The myometrium is the outer layer of the uterus. Its contraction and steroidogenic activities are required for embryo implantation. However, the molecular mechanisms underlying its functions remain unknown in pigs. The myometrium includes the inner circular muscle (CM) and the outer longitudinal muscle (LM) layers. In this study, we collected the CM and LM samples from the mesometrial side (named M) of the uterus on days 12 (pre-implantation stage) and 15 (implantation stage) of pregnancy and day 15 of the estrous cycle. The transcriptomic results revealed distinct differences between the uterine CM and LM layers in early pregnancy: the genes expressed in the LM layer were mainly related to contraction pathways, whereas the transcriptional signatures in the CM layer on day 15 of pregnancy were primarily involved in the immune response processes. Subsequent comparisons in the CM layer between pregnant and cyclic gilts show that the transcriptional signatures of the CM layer are implantation-dependent. Next, we investigated the genome-wide profiling of histone H3 lysine 27 acetylation (H3K27ac) and histone H3 lysine 4 trimethylation (H3K4me3) in pig uterine CM and LM layers. The genomic regions that had transcriptional activity and were associated with the expression of genes in the two layers were characterized. Taken together, the regulatory regions identified in the study may contribute to modulating the gene expression in pig uterine CM and LM layers during implantation. Litter size is one of the most economic traits in swine production. Multiple factors and processes determine the pregnancy outcome. Success in implantation and embryo development is required to achieve a large litter size. The uterus plays a fundamental role in implantation and embryo development during pregnancy. Structurally, the uterus consists of three layers: the endometrium, myometrium, and perimetrium . An endoMuch progress has been made toward understanding the physiological mechanisms of myometrial activity in implantation. In polytocous species such as rabbits, mice, rats, and pigs, it is ideal that embryos can implant evenly along the uterine horns. Thus, intrauterine embryo spacing is a common event during pre-implantation. The mechanical force generated by the myometrial activity of the uterus is essential in the migration of embryos in the uterus ,10,11. IThe myometrium includes the inner circular muscle (CM) layer and the outer longitudinal muscle (LM) layer ,19,20. Tcis-regulatory elements marked by H3K4me3 and H3K27ac in the pig genome [Gene regulation involves multiple factors, such as transcription factors, cofactors, non-coding RNAs, and histone modifications ,35. Histg genome ,46,47,48g genome . HoweverIn the present study, we collected the myometrial samples (including the CM and LM layers) from pregnant pigs (days 12 and 15) and cyclic pigs (day 15) and carried out the RNA-seq and ChIP-seq. For the first time, this study (1) characterized the transcriptional signatures in pig uterine CM and LM layers during implantation, and (2) identified the regulatory regions important to gene expression in the two layers during implantation. The data will provide valuable resources for understanding the mechanisms involved in implantation and identifying functional variants in the pig genome.n = 3 gilts), and (2) the rest of the gilts were naturally mated with boar at the onset of the second estrus (Day 0), and again 12 h later, and uterine samples were collected on days 12 and 15 of pregnancy (n = 3 gilts/day). The uterus was quickly placed on ice and cut into 10–15 cm segments. The uterine segments were flushed with cold RNase-free phosphate-buffered saline (PBS), and pregnancy was confirmed by the presence of conceptuses in uterine flushing. The uterine samples not flushed with PBS were embedded in precooled optimum cutting temperature (OCT) compound , snap-frozen in liquid nitrogen, and stored at −80 °C. Next, the endometrium from the mesometrial (named M) side was removed with sterile scalpel blades at −30 °C. Subsequently, the circular muscles (named CM) and longitudinal muscles (named LM) were carefully separated and collected, respectively. All samples were stored at −80 °C.All animal procedures were performed according to the protocols approved by the Huazhong Agricultural University Ethics Committee (HZAUSW-2016-015). The estrus behavior of Yorkshire gilts was observed using healthy boar. Then, (1) uterine samples were collected from the gilts on day 15 of the estrous cycle (n = 3 gilts each). The sample types included: (1) CM and LM collected from the mesometrial side of the uterus on day 12 of pregnancy , (2) CM and LM collected from the mesometrial side of the uterus on day 15 of pregnancy , and (3) CM and LM collected from the mesometrial