Transcription factors activate gene expression in development, homeostasis, and stress with DNA binding domains and activation domains. Although there exist excellent computational models for predicting DNA binding domains from protein sequence, models for predicting activation domains from protein sequence have lagged, particularly in metazoans. We recently developed a simple and accurate predictor of acidic activation domains on human transcription factors. Here, we show how the accuracy of this human predictor arises from the clustering of aromatic, leucine, and acidic residues, which together are necessary for acidic activation domain function. When we combine our predictor with the predictions of convolutional neural network (CNN) models trained in yeast, the intersection is more accurate than individual models, emphasizing that each approach carries orthogonal information. We synthesize these findings into a new set of activation domain predictions on human transcription factors. Transcription factors regulate gene expression with DNA binding domains and effector domains. DNA binding domains are structured, conserved, and recognize related DNA sequences . Profileacidic exposure model for activation domain function . We split the longest predictions and tested 150 regions in our high-throughput assay  and VP16 H1  . The firut assay . Of the ut assay . The sucSaccharomyces cerevisiae transcription factors   . Both of7 tiles) . Multipl7 tiles) . Sanbornno acids . Erijmanragments , consistragments , 2022. Iragments . All of In our previous work, we performed very little optimization of our mechanistic predictor . Here, wBased on our improved understanding of how the original mechanistic predictor functions, we developed a modestly improved mechanistic predictor. Our new predictor uses tryptophan, phenylalanine, and leucine residues, 39-AA windows, and more relaxed thresholds for net charge and hydrophobic residue counts. The new model predicted many more activation domains with minimal loss of accuracy. The primary value of the mechanistic predictor is its simplicity. This model is so simple that we remain surprised this rule was not uncovered earlier. When we compare our mechanistic predictors to CNN models trained in yeast, we found that the intersection is more predictive than individual models, emphasizing that each approach carries distinct information. We synthesize these findings into a new set of activation domain predictions.The gold standard list (GSL) consists of activation domains from 2 sources. The first source is transcription factors with activation domains that were annotated in UniProt, which are in the “data/UniprotActivationDomains_HighqualitySet.csv” file. The second source is activation domains manually curated from the literature, which are in “/data/ActivationDomainsHuman.csv.” In “/notebooks/Building the GSL.ipynb,” we combined activation domains from both sources into one list, merging entries with the same UniProt ID with overlapping start and end positions. In cases where the annotated boundaries differed, we used the longest region.For the tested regions in The sequences of all 1,608 human transcription factors were downloaded from UniProt . We downt-test between the 2 population means. We report all enrichments that were significant at P < 0.01 after a Bonferroni correction for multiple hypothesis testing. We used a threshold of 15% for enrichment of individual amino acids . This 15% threshold is an emerging standard in the field , yielding 881K tiles. The full proteome and GSL were similarly decomposed into tiles. The composition of each tile was computed by counting amino acids. The net charge was calculated as R + K – D – E. The original predictor counted W + F + Y + L residues. Other variants of the predictor counted other subsets of residues. For the composition histograms in he field . We clasThe main predictor function is make_predictions in “notebooks/AD_predictor_tools.py.” The make_predictions function has parameters that can be used to vary the predictor. We varied the predictor in one way at a time. We adjusted the parameter UpperCorner_slope1 to change the slope of the boundary line originating from the upper corner, CITED2. To create region D in We identified all the tiles that satisfied equation  and thenWe then performed permutation testing. We used the function compare_to_random in “notebooks/AD_predictor_tools.py” to compare the overlap with the GSL of predictions vs random sequences. To create a random set of sequences, we counted the number of unique UniProt IDs in the predictions and randomly sampled the same number of transcription factors from all human transcription factors. We found that preserving the fact that sometimes there were multiple predictions per transcription factor was more stringent in the permutation test than assuming all predictions were independent. Then, for each unique UniProt ID in the predictions, we recorded the length distribution and number of predictions. We iterated through unique UniProt IDs and randomly selected parts of each sampled transcription factor so that the selected parts had the same number and length distribution as predictions with one UniProt ID. Finally, we recorded the number of times a randomly sampled transcription factor region had any overlap with a GSL entry, using start, end, and UniProt ID to compare. Here we used 1-AA overlaps to increase the stringency of the permutation test. We compared this number to overlaps of predictions with the GSL. We repeated this random sampling for 10,000 permutations. We never randomly sampled as many overlaps as the predictor.We searched for signatures of molecular grammar in multiple ways. Generally, we would compute parameters for all the activation domains on the GSL and look for enrichment compared to all Lambert transcription factors. For the charge mixture parameters, we tried the original formulation of Kappa and Omega as well as a custom variation of Omega that quantified the mixture of WFYL residues with acidic residues . We thenhttps://github.com/FredHutch/adpred and then ran it on all Lambert transcription factors. Using the criteria detailed by the authors at https://adpred.fredhutch.org/, we considered sequences with at least 10 consecutive positions with a score of at least 0.8 to be a predicted activation domain. We downloaded the activation domains predicted on human transcription factors by PADDLE at https://cdn.elifesciences.org/articles/68068/elife-68068-fig 3-data1-v3.xlsx. We used predictions of both medium and high strength.The 2 neural networks to which we compared the predictor were ADPred and PADDLE . We instet al. list). Then, it compares our predictions that overlap with a prediction made by a neural network to the list of known activation domains.We used the compare_two_predictors function in “notebooks/AD_comparison_tools.py” to compare our predictor to the neural net predictors. This function first individually compares the overlap of our predictions and a neural net's predictions with a list of known activation domains . We removed all entries not on the Lambert list of transcription factors . Nicole To quantify the performance of our activation domain predictors, we curated a GSL of 167 activation domains from 135 proteins, including 129 human transcription factors . This liOur GSL and the Soto list are highly overlapping , and aciet al. list contains 290 (79.6%) acidic, 18 (3.5%) Q-rich, 119 (22.9%) P-rich, 124 (23.8%) S-rich, 36 (6.9%) glycine-rich, and 9 (1.7%) alanine-rich activation domains. Using our criteria, some activation domains are enriched for more than 1 amino acid, notably acidity, and serines. Annotated acidic activation domains on the 2 lists also overlap with our tested regions . Some activation domains increase activity when they are phosphorylated   and examTo determine the parameters that contribute most to the predictor, we performed a sensitivity analysis. We removed each of the 8 AAs in the predictor and recomputed predictive power . F, L, aFor further comparison, we replaced the y-axis parameter with all singles, pairs, and triplets of amino acids . For eac, Region D, To make new predictions, we altered the boundaries of the mechanistic predictor to include more tiles. We had already tested nearly all the original predictions and found that 72% had detectable activity in our assay . Making Fig. 4aRegion E) and lowered the acidic boundary (Region F). When we consider only acidic activation domains, the projected tiles look very similar to those of the full GSL , where amino acids must have a defined spacing or arrangement, e.g. ΦxxΦΦ, where Φ is a bulky hydrophobic residue . Examplearameter , the Omearameter , and reparameter . Other sarameter . The gran = 144) and PADDLE (n = 604) increased sensitivity , which is interesting because leucines make larger contributions to activity in human activation domains than in yeast activation domains . Indeed,Why does the combination of the mechanistic predictor and the CNNs improve performance? Some of this improvement is likely because each approach contributes orthogonal information. We also believe that the overlap might be providing some insight into how the CNNs work. Convolutional neural networks are black-box models, which makes it difficult to understand the source of their accuracy. ADpred and PADDLE take as inputs primary sequence, predicted secondary structure, and predicted intrinsic disorder, but the models do not tell us which feature, or combination of features, is most