used as biomarkers is unclear. However, I do not believe that the EGR genes would be useful as biomarkers for either ME/CFS (as they are upregulated only in a subset) or EBV-associated ME/CFS (as EBI2 is likely to be more specific).