side of the uterus on day 15 of the estrous cycle . Total RNA was isolated using the HP Total RNA Kit  according to the manufacturer’s instructions. The quality, concentration, and integrity of the total RNA were determined using the NanoDrop spectrophotometer , Qubit 2.0 Fluorometer , and Bioanalyzer 2100 system , respectively. A total of 18 libraries (n = 3 gilts per sample type) were constructed for RNA sequencing using the Illumina HiSeq X Ten (PE150) platform . The reads were mapped to the Sus scrofa genome assembly 11.1 (ENSEMBLE: ftp://ftp.ensembl.org/pub/release-93/gtf (accessed on 24 June 2021)) with HISAT2 (v2.1.0) [p-values. Genes with adjusted p-value < 0.05, |log2foldchange| > 1 and base mean > 100 were identified as DEGs. The Venn analysis of the DEGs was performed using Venny (v2.1.0) (https://bioinfogp.cnb.csic.es/tools/venny/index.html (accessed on 14 December 2022)). Gene ontology (GO) analysis for the DEGs was carried out using DAVID Bioinformatics Resources (2021 Update) (https://david.ncifcrf.gov/ (accessed on 16 May 2022)) [The uterine circular muscles (CM) and longitudinal muscles (LM) were collected from the mesometrial side (named M). Six types of pig myometrium samples were used for RNA-seq (X Ten PE1 platform(v2.1.0) . Informa(v2.1.0) . The DEGy 2022)) .® qPCR SYBR Green Master Mix  in a Bio-Rad CFX384 Touch Real-Time PCR Detection System . The paired or unpaired Student’s t-test in R (v4.0.5) was used for statistical analysis. A p-value of < 0.05 was considered statistically significant.Quantitative real-time PCR (qRT-PCR) was carried out to validate the RNA-seq data in the same RNA samples used for RNA sequencing. The total RNA was reverse-transcribed into cDNA using FastKing gDNA Dispelling RT SuperMix  according to the manufacturer’s instructions. The gene-specific primers are listed in n = 2 gilts for each type) were ground to a fine powder in liquid nitrogen and processed using the Cleavage Under Targets and Tagmentation (CUT&Tag) method [2, 30 mM HEPES (pH 7.5), and 1 mM CaCl2). After discarding the supernatant, 10 µL binding buffer was re-added to equilibrate the beads. Approximately 100,000 nuclei were resuspended in wash buffer, mixed with equilibrated Concanavalin A beads, rotated, and incubated for 15 min at room temperature. Then, the beads were resuspended in a mixture containing 0.05% digitonin, 1.5 mM EDTA, and a 1:50 diluted primary antibody, rotated, and incubated for 3 h at room temperature. After incubation, the beads were washed three times with Dig-wash buffer , 0.5 mM spermidine, 0.05% digitonin, and protease inhibitor), and incubated with pA-Tn5 (1:100) in 100 µL Dig-300 buffer , 0.5 mM spermidine, 0.01% digitonin, and protease inhibitor) for 1 h at room temperature. Next, the beads were washed three times with Dig-300 buffer, resuspended in 100 µL tagmentation buffer (1 mM MgCl2 and Dig-300 buffer), and incubated on a ThermoMixer at 37 °C for 1 h. After discarding the supernatant, the beads were suspended in 150 µL elution buffer and incubated on a ThermoMixer at 65 °C for 1 h. Similarly, after discarding the supernatant, 250 µL TE buffer and protease K were added to the beads and incubated on a ThermoMixer at 55 °C for 6 h. After incubation, 400 µL saturated phenol was added to the supernatant, mixed well, and centrifuged at 15,000 rpm for 5 min at room temperature. After this, 5 M NaCl, glycogen, and precooled 100% ethanol were sequentially added to the supernatant to precipitate the DNA, eluted with precooled 75% ethanol and solubilized with 30 µL ddH2O. The immunoprecipitated DNA and immunoprecipitated control (IgG) were purified. ChIP-seq libraries were constructed following the manufacturer’s instructions for Illumina, and were sequenced on an Illumina NovaSeq PE150 platform .To investigate the regulatory mechanisms underlying the pregnancy-dependent transcriptional changes on day 15, we examined the genome-wide profiling of H3K27ac and H3K4me3 in three types of myometrium samples . The samples  method ,54 with Sus scrofa genome assembly 11.1 with BWA (v0.7.17-r1188) [The clean data were mapped to the 7-r1188) . The dup7-r1188)  and Pica7-r1188)  with 3 k7-r1188) . The enr7-r1188)  and ChIP7-r1188) . The mod7-r1188) , and reghttps://jaspar.genereg.net/ (accessed on 10 August 2022)) [p-value < 0.05 for the Student’s t-test was considered significant.The binding sites of transcription factors in the differential histone modification regions within the TSS ± 1 kb region of the DEGs were predicted by the JASPAR database (2022) (t 2022)) . A dual-The transcriptome data were obtained from four types of myometrium samples  . The hieTherefore, the GO analysis was restricted to the DEGs identified between the two layers on days 12 and 15 of pregnancy. Briefly, on day 12 of