important for prediction. For regulatory DNA CNNs, there are emerging tools for extracting mechanistic insight , but anan = 47) because in these cases little or no experimental effort was devoted to finding a minimal activation domain. Here, the positive predictive value is 0.706 and the true positive rate is 0.344. This alternative assessment with a smaller search space likely represents the maximum performance of our model. The true performance of our revised mechanistic predictor sits between these 2 estimates.Assessing the positive predictive value (precision) and the true positive rate  of the mechanistic predictor is challenging because existing lists of activation domains are incomplete, making it difficult to evaluate which predictions are false positives and which are novel predictions. We had previously used permutation tests to randomly select transcription factor regions to show the mechanistic predictor identified more activation domains than expected by chance . All varDuring the review process, a systematic screen for human activation domains in K562 cells was published . This scFinally, as previously published, the most rigorous assessment of our predictor is experimental validation . When weN-terminal region, which is least important for repression activity  correctly predicted as true negatives. Second, we looked at transcription factors with repression domains as defined by Soto and found that 66/384 (17%) had predicted activation domains, indicating a low false positive rate. Alternatively, these may be bifunctional transcription factors that activate and repress transcription. Third, we looked at the number of predicted activation domains that overlap repression domains and found 33 examples. Of these, 27/33 overlap KRAB domains, which are a very interesting set of false positives. We had previously tested 13 predictions that overlap KRAB repression domains . One is activity . Three oBased on these assessments, the mechanistic predictor is accurate and sensitive. However, we wish to emphasize that the main utility of the mechanistic predictor is its simplicity and interpretability.Both our original mechanistic predictor and the improved mechanistic predictor do not identify all of the acidic activation domains on the gold standard and Soto lists. Similarly, neither ADpred 53/105) nor PADDLE (76/105) can detect all these acidic activation domains. We have tuned the mechanistic predictor to have a low false positive rate at the expense of a high false negative rate. While there are multiple interpretations of this result, we favor the interpretation that there are multiple subclasses of acidic activation domains and that the existing predictors can find the one subclass that is well described by the acidic exposure model. These models miss activation domains where activity is regulated by modifying the net charge with post-translational modifications. For example, the first activation domain of p53  has three sites that increase activity when phosphorylated  . These r/105 nor Accurate computational models for predicting activation domains from protein sequence will advance basic science and precision medicine. Computationally annotating activation domains would allow studies of how paralogous transcription factors diversify after duplication and enable evolutionary comparisons of domain shuffling. Comprehensive lists of transcription factors with activation domains could improve gene regulatory networks by adding signs to the connections inferred from genome binding data  or by diOur mechanistic predictor is valuable because it is simple and interpretable. Its accuracy comes from the acidic exposure model, which describes a subclass of acidic activation domains that balance acidic residues with key hydrophobic residues. The analyses presented here confirm and strengthen our previous conjectures . This woWhy are so many activation domains negatively charged? What is the mechanistic advantage of acidity? This question has been repeated many times since it was posed by Paul Sigler . In prinActivation domains display very flexible sequence grammar. If activation domains had strict sequence grammar requirements for function, we would have seen these signatures in the evolutionary record, mutagenesis, or in tiling experiments. An early grammar model, the 9aaTAD model, can identify known activation domains, but in high-throughput screens of random peptides or yeast transcription factors, it does not detect more often than expected by chance . Insteadonly solution. Motifs are uncommon and rarely generalize beyond a few transcription factors. Surveys of random peptides, yeast transcription factors, and human transcription factors found enrichment of only [DE][WFY] “mini motifs” (How do we square a very flexible-grammar with the documented role of short linear motifs and amphipathic alpha helices? The dominant model for activation domains is that they are anchored by a hydrophobic short linear motif embedded in a permissive context. At this time, the features of the context are more clearly defined than the motifs. The context is acidic residues and intrinsic disorder. In some cases, the motifs are clearly present, conserved, and contribute to activation domain activity . A motif motifs” . We argu motifs” . Fuzzy bIt is not clear at what point the motif ends and the context begins. In our mutagenesis of VP16 and CITED2, we found that virtually every hydrophobic residue contributed to activity, blurring the distinction between motifs and context. Adding aromatic residues near a motif—in essence extending the motif—increases activation domain activity . Based oWe conclude that the human proteome contains a class of strong acidic activation domains that can be recognized by the clustering of W, F, L, and acidic residues. This balance between acidity and hydrophobicity accurately predicts known activation domains, many of which are well described by our acidic exposure model. Our work implies there are other classes of acidic activation domains that are not predicted by our model and which likely bind to other coactivators. Forthcoming, comprehensive maps of activation domains  will creiyad131_Supplementary_DataClick here for additional data file.iyad131_Peer_Review_HistoryClick here for additional data file.


Lin28B gene is involved in the initiation of puberty, but its regulatory mechanisms remain unclear. Therefore, in this study, we aimed to study the regulatory mechanism of the Lin28B promoter by cloning the Lin28B proximal promoter for bioinformatic analysis. Next, a series of deletion vectors were constructed based on the bioinformatic analysis results for dual-fluorescein activity detection. The transcriptional regulation mechanism of the Lin28B promoter region was analyzed by detecting mutations in transcription factor-binding sites and overexpression of transcription factors. The dual-luciferase assay showed that the Lin28B promoter region −837 to −338 bp had the highest transcriptional activity, and the transcriptional activity of the Lin28B transcriptional regulatory region decreased significantly after Egr1 and SP1 mutations. Overexpression of the Egr1 transcription factor significantly enhanced the transcription of Lin28B, and the results indicated that Egr1 and SP1 play important roles in regulating Lin28B. These results provide a theoretical basis for further research on the transcriptional regulation of sheep Lin28B during puberty initiation.The  Lin28B in regard to the onset of puberty through transcriptome sequencing technology and high expression was detected in the hypothalamus, pituitary, ovaries, and fallopian tubes through qPCR and Western blot technology. The Lin28B gene has a certain relationship with the initiation of puberty in Dolang sheep [Puberty in female animals is the period when estrus appears for the first time and ovulation occurs. It plays an important role in animal growth and development. In addition, this is an important phase for animals to acquire their reproductive ability . Pubertyng sheep .Lin28B gene was first cloned from human hepatocellular carcinoma cells. It is overexpressed in hepatocellular carcinoma and is highly similar to the heterochronous gene Lin28. These two genes may originate from the duplication of the same ancestral gene. Their mRNAs have a long 3′-UTR with the complementary site of Let7 microRNA, which indicates that these genes may be the natural target of Let7. Lin28B contains a cold shock domain (CSD) and a pair of CCHC zinc finger domains [Lin28B is involved in the regulation of puberty [Lin28B mRNA was abundantly expressed in the hypothalamus of neonatal male and female rats and that Lin28B expression decreased sharply during the transition from infancy to puberty [Lin28B expression decreases in the ovaries of female mice from infancy to puberty [The  domains . Related puberty ,6. SangiThe promoter is usually located upstream of the 5′end of the structural gene, and is a nucleotide sequence that RNA polymerase recognizes and specifically binds to in the template DNA to ensure the accuracy of transcription initiation . StudiesLin28B in the literature. In order to clarify the significance of the promoter region to the transcriptional regulation of the Lin28B, this study constructed a promoter deletion fragment vector to explore the core promoter region. To further elucidate the transcription regulation mechanism of Lin28B, this study predicted and verified the related transcription factors SP1 and Egr1, thus providing a theoretical basis for the