pregnancy, in addition to the common pathways enriched in the genes upregulated in the two layers, smooth muscle contraction pathways were mainly enriched for genes upregulated in the LM layer other than the CM layer B. On theWe next investigated whether the transcriptional signatures in GD15M_CM were associated with conceptus implantation. The analyses were performed by integrating the transcriptional data of the uterine smooth muscles (including LM and CM) from the M side of the uterus on day 15 of pregnancy and day 15 of the estrous cycle. The hierarchical clustering and principal component analysis indicated the discrimination among the different types based on the transcriptome data . SubsequADRA2A , one of the 9 adrenergic and 18 cholinergic receptor genes, was highly expressed in the LM layer , PLCB4 (Phospholipase C beta 4), ITPR1 , IRAG1 , CAMKK1 , MYL9 (Myosin light chain 9), and PPP1R14A (Protein phosphatase 1 regulatory inhibitor subunit 14A). Consistently, the qRT-PCR confirmed the expression patterns of the genes listed above, and also the genes of MYH11 (Myosin heavy chain 11) and SMTN (Smoothelin), two smooth muscle contraction marker genes [Our RNA-seq data shows that LM layer A. ADRA2A pathway ,64. As eseq data B. The geer genes ,66 , IRF1 (Interferon regulatory factor 1), STAT1 , STAT2 , and NLRC5 (NLR family CARD domain containing 5); (2) Major histocompatibility complex (MHC) class I-related genes, including PSMB8 (Proteasome 20S subunit beta 8), PSMB9 (Proteasome 20S subunit beta 9), PSMB10 (Proteasome 20S subunit beta 10), TAP1 , and B2M (Beta-2-microglobulin); (3) MHC class II-related genes, including CIITA (Class II major histocompatibility complex transactivator), SLA-DMA , SLA-DMB , and SLA-DRB1 (MHC class II histocompatibility antigen SLA-DRB1); and (4) T cell-related genes, including CD3E (CD3 epsilon subunit of T cell receptor complex), CD3D (CD3 delta subunit of T cell receptor complex), CD8A (CD8a molecule), LCK , and ZAP70 (Zeta chain of T cell receptor-associated protein kinase 70). Expectedly, the qRT-PCR results confirmed that those genes were expressed at significantly higher levels in the GD15M_CM  , focusinp = 9.72 × 1027; p = 1.98 × 1010; ADRA2A, CAMKK1, CAMKK2, and MYL9, showed increased expression and enrichment of H3K4me3 marks . Specifically, many genes, such as IRF1, GBP1, NLRC5, PSMB9, SLA-DMB, and CD3E, showed increased expression and enrichment of H3K4me3 marks , CD8A, CD3E, GBP1, PSMB9, SLA-6 (MHC class I antigen 6), and NLRC5, were significantly or extremely significantly elevated. Subsequently, we predicted the binding sites of transcription factors in the regions. IRF1, NLRC5, GBP1, and PSMB9, have a STAT1  binding site, while GBP1 and PSMB9 contain the IRF1 (Interferon regulatory factor 1) binding site as well.Furthermore, we examined whether the differential H3K4me3 modification regions within ±1 kb from the TSS had activity to promote the expression of immune response processes-related genes. Dual-luciferase reporter assays were performed with empty vector (pGL3-Basic) and target gene vectors. Pig embryo implantation is a complex process during which coordinated embryo–uterine interactions are essential. The myometrium, one of the layers of the uterus, is a type of involuntary non-striated muscle. Its contractility helps the embryos enter the uterine horn from the oviductal-uterine junction and evenly distribute and space out along the longitudinal axis before their implantation sites are finally fixed. In addition, the myometrium also has an endocrine function required for establishing implantation ,10. ThusThe uterine myometrium consists of two layers: the circular muscle (CM) and longitudinal muscles (LM). Previously, Kurowicka et al. reported that the contractility of the LM layer is significantly higher than that of the CM layer in early pregnant (days 14–16) and cyclic (days 14–16) pigs . In addiADRA2A, encoding the α2-adrenoceptor subtype, is the predominantly expressed receptor gene in the LM layer. α2-adrenoceptor belongs to the G protein-coupled receptor family, which regulates smooth muscle cell contraction by acting through the phosphatidylinositol signaling pathway to activate the calmodulin signaling pathway [MYH11 and SMTN) were also expressed at a higher level in the LM layer. The findings suggest that the α2-adrenoceptor-mediated signaling may contribute to the contraction function of the LM layer in pregnant and cyclic pigs. Noradrenaline is a natural ligand for α-adrenoceptors. Many studies in model animals have demonstrated that interfering adrenergic signaling during early pregnancy can disrupt embryo distribution along the longitudinal axis, leading to embryo loss [The sympathetic system modulates uterine contraction. Noradrenalin and cholinergic regulate uterine contraction by acting directly through the corresponding receptors in the myometrium during pregnancy ,71. This pathway ,64. Consryo loss ,73,74. TThis study found that genes related to immune response processes were upregulated in the CM layer on day 15 of pregnancy. On the other hand, we provided evidence to show that the transcriptional signature appears at the early embryo attachment stage and is implantation-dependent. The immune response processes identified include the cellular response to interferon-gamma, antigen processing, and presentation. Major histocompatibility complex (MHC) classes I and II are involved in processing endogenous and exogenous antigens and presenting antigens to the antigen-presenting cells . IFN-gamcis-regulatory elements (CREs), including promoters and enhancers, which can be identified with techniques such as ChIP-seq [As described above, we found that the LM and CM layers have distinct transcriptional signatures at the early attachment stage, and we examined the gene regulatory mechanisms underlying the difference. The regulation of gene expression is primarily controlled by ChIP-seq . Our gencis-regulatory elements have been determined using the ChIP-seq technique. Many cis-regulatory elements significantly contribute to phenotypic variation in complex traits [ADRA2A and some immune response genes identified in the study. A recent study on subjects with schizophrenia characterized that a promoter region in ADRA2A, epigenetically modified by both H3K4me3 and H3K27me3, could modulate the expression of ADRA2A in the dorsolateral prefrontal cortex [ADRA2A was highly expressed in the LM layer, and H3K4me3 was significantly enriched in its proximal promoter (≤1 kb). Thus, the proximal promoter we identified might contribute to modulating the expression of ADRA2A in the pig LM layer during implantation and is worthy of further study. Meanwhile, the promoters marked by H3K4me3 were identified in some immune response genes highly expressed in the pig CM layer on day 15 of pregnancy. Moreover, we validated the transcriptional activity of the promoters in several genes, including LCK, SLA-DMB, IRF1, C3, CD8A, CD3E, GBP1, PSMB9, SLA-6, and NLRC5. Cis-regulatory elements are non-coding DNA regions with transcriptional regulatory activity. A typical cis-regulatory element consists of binding sites for transcription factors and other associated cofactors that determine its function [STAT1 is a member of the STAT protein family. It can be activated by various ligands (such as IFN-gamma) and then translocated to the cell nucleus to drive the expression of the target genes. Many target genes of STAT1 have been identified, including IRF1, GBP1, NLRC5, PSMB9, and CIITA [STAT1 gene expression was upregulated in the CM layer on day 15 of pregnancy and detected the STAT1 binding site in the promotor regions identified in IRF1, GBP1, NLRC5, and PSMB9. On the other hand, IRF1 is a member of the interferon regulatory transcription factor family. In agreement with previous reports that GBP1 and PSMB9 could be regulated by IRF1 [GBP1 and PSMB9 contain the IRF1 binding site. It is worth noting that STAT1, IRF1, GBP1, and PSMB9 are IFN-gamma response genes, and NLRC5 is a specific transactivator of MHC class I genes that are highly inducible by IFN-gamma stimulation [cis-regulatory elements that could interact with STAT1 and IRF1, which in turn upregulate the expression of the genes, such as GBP1, NLRC5, and PSMB9.In humans and other animals, a large number of x traits ,83,84. Wl cortex . Consistfunction . STAT1 ind CIITA ,88,89,90 by IRF1 ,92, thismulation . TherefoIn conclusion, this study, for the first time, characterized the transcriptome and histone modification profiles for H3K4me3 and H3K27ac in pig uterine CM and LM layers during implantation. In addition, the distinct molecular features between the two layers were identified. Furthermore, the study identified the regulatory regions marked by H3K4me3 with transcriptional activity in the two layers.


Pancreatic β-cell maturity and function are regulated by a variety of transcription factors that enable the adequate expression of the cellular machinery involved in nutrient sensing and commensurate insulin secretion. One of the key factors in this regulation is MAF bZIP transcription factor A (MafA). MafA expression is decreased in type 2 diabetes, contributing to β-cell dysfunction and disease progression. The molecular biology underlying MafA is complex, with numerous transcriptional and post-translational regulatory nodes. Understanding these complexities may uncover potential therapeutic targets to ameliorate β-cell dysfunction. This article will summarize the role of MafA in normal β-cell function and disease, with a special focus on known