regulation mechanism of sheep Lin28B in puberty initiation.At present, there is no related research on the promoter region of sheep All animal experiments were approved by the Animal Protection and Use Committee of the Tarim University of Science and Technology College .V/V)), and stored in a refrigerator at −20 °C for later use. Genomic DNA was extracted from Dolang sheep blood using a DNA extraction kit  according to the manufacturer’s instructions. Its purity was detected using 1.5% agarose gel electrophoresis, and its content was detected using an ultraviolet spectrophotometer. The extracted DNA was stored at −20 °C.Under natural light, all Dolang sheep were given free access to food and water and were healthy. Fifteen three-month-old female Dolang sheep maintained at the Tarim University experimental station were used as models in this study. Blood (5 mL) was collected from the jugular vein of each sheep, treated with EDTA-K anticoagulant  was used to design primers to amplify 7 promoter fragments of different lengths, namely L1 (−2837 to +162), L2 (−2337 to +162), L3 (−1837 to +162), L4 (−1337 to +162), L5 (−837 to +162), L6 (−337 to +162), and L7 (−837 to −338). The restriction endonuclease sites NheI and XhoI  were added to the upstream and downstream primers, respectively, and protective bases were added before the enzyme cutting sites. Primer sequences are listed in 2O) 9.5 µL. PCR conditions: pre-denaturation at 94 °C for 5 min; denaturation at 94 °C for 30 s, annealing at 60 °C for 30 s, extension at 72 °C for 2 min, 40 cycles; extension at 72 °C for 10 min.Using the promoter sequence of the sheep pGL3-Basic vector; ligation system (10 µL): 4 µL of recovered PCR product, 1 µL of vector, 5 µL of solution I. The ligation reaction was carried out at 4 °C for over 12 h. A volume of 5 µL of the ligated product was added to 100 µL DH5α cells ; after heat-shock in a 42 °C water bath for 90 s, the centrifuge tube was quickly transferred to an ice bath for 1–2 min. Liquid LB medium (395 µL) was added, and the mix was shaken at 200 rpm at 37 °C for 90 min. Then, 200 µL of LB medium containing DH5α was added to AMP medium at 37 °C and cultured for 12–16 h. Single colonies were selected for culture and plasmid purification. Colony screening was performed by polymerase chain reaction (PCR) and double enzyme digestion (NheI/XhoI) experiments. The positive plasmid was sent to the Shanghai Sangon Bioengineering Company for sequencing. Plasmids with the correct sequence were amplified in large quantities and stored at −20 °C for later use.The PCR product was subjected to electrophoresis gel, and the target fragment was recovered using an agarose gel recovery kit . The recovered product was ligated to the v/v), and the cells were counted using a hemocytometer. The day before transfection, a total of 1 × 104 293T cells (provided by the Chinese Academy of Sciences Cell Bank) were inoculated in 96-well plates, and 150 µL freshly prepared complete medium   was added, mixed, and gently shaken to prevent cell accumulation in the center of the well that could result in mass cell death. They were cultured at 37 °C with 5% CO2 for subsequent experiments. When the confluence of the cells reached 70–80%, the cells were co-transfected with the target plasmid and the internal reference plasmid pRL-TK in a ratio of 10:1 according to the instructions of the Lipofectamine™ 3000 transfection reagent . At least three biological replicates were performed. The 96-well plate was taken out of the incubator 36 h after transfection, and the dual-luciferase activity was detected according to the instructions of the Dual-Glo® Luciferase Assay System kit . The activity of firefly luciferase and Renilla luciferase were detected with a Synergy H1 chemiluminescence detector .Cultured 293T cells were suspended in PBS for later use. Trypan blue staining solution (0.4%) was added to the cell suspension at a cell dye ratio of 1:1 . The primer design instructions from the Primer Premier software (version 6.0) were used to design the transcription factor  primers . Using tpRL -TK  and pGL3-basic, plasmid L7, mutant plasmids ET1 or ST2; at least three biological replicates were set up for each treatment. The 96-well plate was removed from the incubator 36 h after transfection, and the dual-luciferase activity was detected according to the instructions of the Dual-Glo Luciferase Assay System kit.According to the instructions of the Lipofectamine™ 3000 transfection reagent, 293T cells were co-transfected with Egr1 overexpression vector was constructed by Suzhou GENEWIZ Co., Ltd. . The Egr1 overexpression vector, pEGFP-N1, and plasmid L7 were co-transfected into 293T cells according to the instructions of the Lipofectamine™ 3000 transfection reagent, and at least three biological replicates were set up for each treatment. The 96-well plate was removed from the incubator 36 h after transfection, and the dual-luciferase activity was detected according to the instructions of the Dual-Glo Luciferase Assay System kit.The p < 0.05 and ** p < 0.01 indicated significant and extremely significant differences, respectively.Relative activity value of dual luciferases = firefly luciferase activity /Renilla luciferase activity values . The data were analyzed using SPSS  and expressed as the standard error of the mean. At least three biological replicates were set up for each treatment group, and the statistical analysis was performed using a one-way ANOVA. * Lin28B promoter fragment as a template, a series of deletion primers was used to clone Lin 28 gene promoter deletion fragments of different lengths: L1 (−2837 to +162), L2 (−2337 to +162), L3 (−1837 to +162), L4 (−1337 to +162), L5 (−837 to +162), L6 (−337 to +162), and L7 (−837 to−338). The missing fragment of the Lin28B promoter was ligated to a luciferase reporter gene vector to obtain the corresponding recombinant vector.As shown in Lin28B.After incubation for 36 h, the promoter activity of each fragment was detected . The resThe results of the dual luciferase assay  showed tLin28B promoter L7 (−837 bp to −338 bp) fragment and the transcription factors related to the transcriptional regulation of puberty with higher scores were selected. The results are summarized in SP1 and Egr1.The online software JASPAR and Animal TFDB 3.0 were used to predict the transcription factor binding sites in the Egr1 and SP1 mutations compared with the non-mutated fragment was extremely significantly different (p < 0.01), and the difference between the L7 fragment and the EGRI mutant vector was more significant  than that of the control group co-transfected with the pEGFP-N1 and L7 fragments  to kidney luciferase (pRL-TK) ratio to determine gene expression. This method can reduce experimental errors caused by factors such as cell activity, transcription efficiency, and cleavage efficiency, and the test results are reliable and accurate [Lin28B gene and the mRNA expression in the process of puberty initiation in Dolang sheep. Thus, we inferred that L7 (−837 to −338 bp) is the core promoter region of the gene.Puberty is the period when the animal’s reproductive ability begins, and the timing of puberty is also related to the animal’s reproductive ability. Animals with early puberty have a higher reproductive ability and more offspring throughout their life. Studies have confirmed that there is a certain correlation between the expression of  animals . Howevercription . Lucifercription ,15. The accurate . In thisaccurate . AccordiLin28B promoter L7 (−837 bp to −338 bp) fragment, and the intersection of higher scores was selected to obtain the two transcription factors Egr1 and SP1. The Erg1 transcription factor was point-mutated in this study, and an overexpression vector was constructed. Double-luciferase detection results showed that the Egr1 transcription factor plays an important role in the transcriptional regulation of Lin28B. According to relevant studies, Egr1 plays an important role in the reproductive system, and Egr1 can maintain sufficient LHb expression in the female pituitary [Egr1 and simultaneously binds Egr1 to two conserved cis-acting elements of the LH proximal promoter [Egr1 knockout mice showed reduced fertility, blocked GnRH-induced pituitary LH production, and impaired ovarian LH receptor expression. Egr1 regulates the expression of many important factors involved in folliculogenesis and ovulation in the ovary [Egr1 is involved in various cell biological processes, such as differentiation, proliferation, and apoptosis, and Lin28B is a key gene involved in the development of puberty in sheep. Through research, it was found that Egr1 can combine with the promoter region of Lin28B gene and significantly increase the promoter activity of Lin28B gene. Therefore, this study suggests that the Egr1 transcription factor plays an important role in the transcriptional regulation of Lin28B during puberty in sheep. In this study, we constructed a point mutation vector for the transcription factor SP1. After testing, it was found that the SP1 transcription factor was found to affect the transcriptional regulation of Lin28B. Studies have reported that SP1 interacts with various epigenetic factors, actively regulates gene transcription, and promotes gene expression [SP1 interacts with epigenetic factors that negatively regulate gene transcription to inhibit gene expression [SP1 is present in oocytes and somatic cells. Knockdown of SP1 expression, especially in pregranulosa cells, significantly inhibits oocyte apoptosis and primordial follicle formation, suggesting that somatically expressed SP1 plays a role in primordial folliculogenesis, clarifying that SP1 regulates pregranulosa cells during mammalian ovarian development to control the establishment of ovarian reserves [SP1 plays a role in the transcriptional regulation of Lin28B during puberty in sheep.A gene promoter is a complex region containing many transcription factor binding sites which determine the transcription start site and frequency . Transcrituitary ,21. Duripromoter ,23,24. Ehe ovary ,25,26. Ppression . Furtherpression . During reserves . At presreserves , the pulreserves . This caLin28B, we constructed a transcription factor point mutation vector and an Egr1 overexpression vector. Double-luciferase assays revealed that the transcription factors SP1 and Egr1 can significantly affect the activity of the promoter. Therefore, this study suggests that the transcription factors SP1 and Egr1 play a role in the transcriptional regulation of Lin28B.In this study, by constructing promoter fragment deletion vectors, we found that −887 to −338 bp is a region with high transcriptional activity. To further understand the transcriptional regulatory mechanism of


One bottleneck in understanding the principles of 3D chromatin structures is caused by the paucity of known regulators. Cohesin is essential for 3D chromatin organization, and its interacting partners are candidate regulators. Here, we performed proteomic profiling of the cohesin in chromatin and identified transcription factors, RNA-binding proteins and chromatin regulators associated with cohesin. Acute protein degradation followed by time-series genomic binding quantitation and BAT Hi-C analysis were conducted, and the results showed that the transcription factor ZBTB21 contributes to cohesin chromatin binding, 3D chromatin interactions and transcriptional repression. Strikingly, multiomic analyses revealed that the other four ZBTB factors interacted with cohesin, and double degradation of ZBTB21 and ZBTB7B led to a further decrease in cohesin chromatin occupancy. We propose that multiple ZBTB transcription factors orchestrate the chromatin binding of cohesin to regulate chromatin interactions, and we provide a catalog of many additional proteins associated with cohesin that warrant further investigation.  Chromatin structures are partitioned into hierarchical units, including chromosome territories, A/B compartments, topologically associating domains (TADs), enhancer–promoter loops, nucleosome clutches, globular chromatin domains and nucleosomes . AlthougStructural maintenance of the chromosome (SMC) complexes  are crucial for the organization of interphase chromatin structures . PreviouDrosophila (The ZBTB transcription factors Mod (mdg4) and Cp190 are known to play critical roles in the regulation of insulator activities in osophila . The zinosophila ,58. Thesosophila . The majHere, we report that ZBTB21 facilitates the chromatin occupancy of cohesin and modulates 3D chromatin interactions and transcriptional repression. A number of ZBTB transcription factors  have been shown to interact with cohesin. The double depletion of ZBTB21 and ZBTB7B further decreases the chromatin occupancy of cohesin. These results imply that various ZBTB transcription factors facilitate cohesin chromatin binding in addition to performing transcriptional regulatory activities that modulate chromatin interactions in mammalian cells.l-glutamine , NEAAs , CHIR99021 , PD0325901 , β-mercaptoethanol  and mLIF  at 37°C with 5% CO2. To degrade the target proteins, we first treated the degron-tagged mESCs with 1 μg/ml doxycycline  for 24 h, then added 500 μM indole acetic acid  for the indicated times for different analyses. HEK293T  cells were cultured in DMEM  supplemented with 10% FBS  and PenStrep  at 37°C with 5% CO2.Mouse embryonic stem cells (mESCs)  were cultured in knockout Dulbecco’s modified Eagle’s medium , supplemented with 15% fetal bovine serum (FBS), PenStrep , nucleoside , The donors and single guide RNAs (sgRNAs) for the degron-tagged cells were generated as in the previous report . The homThe degron donors and corresponding sgRNAs were transfected into the mES-OsTIR1 cell line generated in a previous study . The tra2, 0.2 mM EDTA, 0.3 M NaCl, 1 M urea, 1% NP-40) to isolate chromatin. The chromatin pellets were resuspended in 1 ml of sonication buffer . Micrococcal nuclease  was added to the chromatin extracts to digest chromatin. The chromatin was further fragmented by sonication with biorupter with the setting at the position high 30 s on/60 s off 15 times. The 5% lysate was saved as input: half for the input of protein analysis and half for the input of DNA analysis. Different antibodies were added to the soluble chromatin extracts and incubated at 4°C with rotation overnight. The next day, Protein G Dynabeads  were added to the chromatin–antibody reaction and incubated for ∼2 h at 4°C. Protein G beads were washed with sonification buffer, high-salt buffer, LiCl buffer and TE buffer, respectively. The ChIP products were eluted from beads by heating at 65°C for 15 min. De-cross-linked DNA was used for ChIP-seq library preparation, and eluted protein products were used for mass spectrometry (MS) analysis. High-salt buffer , LiCl buffer  and TE buffer .The ChIP experiment was performed based on a previous publication . Brieflyin situ Hi-C is that BAT Hi-C employs a biotin-labeled bridge linker to mediate proximity ligation, while in situ Hi-C uses blunt end ligation. Briefly, the 1% formaldehyde-fixed cells were digested by AluI  for at least 12 h. Then, the restriction enzyme was inactivated by heat. The DNA overhangs were added with a base dA by Klenow (3′–5′ exo-) enzyme . The dA overhang DNA products were ligated with a bridge linker by T4 DNA ligase  at room temperature for at least 6 h. Then the chromatin fractions were isolated, and the unligated DNA was digested by lambda exonuclease. The ligated DNA was de-cross-linked at 65°C overnight. The ligated DNAs were purified, and then fragmented by sonication. The fragmented DNA was examined by 1% DNA agarose gel electrophoresis. The size of the DNA product was ∼0.2–2 kb. The ligated DNA was purified by biotin–streptavidin pulldown. The DNA library was constructed with the NEBNext®Ultra™II DNA Library Prep Kit for Illumina®. The 300–500 bp DNA products were purified by the Magen gel purification kit. The DNA was sequenced at Hiseq 4000, and 150 bp paired-end reads were obtained for downstream analyses.The BAT Hi-C was performed by following the protocol published previously . The majThe 4C-seq was described in a previous publication . BrieflyA total of 20 million native cells were used for immunoprecipitation experiments. A 1 ml aliquot of IP lysis buffer  was used to resuspend cell pellets by rotating the cell lysates at 4°C for 30 min. The supernatant (5% cell lysis supernatant was saved as an input sample) was collected. Then, different antibodies were added to the pre-cleared whole-cell lysates and incubated overnight with rotation at 4°C. The protein–antibody complexes were incubated with Protein G for ∼2 h at 4°C. The beads were washed by sonification buffer four times and by high-salt buffer once. The supernatant was discarded, and a suitable volume of 1× SDS loading buffer was added. The eluted IP samples were boiled at 100°C for 5 min. The protein elution and input samples were used for western blotting analyses. Then the samples were run on different concentration SDS–polyacrylamide gel electrophoresis (PAGE) gels. They were transformed onto nitrocellulose membranes and the membranes were blocked with skim milk and then incubated with diluted primary antibody (1:1000–1:5000) overnight at 4°C. On the next day, the membranes were washed and incubated with secondary antibodies (1:5000). The membranes were washed and analyzed on a G.E AI 600 RGB imaging system.The cell lines that express proteins of interest with GFP tags were imaged on 35 mm glass-bottom dishes  by Nikon A1R-si. Before imaging, the cells were digested by 0.25% trypsin EDTA  and transferred to the 35 mm glass-bottom dishes for ∼6 h. Then the cells were imaged by a confocal microscope (Nikon A1R-si). Hoechst 33342  was used to stain the nucleus. Laser: 405 nm, 488 nm.Input – CtSample). The results of ChIP-qPCR were displayed by prism 8. At least two replicates were run for each qPCR. The statistical significance was evaluated by Student's t-test. The primers for the ChIP-qPCR can be found in ChIP-quantitative PCR (qPCR) primers were designed and validated by NCBI primer-blast. The Ct value of input DNA was used for normalization. Calculation formula: %Input = 5%*2^  .ChIP DNA libraries were constructed according to the previously reported TELP library construction protocol  or the NFor RNA-seq, the ZBTB21-degron mESCs were induced by doxycycline for 24 h, and treated with IAA for 24 h. The untreated cells and IAA-treated degron cells were collected. RNA extraction was performed with Trizol by following the manufacturer's instructions. Our RNA-seq library was prepared by using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® , which is regularly used in our lab ,69,70. T2, 0.01% NP-40 and protease inhibitors) to lyse cell membranes. The cell suspension was transferred to a 1.25 ml sucrose cushion [24% sucrose (w/v) in NP-40 cell lysis buffer] to extract the nuclei. The supernatant (cytoplasmic fraction) was collected. The nuclei were gently resuspended with 0.5 ml of glycerol buffer . A 0.5 ml aliquot of nuclei lysis buffer  