transcriptional and post-translational regulators of MafA expression These factors have roles in development and terminal differentiation across multiple tissues [Pancreatic β-cells are critical in maintaining euglycemia by responding to blood glucose oscillations. β-cell dysfunction is a feature of diabetes mellitus which is characterized by abnormally high blood glucose levels. β-cells are responsible for the synthesis, storage and secretion of insulin, a hormone that is paramount for metabolic homeostasis. One hundred years after the successful isolation of insulin by Banting and collaborators, studies on insulin and β-cells have determined many of the factors needed for normal β-cell function, which is best defined by the appropriate expression of canonical β-cell factors involved in glucose-stimulated insulin secretion. One of these factors is the MAF bZIP transcription factor A (MafA). MafA, a large basic leucine zipper (bZIP) transcription factor (TF), derives its name from the viral oncogene  chicken . The sev tissues ,3,4, but tissues . For ins tissues ,7,8 and  tissues ,10. ThesHere, we review the transcriptional and post-transcriptional regulation of MafA, with a focus on post-translational modifications (PTMs) and their effects on MafA stability and transcriptional activity. In addition, this review will discuss β-cell transcriptional targets of MafA. Other MAF factors and the role of MafA in non-β-cells are reviewed elsewhere . BecauseIns1, Ins2, Pdx1, Neurod1 and Slc2a2, suggesting that MafA is essential for the transcriptional identity of β-cells. MafA works synergistically with Neurod1 and Pdx1 to transactivate the insulin promoter, which is an effect that could not be fully replicated with MafB nor Maf [The importance of MafA for glucose homeostasis was shown in MafA-deficient mice, which displayed glucose intolerance after weaning due to impaired glucose-stimulated insulin secretion (GSIS) and age-dependent diabetes progression ,12. MafA nor Maf . Along t nor Maf  and when nor Maf  or in ot nor Maf ,17.Mafa expression is low at birth, increasing during the postnatal period [∆pancMafa), the effects of MafA loss are not apparent until three weeks of age, when the KO mice showed lower β-cell mass, decreased insulin expression, and impaired glucose tolerance compared to controls [Ins1 and Ins2 transcription [Gcg gene expression [Mafb transcriptional start site [∆pancMafb mice have higher blood glucose levels at P1, but two weeks after birth, blood glucose levels normalize. Likewise, at E15.5, ∆pancMafb mice have a lower number of insulin+ and glucagon+ cells compared to controls, but two weeks after birth, cell numbers became roughly the same [Several studies suggest that MafA is dispensable for β-cell development but plays an essential role in β-cell maturation and glucose responsiveness of adult β-cells. In rodents, l period ,19. Intel period ,21, suggcontrols . Similarcontrols . These rcontrols . MafB cacription , but in pression ,23. The art site . Mafb∆pathe same . MafB dothe same ,26, but the same .MAFA mRNA expression increases postnatally in an age-dependent manner [The MAFA expression pattern in humans is similar to that observed in rodents. In human β-cells, t manner . In contt manner , and MAFt manner . Howevert manner .db/db mice, MafA is decreased due to hyperglycemia-associated oxidative stress and c-Jun activity [MAFA mRNA levels and protein expression [db/db mice, GSIS and β-cell mass improved, suggesting that preserving MafA expression in β-cells can still mitigate diabetes progression [MAFA/MAFB had increased expression of genes related to β-cell identity, glucose metabolism and exocytosis compared to β-cells that only expressed one or neither TF [2+ fluxes and impaired insulin secretion [Reduced MafA expression is associated with diabetes progression in mice and human patients. In islets isolated from diabetic activity . Similarpression ,29,30. Spression  and in ipression , MafA wagression . scRNA-sgression . This figression . In a diither TF . Since Mither TF ,36, it iither TF ,35,37. Iecretion .Ins2 promoter is TGCAGCTTCAGCC whereas the equivalent in the human INS promoter is TGCAGCCTCAGCC (the underlined nucleotides indicate deviations from the MARE consensus sequence) [Ins2 and human INS promoter. MafA can bind to MARE2 and MARE3 in the rat Ins2 promoter while MafA can bind to MARE1 in the human INS promoter [MafA transcriptional activity is necessary for proper postnatal β-cell function. MafA was first identified as a TF of insulin that binds to the C1 element of the insulin gene promoter. MAF proteins, including MafA, bind to the MAF-recognition elements (MAREs), through a DNA consensus sequence TGCTGAC(G)TCAGCA . Howeverequence) ,16,40. IIns1 and Ins2 gene promoters are glucose intolerant. These C1 box mutations also prevented MafA from activating the promoter in luciferase assays, further supporting the importance of MafA in upregulating the promoter of its target genes [INS promoter have been shown to cause permanent neonatal diabetes, which may be attributed to the disruption of MAFA and NEUROD1 binding sites [Although MafA can activate the insulin promoter alone, the addition of Neurod1 and Pdx1, which bind the E box and A box of the insulin promoter, respectively, synergistically promote insulin transcription . Consistet genes . Human mng sites ,43. Otheng sites .2+ influx and oxidative phosphorylation. Many studies have identified a plethora of MafA target genes, all having important roles in proper β-cell function, further supporting MafA’s critical role in β-cells. While some studies have found direct MafA target genes , other studies describe genes that are differentially expressed when MafA protein is overexpressed or silenced, without showing direct MafA binding to the target gene promoters . Some of these MafA-target genes are summarized in Beyond insulin, MafA increases the expression of a wide range of genes involved in β-cell function, including genes implicated in glucose sensing, insulin processing, CaMafa gene expression. The Mafa promoter consists of six regions (R1-R6), which contain highly conserved sequences 25 kb upstream the 5′ MafA untranslated region (UTR). R1 spans from −9389 to −9194, R2 spans from −8420 to −8293, R3 spans from −8118 to −7750, R4 spans from −6622 to −6441, R5 spans from −6217 to −6031 and R6 spans from −250 to +56. Of these, R3 has been shown to be most necessary, but not sufficient, for transcriptional initiation mediated by direct TF binding [Mafa promoter, including Pax6, Nkx6.1, Nkx2.2, Pdx1, Hnf1a, Foxa2 and Isl1. Changes in these transcription factors directly correlate with Mafa expression. However, there are some exceptions. Neurod1, another important insulin transcription factor, can bind to R3 of the Mafa promoter, but Neurod1−/− mice do not have detectable changes in MafA staining [Neurod1∆endo mice had lower Mafa mRNA levels, suggesting that Neurod1 may regulate Mafa transcription [Mafa. Pax4 overexpression limits R3-mediated promoter activity, but there is limited evidence of direct Pax4 binding to R3 via ChIP, possibly due to Pax4 regulating Mafa in an indirect manner [The first level of MafA regulation is through  binding . Many trstaining . Howevercription . AdditioMafa transcription through other mechanisms, as summarized in db/db mice, acts as a negative regulator of Mafa transcription by preventing Foxa2 from interacting with the Mafa promoter, highlighting the importance of the transcriptional regulation of Mafa in metabolic disease states [Other TFs bind elsewhere in the MafA promoter or regulate e states .2O2 exposure, translocates to the nucleus where it enhances Mafa expression by binding the forkhead element in the Mafa promoter [Mafa mRNA and decreased age-dependent β-cell mass decline [3), bound to the thyroid hormone receptor, directly transactivates the Mafa promoter [Mafa transcription. CREB constitutively binds the cAMP response element of the Mafa promoter, while Crtc2 binds the Mafa promoter following forskolin treatment, enhancing Mafa transcription. This MafA expression-potentiating mechanism is impaired by chronic high glucose levels [Foxo1, a TF involved in a number of metabolic pathways, is inactive in resting β-cells, but after acute high glucose or Hpromoter ,66. Over decline . Convers decline . Other mpromoter . Similare levels .Mafa in β-cells is also sensitive to acute changes in glucose levels. In low glucose conditions, Mafa mRNA levels are low, but after acute high glucose exposure, Mafa transcription increases. This glucose-dependent transcriptional regulation is mediated by the hexosamine biosynthesis pathway and is thought to be independent of glycolysis [Mafa transcription paradoxically decreases [MafA promoter, which at least partly contributes to lower Mafa mRNA [Mafa transcription, possibly by decreasing Pdx1 nuclear localization and binding to the Mafa promoter [Mafa transcription under low glucose conditions, thereby causing a decrease in Mafa transcription [Mafa at the transcriptional level [The transcription of ycolysis . Howeverecreases ,70. Oxidafa mRNA . Palmitapromoter . Additiocription ,73. Likeal level .Mafa gene expression is also affected by non-coding RNAs (ncRNA). A number of studies have identified microRNAs (miRNA)—short ncRNAs that base-pair with their target mRNA at the 3′-untranslated region (UTR) to promote mRNA instability and repression in protein translation [Mafa gene expression [Mafa mRNA, resulting in mRNA degradation and decreased protein translation [MAFA that is not present in the 3′-UTR