was added to the resuspended nuclei. After centrifugation, the supernatant was saved as a nucleoplasmic fraction. The pellets were chromatin fractions used in this study; 1× SDS loading buffer was added to the chromatin pellets and boiled at 100°C for 5 min. Western blot was used to examine the target proteins.The chromatin was prepared by following the protocol published previously . A totalFor ChAR-seq, the ZBTB21-degron mESCs were induced by doxycycline for 24 h, and treated with IAA for 6 h. The chromatin was prepared by following the chromatin fraction preparation protocol as described above. The chromatin RNA was extracted from chromatin fractions with Trizol by following the manufacturer's instructions. The total RNA samples were firstly depleted of rRNAs and were constructed into next-generation sequencing libraries by following the experimental procedure for RNA-seq library preparation. The ChAR-seq libraries were sequenced in the same way as for RNA-seq libraries.P-value < 0.05, log2 fold change (FC) > 1]  value for each protein. For mESCs, the candidate proteins were selected if the total peptide counts of the proteins in ChIP samples were at least twice more than those in input samples. For B16 and CH12F3 cells, the candidate proteins were identified by the R package DEP [FC) > 1] . The misFC) > 1] ,73, UnipFC) > 1] .https://github.com/nboley/idr) with an idr threshold of 0.05 to obtain repeatable peaks. Peaks that overlapped with the mm10 or hg19 blacklist were removed from the peak lists.Raw ChIP-seq reads were firstly trimmed using Cutadapt software  and thencis-regulated elements were defined as below:The genomic distribution of peaks was annotated according to the priority of promoters, enhancers, CTCF sites, gene bodies and others. The Promoters: ±1 kb from transcription starting site (TSS) of Refseq genes.Enhancers: H3K27ac peaks that did not overlap with promoters. Superenhancers were downloaded from a previous publication .CTCF sites: CTCF peaks that did not overlap with enhancers and promoters.n = 24 036, in Figure http://homer.ucsd.edu/homer/ngs/index.html) software. The genes regulated by each peak were assigned by the R package ChIPseeker  threshold of 0.05 to obtain more robust changes in 0–1, 0–6 and 1–6 h. These differential peaks were then merged and clustered using the R package hclust. For clustering analysis, a heatmap was used to visualize the changes of cohesin binding. Due to the high variability of cohesin binding signals at different regions, the z-score, which was calculated by (x – mean)/standard deviation (SD), was used to normalize the data in order to make the change patterns clearer. To determine the optimal cluster numbers, we used the ‘Elbow method’ to test a range of cluster numbers and finally selected three clusters to reflect the biologically meaningful changes.Peaks of ZBTB21, cohesin and CTCF ChIP-seq datasets in Figure Statistical tests used in this study were all determined by two-side Wilcoxon test or Student's test as mentioned in each figure legend.n = 34 M) and merged together between the two replicates (n = 68 M) to obtain higher resolution. The resolution of our Hi-C library is ∼25 kb, so the interactions <20 kb are below our Hi-C resolution, which are potential false-positive interactions. Therefore, we removed the interactions <20 kb in our analyses. Many previous publications also removed the short-distance interactions for subsequent analysis (https://github.com/4dn-dcic/hic2cool). The ‘.cool’ format Hi-C maps were balanced by cooler software  and adjusted by H3K27ac ChIP-seq signals. The saddle plots were computed by compute-saddle of Cooltools software at 50 kb resolution and normalized by the expected matrix in ‘–quantiles’ mode. The enrichment score was calculated as (AA + BB)/2AB, where AA refers to the mean normalized density of bins in the top 20% eigenvector scores, BB refers to the mean normalized density of bins in the bottom 20% eigenvector scores, and AB refers to the mean normalized density of bins between AA and BB.A/B compartments were called using call-compartments of Cooltools software  with ‘–rescale’ and ‘–local’ parameters. In general, this is similar to meta-gene analysis, commonly performed for ChIP-seq data. The positions of TADs were downloaded from Bonev et al. . Each TALoops were called in 5, 10 and 25 kb resolution using Mustache software  with parP-threshold of 0.05. ZBTB21, differential cohesin and GRO-seq signals were plotted by heatmaps centered around the ATAC-seq peaks in ZBTB21-responsive (n = 7973) and less-responsive (n = 1112) loops. GRO-seq signals were not classified into + or – strands due to the inability to distinguish ATAC-seq peaks between the + or – strands. APA plots of loops were calculated by Coolpup software at 25 kb resolution and normalized by the expected matrix. The enrichment score was computed as the mean normalized density of the central 3*3 bins.ZBTB21-responsive loops were defined as the top 500 loops that contained both ZBTB21 peaks and down-regulated cohesin peaks; less ZBTB21-responsive loops were defined as the bottom 500 loops that did not contain ZBTB21 peaks and down-regulated cohesin peaks. Loops were ranked by the sum of the ZBTB21 ChIP-seq signal and the absolute value of log2FC of cohesin ChIP-seq signal upon ZBTB21 degradation at the ATAC-Seq peaks inside each loop. Because the loop length distribution analyses indicated that loops >500 kb showed more changes, loops >500 kb were classified into ZBTB21-responsive and less-responsive loops. Down-regulated cohesin peaks were identified using cohesin ChIP-seqs before and after ZBTB21 degradation for 6 h by Diffbind software using a https://github.com/dekkerlab/cworld-dekker) at 25 kb resolution. The insulation scores of the CTCF Hi-C matrix were computed by cworld software at 20 kb resolution. CTCF Hi-C data were downloaded from a previous publication . Uniquely mapped reads without PCR duplication were kept for further analysis. The expression of the mm10 genes was counted by featureCounts software at gene body regions [+300 from the TSS to the transcription end site (TES)] and normalized by the spike-in reads mapped to the dm6 genome in each sample. DEGs were identified by the R package DEseq2  with an cis-elements and clustered based on the percentage of occupancy using the R package hclust. Gene expression data of the transcription factors in 53 different tissues were obtained from the GTEx database. All the samples were grouped and averaged by tissues. The expression percentage of each gene was defined as the percentage of tissues whose averaged transcripts per million (TPM) value was >10. CRISPR scores of human genes were obtained from a previous publication  and identified 260 and 1332 cohesin-interacting proteins in B16 and CH12F3 cells, respectively . Our cohesin ChIP-MS data were quite reproducible and were strongly enriched in known cohesin components  and NIPBL ChIP-seq after ZBTB21 depletion (Suppelementary Tables S1 and S4). The western blotting results confirmed the acute depletion of ZBTB21 proteins in our ZBTB21-degron mESCs Figure . We alsos Figure . The chrs Figure . Screenss Figure . We alsos Figure , suggestWe next carried out time-course cohesin ChIP-seq after ZBTB21 depletion to determine the dynamic effects on cohesin. The clustering analyses of the differential peaks indicated the existence of three clusters Figure , E: in CTo gain functional insights into ZBTB21 chromatin binding, we performed total RNA-seq and ChAR-seq to enrich nascent RNAs after ZBTB21 depletion . The difin situ Hi-C method. The Hi-C data were processed via previously established pipelines  using the ZBTB21 depletion Hi-C dataset. The results showed that ZBTB21 depletion increased the Hi-C interaction signals at promoters, enhancers, CTCF sites and superenhancers  has remained unclear. Many cofactors and chromatin regulators have been implicated in the formation of biomolecular condensates to mediate local chromatin structures ,115,116.et al., indicating that ZBTB21 may also function as a pioneer factor (similar to OCT4 and SOX2) to facilitate cohesin binding.We obtained 25 kb resolution Hi-C maps in the current study, which is not high enough. This could be why we did not observe robust effects on 3D chromatin loop changes. Higher resolution chromatin interaction mapping (such as Micro-C) and elucidation of the detailed protein−protein interactions between cohesin and ZBTB21 would be valuable to clarify the molecular mechanism of ZBTB21 function in the future. A previous study showed that transcription factors are enriched at local cohesin-binding sites, and the pioneer transcription factors OCT4 and SOX2 create an open chromatin conformation for cohesin binding . We idenDrosophila insulator proteins such as Mod (mdg4) and Cp190 are also ZBTB transcription factors and have been shown to function as regulators of insulator activities (Drosophila, which might prevent cohesin extrusion at critical chromatin regulatory elements. This potential mechanism, together with transcriptional repressor activity, would be important for the specific gene expression program during different developmental processes.ZBTB transcription factors have been reported to function in several critical developmental processes and diseases. For example, many ZBTB transcription factors, including ZBTB7A, ZBTB7B and ZBTB35, are required for the proliferation and differentiation of T cells ,121; ZBTtivities . Previougkad401_Supplemental_FilesClick here for additional data file.


The importance of myosin motor protein is well-characterised within the cytoplasm and cytoskeleton. However, mounting evidence on four nuclear myosins highlights the central role these proteins have in maintaining genomic stability and gene expression. This review focuses on each of their critical roles in chromatin structure, chromosome translocation, transcription regulation, and DNA damage repair in terms of maintaining chromosome and chromatin integrity. Myosins are found throughout eukaryotic cells and are a large superfamily of actin-dependent motor proteins. Conventional myosins are generally responsible for muscle contraction however, it is unconventional myosins which are more abundant with a larger breadth of functions and localisations. Currently, seven myosins have been identified within the nucleus, all roughly following the same conserved structure .Chromosome organisation and gene expression must be tightly regulated to ensure cell survival and genome stability. Dysfunction at any stage can compromise this stability and, in some cases, cause the development of diseases such as cancer . Four myDrosophila flies and mammals, NMI is shown to interact with the genome. NMI knockdowns provide evidence of its importance as it leads to the impairment of cell cycle progression, an increase in DNA damage, and an increase in apoptosis [The first myosin to be discovered in the nucleus was the myosin I isoform Nuclear Myosin I (NMI). Subsequently, two other isoforms were found: Myosin IA and IC. Being the first, NMI is the most characterised and researched. In both poptosis ,14. The Genetic information in eukaryotes resides in the nucleus in the form of chromosomes: 3D multi-layered structures of folded genome. The structure is divided into two distinct compartments depending on the type of chromatin. Within these compartments, the chromosome structure is further divided into topologically associated domains (TADs) ,15.Each chromosome occupies its own separate chromosomal territory that can be found throughout the nucleus. Gene-poor chromosomes are generally localised around the nuclear envelope and anchored through Lamin B interactions, whereas gene-rich chromosomes reside in a more central position . The terHeat shock confirms the interactions of Lamin A/C and NMI by demonstrating their role in the relocation of the Hsp70 gene locus towards the nuclear interior and SC35 nuclear specks to activate Hsp70 gene expression. In turn, recruiting molecular chaperones to protect the DNA ,17.Despite NMI function being intact under heat stress, senescence in human dermal fibroblasts (HDFs) shows deregulation in chromosome movement, mirroring results found in serum removal studies. Chromosome 10 is particularly influenced, being found near the nuclear periphery rather than the interior with some genes being down-regulated potentially permanently. One possible explanation could be the alteration of NMIβ localisation. Normally NMIβ can be found throughout the nucleus however, once an HDF cell enters G0 the widespread distribution is lost in favour of large aggregates in the nucleoplasm .TAD organisation is also influenced by NMI. Mouse embryonic stem cell models reveal the binding of NMI to the ISWI chromatin remodeller SNF2H and the Williams syndrome transcription factor (WSTF), forming the complex B-WICH. This complex aids in isolating specific TADs by enabling the recruitment of the insulator protein CTCF ,10.The functions of the B-WICH complex also involve the regulation of the 3D genome, with evidence of a focal role in mRNA and rRNA transcription. It's required for the function of RNA Polymerase I (RNAPI), II (RNAPII), and III (RNAPIII) .The interaction of B-WICH with RNAPI activates ribosomal transcription initiation through nucleosome organisation. B-WICH is responsible for the acetylation of H3 and H4, causing the tightly wound chromatin structure to change configuration, exposing a 200 bp region around the rDNA promoter. This occurs due to the recruitment of H3 histone acetyl-transferases (HATs), which directly interact with the NMI tail domain ,14. The 2 (PIP2) interactions with the NMI C-terminus [2 binding is through myosin phosphatase Rho-interacting proteins (MPRIPs) which are a part of the RNAPII/NM1 complex. MPRIPs have been shown to bind to nuclear actin via the N-terminus and can form phase-separated liquid-like condensates via the C-terminus. Both of these functions are speculated to regulate transcription [NMI is an important component as it forms part of the pre-initiation complex (PIC) and can control RNAPII activation ,2,21,22.terminus . Anothercription ,25.Unsurprisingly, the role of NMI in unwinding the chromatin structure at promoter sites for RNAPIII is the same. However, the targets are specifically around the 5S rRNA and 7SL RNA genes. c-Myc-Max subsequently binds and recruits necessary HATs alongside NMI . AdditioThe genome regulatory functions of the B-WICH complex expand to include DNA double strand break (DSB) repair. Breaks in the DNA can take different forms however, DSBs are the most severe and can form due to various factors including exposure to ionising radiation and chemotherapy drugs ,27. The Cdkn1A gene is activated to encode the protein p21. The p53 tumour suppressor is activated through a phosphorylation cascade when DSBs are recognised. Although the expression of p53 is not reliant on NMI, the ability of p53 to act as a transcription factor for p21 expression is, and B-WICH with RNAPII is required  family. The phosphorylated form pSer-139) is known as γH2AX and recruits the PI3KK ATM kinase, resulting in a feedback loop. The accumulation of γH2AX flanking the DSB recruits necessary chromatin remodellers and HR repair proteins, such as BRCA1, RAD51, and RAD52, to the lesion. There is evidence that γH2AX enables NMI-dependent chromosome movements for HR repair. The phosphorylated histone has no noticeable effect on chromatin-associated NMI when DSBs are induced using agents such as cisplatin. However, the inhibition of PI3KKs known to phosphorylate H2AX has an adverse effect on NMI recruitment. Likewise, the same reduction in NMI-chromatin interactions is seen when γH2AX is down-regulated, and both saw hindrance of chromosome movements 39 is kno.Myosin VI (MVI) has multiple isoforms arising from alternative splicing. The resulting variations are referred to by the absence/insertion of amino acid residues in the tail domain. Transcriptional regulation is controlled by the Non-Insert (NI) isoform which can be imported into the nucleus and is associated with cancer development when overexpressed . Both NMAlthough not as researched as NMI, the established roles of MVI include transcription initiation and elongation, gene expression and silencing, and chromosome movement. These functions are mostly in conjunction with RNAPII, protein binding partners, and hormone stimulation ,34.Regulation of MVI is controlled by the binding of the transcription co-activator protein NDP52 ,26 or thDuring initiation, MVI acts as an anchor for transcription factories. These stable clusters of RNAPII and activator proteins reside close to actively transcribed genes and mediate transcription by regulating chromatin interactions ,12,36. TTNF, TBX21, and TNFAIP8 require MVI for this process [The presence of transcription factories allows for RNAPII in a paused state to be loaded to gene promoter sites. Phosphorylation of RNAPII must occur by the Positive Transcription Elongation Factor (P-TEFb) and preliminary findings in T cells suggest that certain genes including  process .PS2 and GREB1 showed a reliance on MVI for gene transcription, with expression dropping by 70–80% and 20–30%, respectively, when ER was impacted using a luciferase assay [PS2 and GREB1 expression. This link hints at MVI having an important role in ER signalling and being somewhat regulated by hormone stimulation [The transcriptional control of MVI for specific genes also includes those regulated by androgen receptors (AR) and oestrogen receptors (ER) ,35. For se assay . ER-posimulation ,35.Gene expression mediated by MVI has been linked to two possible binding partners: Heterogenous Nuclear Ribonucleoprotein U (hnRNPU) and nucleolin. Little has been identified in terms of mechanism however, MVI was found to colocalise with both proteins in SC35 nuclear specks and PML nuclear bodies, suggesting a role in pre-mRNA processing and transport, rDNA transcription, rRNA maturation, and chromatin