of rat Mafa [Mafa gene expression include miR-149, which negatively regulates Mafa after arsenite exposure [Mafa by inhibiting Mitogen-activated protein 4 kinase 4 (Map4k4) [Mafa [nslation —that regpression ,77,78,79nslation . Howevernslation , likely rat Mafa . Other mMafa is also affected by long non-coding RNAs (lncRNA). LncRNAs are transcripts of >200 bp that that lack protein-coding potential, but have biological roles through chromatin remodeling, imprinting induction, regulation of splicing and mRNA translation [Meg3 binds Ezh2, a methyltransferase, which causes suppression of gene expression of three repressive transcriptional regulators  via promoter methylation, culminating in an increase in Mafa expression [MEG3 promoter is hypermethylated in islets from human subjects with T2D, suggesting that MEG3 expression is downregulated in T2D, which may contribute to decreased MAFA expression [nslation . For exapression . In humapression ,84.Mafa transcriptional regulation. Post-translational modifications (PTMs) add a second layer of regulation, allowing for dynamic changes in MafA activity or stability in response to changes in β-cell environment. While most documented PTMs are found in the N-terminal transactivation domain of MafA [The previously discussed studies indicate the importance and potential mechanisms of  of MafA , other r of MafA   ,95. MostInterestingly, another study identified MAFA S64F missense mutations in two families, which led to hypophosphorylation of MafA . Phenoty32KE) [Small ubiquitin-like modifier (SUMO) has been shown on MafA lysine residues. Despite its name, SUMOylation does not target proteins for proteasomal degradation but rather affects protein function, stability and transport. SUMOylation occurs on lysine residues at a consensus Ψ-K-X-E motif, where Ψ represents a hydrophobic amino acid. SUMOylation of Lys32 occurs at this motif (VK32KE) ,101, and32KE) . In immo32KE) ,101. Int32KE) . These r32KE) .Ubiquitin acts in pleiotropic ways, depending on the pattern of ubiquitination. Polyubiquitination usually marks a protein for proteasomal degradation, but monoubiquitination can regulate DNA repair and gene expression ,104. MafMafA ubiquitination is largely contingent on MafA phosphorylation. A number of studies have shown that blocking MafA phosphorylation, in particular at S65, significantly decreases ubiquitination and increases MafA half-life ,94,95. CAlthough MafA ubiquitin status is dependent on its phosphorylation status, there are mechanisms that protect MafA phosphorylation from degradation. K(lysine) acetyltransferase 2B (Kat2b), also known as p300/CBP-associated factor (PCAF), is believed to enhance the transactivation potential of GSK3-phosphorylated MafA by protecting it from degradation . Kat2b dHowever, MafA ubiquitination may be even more complex, as two novel regulator proteins for MafA have also been identified: ubiquitin-specific protease 7 (USP7) and HMG-CoA reductase degradation protein 1 (HRD1), also known as synoviolin. USP7 is a ubiquitin protease that cleaves ubiquitin off its substrate while HRD1 is an endoplasmic reticulum-resident E3 ubiquitin ligase ,108. Re-Mafa gene expression [MafA may be regulated by other PTMs. MafG can be acetylated at Lys53, Lys60, Lys71 and Lys76, which increases its transcriptional activity . Indeed,pression , no evidpression . StudiesMafa transcription, but MafA also shows important post-translational regulation through phosphorylation, SUMOylation and ubiquitination. Further elucidation of the molecular underpinnings of MafA regulation may provide novel therapeutic targets for the treatment and prevention of diabetes.In the last two decades, many studies have investigated MafA in the context of β-cell function. Since its discovery as a regulator of insulin transcription, MafA has also been identified as a TF for numerous other genes involved in the acquisition of β-cell functional maturity status, including genes involved in glucose sensing and insulin secretion. Consistently, the loss of β-cell MafA in mice leads to glucose intolerance and diabetes progression. In humans, decreased β-cell MAFA correlates with T2D, further supporting the importance of MAFA in maintaining β-cell function. Many studies have identified regulators of