remodelling [TNF, TNFAIP8, and TBET alleles, as well as RNAPII binding to the gene promoter  and is commonly associated with intracellular cargo transportation and exocytosis . Much liBoth isoforms myosin Va and Vb are present in the nucleus of transcriptionally active cells, yet distribution differs slightly. Myosin Va is found throughout in small concentrations excluding the nucleoli, with high concentrations in SC53 nuclear specks, hinting at a possible role in mRNA slicing . TranscrIn contrast, myosin Vb localises in the nucleoli, specifically the dense fibrillar component region, where it associates with rDNA, newly synthesised rRNA, β-actin, and RNAPI ,42. MinoThe most researched myosin II is responsible for muscle contractions in skeletal, cardiac, and smooth muscle. However, it is the unconventional nuclear non-muscle myosin II (NMII) that, as the name suggests, resides in the nucleus . Like MVStem cell commitment requires the activation/silencing of specific genes and is regulated by tissue mechanics. The tissue mechanics necessary take the form of forces exerted on the cell such as compression between cells, and changes to tissue viscosity and stiffness. Mechanical strain has been shown to significantly reduce total mRNA and down-regulate ∼4000 genes. Transcriptional repression was put down to two interconnected factors: the Emerin-dependent compaction and rearrangement of chromatin, and the replacement of global H3K9me2,3 with H3K27me3 .Induced strain in epidermal stem cells coincided with an increase in NMII, F-actin polymerisation, and Emerin at the nuclear envelope, forming a mechanosensory complex ,45. EmerICAM-1 transcription [NMII has also been linked to the assembly of the PIC and the regulation of cription ,46. The The evidence put forward in this paper, along with the complexity of the transcription and gene expression process, starts to form a comprehensive picture of how all four of these nuclear myosins function in an interconnected system. These myosins have similar compositions, perhaps explaining the overlap and potentially revealing new functions that have yet to be explored.Although a lot of the mechanisms are yet to be fully established, the four myosins are linked to transcriptional regulation. Some mechanisms are unique to specific myosins such as NMI in B-WICH but both NMI and MVI appear to work simultaneously in transcription initiation and elongation. It would be interesting to verify if one model is preferential to the other. If one is removed, does the other compensate and if so, to what extent? mRNA transport and processing is another aspect that could be investigated as localisation in the SC53 nuclear specks seems to be a common crossover.Parallels can also be drawn between the components required for long-range chromosome movements which could lead one to speculate that the underlying mechanisms underlying are similar if not the same. Certain myosins might be responsible for the relocation of different chromosomes e.g. NMI for chromosome 1, 10, and 18, and MVI for chromosomes 2 and 21. Continuing this research could highlight which myosins influence which chromosomes, and how they work together.Chromatin rearrangements to both facilitate transcription and DNA damage repair are among the myosin roles with the least amount of research, with NMI and myosin V having the most established links. So far little overlap between the nuclear myosins have been seen in this regard except for both myosin V and NMI in HR repair. Many papers have looked into the transcriptional response of MVI to DNA damage and early findings have hinted at MVI being capable of chromatin rearrangement, therefore there could be a possibility of MVI also moving heterochromatin breaks or relocating chromatin for another purpose. Moreover, MVI localises on PML nuclear bodies further indicating a potential chromatin remodelling role.Finally, it may be beneficial to analyses the DNA binding sites of the nuclear myosins using techniques such as ChIP-Seq to assess specific gene interactions. Currently, only NMI and MVI have been analysed in such a way, with only NMI being sequences as a whole. This could discover new associations and also provide verification to some studies that have linked some myosins to specific genes e.g. MVI with ER-dependent genes.Myosins have previously been shown to work together in a wide range of cytoplasmic roles such as endocytosis . Given tThe correct organisation of the genome and regulation of gene expression are critical for cellular function and health. Nuclear myosins underpin this activity therefore their function and regulation need to be defined.Nuclear myosins form direct interactions with transcription regulators and RNA Polymerases. They act to support activity of regulators and can physically hold RNA polymerase complexes at promoters. Moreover, nuclear myosins enable movement of chromosomes.The molecular detail on the action of nuclear myosin is required to understand their function. Moreover, information needs to be integrated between gene expression, DNA repair, replication and chromatin organisation fields with the cytoskeletal field to identify wider myosin functions within the nucleus.


FOXC1 as the most significantly enriched binding site for estrogen receptor alpha (ERα) in osteoblasts. FOXC1 is a transcription factor belonging to a large group of proteins known as forkhead box genes and is an important regulator of bone formation. Here, we demonstrate that 17β-estradiol (E2) increases the mRNA and protein levels of FOXC1 in primary mouse and human osteoblasts. GATA4 is a pioneer factor for ERα and it is also recruited to enhancers near Foxc1. Knockdown of Gata4 in mouse osteoblasts in vitro decreases Foxc1 expression as does knockout of Gata4 in vivo. Functionally, GATA4 and FOXC1 interact and regulate osteoblast proteins such as RUNX2, as demonstrated by ChIP-reChIP and luciferase assays. The most enriched motif in GATA4 binding sites from ChIP-seq is for FOXC1, supporting the notion that GATA4 and FOXC1 cooperate in regulating osteoblast differentiation. Together, these data demonstrate the interactions of the transcription factors ERα, GATA4, and FOXC1 to regulate each other’s expression and other osteoblast differentiation genes.Estrogens, which bind to estrogen receptor alpha (ERα), are important for proper bone mineral density. When women go through menopause, estrogen levels decrease, and there is a decrease in bone quality, along with an increased risk for fractures. We previously identified an enhancer near  Transcription factors bind to enhancers and promoters to regulate gene expression. Tissue-specific gene regulation is controlled in part by the spatiotemporal and combinatorial binding of transcription factors. There are many known transcription factors in bone, including RUNX2, estrogen receptor alpha (ERα), GATA4, and FOXC1. Herein, we describe the transcriptional regulation of Foxc1 and Runx2 in bone that is mediated by ERα, GATA4, and FOXC1.Estrogens are important for maintaining bone mineral density in both mice and humans via estrogen receptor alpha (ERα) . EstrogeGata4 in osteoblasts using the Prx1, Runx2, or OCN (Bglap) promoters driving Cre-recombinase leads to trabecular bone loss [Runx2, bone sialoprotein, and osteocalcin [GATA transcription factors, so named because they bind to the consensus DNA sequence (A/T)GATA(A/G), have been identified as important regulators of tissue-specific gene expression during development. GATA4 is necessary for heart, liver, and osteoblast differentiation, among other tissues . Bone-spone loss ,7,8. GATeocalcin .A pioneer factor is a protein that can bind to the genome in sites of condensed chromatin, leading to nucleosome remodeling and the opening of chromatin, the recruitment of additional transcription factors, and the activation of transcription . GATA4 hFOXC1 in humans cause Axenfeld–Rieger Syndrome, characterized by ocular defects but also skeletal abnormalities [ch/ch because it has congenital hydrocephalus) dies at birth and has skull and axial and appendicular bone defects [Foxc1 and Foxc2 leads to disrupted endochondral ossification and skeletal dysplasia [Runx2, the master transcription factor for osteoblastogenesis [There are 50 human and 44 mouse forkhead proteins, categorized from FoxA to FoxS . FOXC1 hmalities . A spont defects ,16, demoysplasia . Mechaniogenesis ,19.Foxc1 regulation and functions for FOXC1 in osteoblasts. Importantly, GATA4 and FOXC1 interact in osteoblasts to directly regulate Runx2.For the first time, we identified key regulators of Animal experiments were approved by the Institutional Animal Care and Use Committee at the University of Tennessee Health Science Center. Animals were maintained in a specific pathogen-free environment at 20–26 °C with a relative humidity of 30–70% and a 12 h light/dark cycle. Commercial rodent chow  and drinking water were available ad libitum.GATA4-Flag-biotin mice 26Sortm1(birA)Mejr Gata4tm3.1 Wtp/J)  were obtSix-week-old female BALB/c mice were sham-operated or ovariectomized and then treated with 50 mg/kg body weight of E2 or sesame oil (as a control) by intraperitoneal injection for 24 h.+/−ERβ+/− mice were bred to produce WT, ERαKO, and ERαβKO littermates.ERα, ERβ, and ERαβ heterozygous mice were kindly provided by Dr. Pierre Chambon . ERα+/−EMouse calvarial osteoblasts were isolated from 2-day-old CD1 or Flag-bio mice by sequential collagenase digestion . The celThe human study was approved by the Institutional Review Board at the University of Tennessee Health Science Center, and all individuals provided informed written consent before participation. Primary human osteoblasts were isolated from the trabecular bone in the femoral or humeral heads of individuals who underwent total joint replacement surgery, as described previously .Human osteosarcoma U2OS-ERα cells were kindly provided by Dr. Thomas Spelsberg and were maintained as described . ERα expGata4  and shGATA4 were purchased from Sigma Aldrich. Two different shRNAs from the RNAi Consortium (TRC) in the pLKO vector were used to knockdown mouse Gata4 was confirmed by qPCR.CCGGCCCAATCTCGATATGTTTGATCTCGAGATCAAACATATCGAGATTGGGTTTTTG and TRCN0000095217: CCGGCATCTCCTGTCACTCAGACATCTCGAGATGTCTGAGTGACAGGAGATGTTTTTG). The knockdown of Actb) values.RNA and qPCR were performed as previously described . The speImmunoprecipitation (IP) reactions were conducted on whole-cell lysates prepared in standard RIPA buffer extracted from calvarial osteoblasts. IPs were performed with antibodies to GATA4  or normal mouse IgG, along with Protein G magnetic beads . Immunoblotting was performed with antibodies to FOXC1  and β-actin . Whole blots are shown in ChIP was performed as previously described for U2OS cells  and FlagChIP was performed as described above, except that after the first overnight incubation with the first antibody, the magnetic beads were washed 3 times with PBS/BSA and then resuspended in TE with 10 mM DTT for 30 min. The eluate was resuspended in ChIP dilution buffer, and the second antibody was added overnight.Formalin-fixed, paraffin-embedded samples were processed through standard deparaffinization protocols. Antigen retrieval was performed by placing the slides in 90 °C citrate buffer and leaving them at room temperature until the temperature reached 55 °C. The tissue was then incubated in blocking buffer  for 30 min. Anti-FOXC1  antibodies were incubated overnight at 4 °C in a humidified chamber, followed by the DAKO Envision Visualization system  and counterstaining with hematoxylin. Immunohistochemistry samples were scored without bias by three independent people using a bone-specific immune reactive scoring index of 0 (negative) -12 (strongly positive), multiplying the percent of positive cells by the intensity of staining .Runx2-promoter-luciferase (a kind gift from Dr. Gary S. Stein) [Gata4 (a kind gift from Dr. Michael Parmacek [Foxc1 was purchased from GenScript.U2OS osteosarcoma cells were plated in 24-well tissue culture dishes and transfected with the rat 0.6 kb . Stein) , pcDNA3-Parmacek ), and pRFoxc1. The datasets that were used are listed below in the Data Availability section.The Cistrome Data Browser ,30 was uFOXC1 in Human Mesenchymal Stem Cells from old, age-matched osteoporotic and non-osteoporotic individuals was obtained in the GEO dataset GSE35959 [The expression of GSE35959 .t-test, were performed using GraphPad Prism®  software.All experiments represent both biological and experimental triplicates. Unless otherwise stated, error bars represent the mean ± 1 standard deviation. Statistical analyses, including Student’s FOXC1 and FOXC1 has been shown to be important for osteoblast biology [FOXC1 in bone cells. All of the papers investigated murine Foxc1 regulation. SOX9 was shown to bind 43 kb upstream of Foxc1  treatment . U2OS-ER biology ,19,33. T site C, A,B by ChFoxc1 in MSCs, osteoblasts, and chondrocytes. We identified additional enhancers near Foxc1 bound by CEBPα, CEBPβ, DLX5, RUNX2, RXR, and VDR  and GATA4 ChIP-sequencing data in combination with publicly available ChIP-seq data obtained from the Cistrome Data Browser for additional transcription factors that bind near  and VDR A,B. EachFoxc1, primary mouse calvarial osteoblasts were treated with 10 nM E2 for 45 min., and ChIP was performed. qPCR primers were designed for these predicted enhancers and the promoter of Foxc1. Indeed, after E2 treatment, ERα was enriched at enhancers A, B, and E , and ERα/ERβ double knockout (ERαβKO) mice. FOXC1 staining was quantified by the number of positive cells and the intensity of staining. FOXC1 was markedly reduced in the ERαKO and ERαβKO mice  or sham-operated and then treated with vehicle or E2 for 24 h. Immunohistochemistry was performed with an antibody to FOXC1. FOXC1 is increased in the E2-treated osteoblasts found along the trabecular bone D. To demβKO mice .Foxc1 expression by binding to Foxc1 enhancers.Together, these experiments demonstrate that E2 and ERα directly up-regulate Foxc1, ChIP was performed to detect GATA4 at the enhancers near Foxc1. Because of inferior antibodies to GATA4, a mouse with GATA4 tagged with the Flag epitope and a biotinylation site at the endogenous locus was created (GATA4 Flag-bio mice) and has been successful for GATA4 ChIP [We have previously demonstrated that GATA4 is a pioneer for ERα in osteoblasts . To deteTA4 ChIP ,7,8. CalTA4 ChIP , as evidFoxc1, wild-type calvarial cells were cultured and knockdown of Gata4 was performed with lentiviral shRNA to Gata4 [Gata4 correlated with a decrease in Foxc1 mRNA at day 0 of differentiation, but not at day 14, (Gata4 in osteoblast progenitors (using PRX1-CRE) causes osteopenia [Gata4 cKO mice have lost nearly all expression of FOXC1.To further demonstrate that GATA4 regulates to Gata4 . Knockdo day 14, B, which teopenia . ImmunohFoxc1 expression by binding to Foxc1 enhancers.Together, these experiments demonstrate that GATA4 in vivo and in vitro directly up-regulates p value < 3.95 × 10−100) in the 5983 identified GATA4 binding sites is a canonical forkhead motif. Simple Enrichment Analysis (SEA) [p value < 3.15 × 10−15, Analysis of our GATA4 ChIP-seq data  using Anis (SEA)  also ideRunx2 and to a downstream enhancer (Runx2 enhancer 1 and on both promoters 1 and 2 (Runx2 promoters and an enhancer at the same time (Runx2-luciferase plasmid containing the osteoblast-specific P1 promoter [p < 0.01)  E. These FOXC1 expression not only in murine calvarial osteoblasts  individuals  are upregulated by FOXC1 [FOXC1 is critical to bone development as it controls endochondral and intramembranous ossification ,15,16,17Runx2 , β-catenRunx2 ,37, and x2 [Msx2 , all impE1 cells . FOX proE1 cells . We showpression , while Gby FOXC1 ,19. ThusRUNX2, ALPL, and BGLAP [Foxc1 by binding to enhancers in mouse osteoblasts. Mechanistically, activation of ERα increases its binding and the expression of FOXC1, while GATA4 knockdown abolishes the binding of ERα at the FOXC1 enhancer in U2OS-ERα cells [Foxc1. Binding alone is not sufficient for transcriptional regulation [Foxc1 mRNA. Furthermore, chromatin conformation assays would reveal additional complexity of the Foxc1 locus.GATA4 and ERα show synchronized actions on osteoblast differentiation by activating the expression of nd BGLAP ,6. GATA4nd BGLAP . We elucgulation , so funcFOXC1 shows high expression in osteosarcoma and promotes cancer progression and metastasis [RUNX2 also has high expression in osteosarcoma and correlates with metastasis and poor survival [Osteosarcoma is a bone cancer that can be caused by osteoblast-committed cells with differentiation defects . FOXC1 stastasis ,40. RUNXsurvival ,42. ThisRunx2 expression by interacting with and binding to the Runx2 enhancer and its two promoters. Furthermore, ERα and GATA4 control the expression of Foxc1, in addition to other transcription factors and cofactors.In conclusion, we show that GATA4 and FOXC1 directly promote