Elucidating these mechanisms is of particular interest as these gene regulatory pathways can serve as potential targets to treat metabolic disease. The zinc-finger transcription factor Krüppel-like factor 15 (KLF15) is a critical regulator of metabolic homeostasis; however, its genome-wide distribution in skeletal muscle has not been previously identified. Here, we characterize the KLF15 cistrome in vivo in skeletal muscle and find that the majority of KLF15 binding is localized to distal intergenic regions and associated with genes related to circadian rhythmicity and lipid metabolism. We also identify critical interdependence between KLF15 and the nuclear receptor PPARδ in the regulation of lipid metabolic gene programs. We further demonstrate that KLF15 and PPARδ colocalize genome-wide, physically interact, and are dependent on one another to exert their transcriptional effects on target genes. These findings reveal that skeletal muscle KLF15 plays a critical role in metabolic adaptation through its direct actions on target genes and interactions with other nodal transcription factors such as PPARδ.Skeletal muscle dynamically regulates systemic nutrient homeostasis through transcriptional adaptations to physiological cues. In response to changes in the metabolic environment ( Skeletal muscle has robust capacity to remodel its metabolic machinery to adapt to changes in physical activity and nutrient availability , 2, 3. Ne.g., liver, skeletal muscle, heart), its role in the metabolism of all three major nutrient classes, and the nature of its circadian oscillations define it as a critical player in regulating metabolic networks (Klf15 knockout (KO) mice, and more recently of muscle-specific Klf15 KO mice (K15-SKO), demonstrates pronounced defects in lipid utilization, decreased exercise capacity, and susceptibility to diet-induced obesity  superfamily of transcription factors, whose ligand-regulated function allows tight coupling of changes in the cellular environment with changes in transcription. The peroxisome proliferator–activated receptors (PPARs) are an important subfamily of ligand-activated NRs whose functions include the transcriptional regulation of cellular development and differentiation, nutrient homeostasis, and metabolism. In mammals, three members of this family have been identified: PPARα, PPARβ/δ, and PPARγ. All three isoforms are activated by natural fatty acids and their derivatives, whose sources include diet, ic acids . The PPAic acids . Small-mic acids  and thiaic acids .Ppard have limited viability due to defects in placentation, adipose mass, myelination, and inflammation , however, remains to be defined in skeletal muscle. Recently, a mouse model harboring a Klf15 “knock-in” allele containing a 3xFLAG epitope tag fused to the C-terminus of KLF15 has been developed and characterized, allowing for robust detection of endogenous KLF15 protein in vivo and ChIP-seq in the adult mouse liver . Gene ontology analysis on proximal targets of KLF15 peaks revealed enrichment of similar pathways seen in previous transcriptomic investigations of muscle KLF15; that is, KLF15 demonstrates increased binding at genes related to circadian rhythmicity and lipid metabolism, the transcription of which are attenuated in the absence of KLF15 (B and C)  (D). This colocalization of binding motifs suggested a possible cooperative role between KLF15 and other transcription factors that bind at these locations. Notably, the peroxisome proliferator response element (PPRE), which binds the PPAR family of NRs, was among one of the most highly enriched motifs found near KLF15-binding sites (D).Nearly 5000 KLF15-binding sites were determined, with 7.31% located within gene promoter regions and 63.78% located in distal intergenic regions A. Gene oof KLF15 , B and CB and C) , 15. Mot, FoxO3) D. This cng sites D.Figure e.g., fasting, exercise), and manifest similar phenotypes when targeted in mice . Importantly, Ppard mRNA expression is not affected in K15-SKO (B), suggesting that the presence of KLF15 may facilitate PPARδ-mediated transcription. PPARδ ChIP-seq corroborates these findings as significant overlap of PPARδ and KLF15 binding occurs in skeletal muscle. Indeed, of the 4238 PPARδ sites identified, 3263 (56.4%) were shared by KLF15 binding (B and C). Motif analysis of 200 bp upstream and downstream of significantly enriched peaks in the PPARδ ChIP-seq revealed the presence of the PPRE consensus sequence, as well as the KLF motif (D), suggesting that PPAR NRs and KLFs bind in close proximity to one another and can coregulate gene transcription. We observed significant binding of both factors at a number of important metabolic genes  (E), strengthening previous evidence of these factors as critical metabolic regulators. Furthermore, co-immunoprecipitation studies using heterologous expression of epitope-tagged constructs demonstrated that KLF15 and PPARδ can form a complex (F). Altogether, these findings indicate that KLF15 and PPARδ colocalize across the genome of adult skeletal muscle and suggest that both transcription factors may function in an interdependent manner to coregulate a critical subset of target genes.Pathway analysis of the downregulated genes identified from RNA sequencing of K15-SKO muscle revealed significant enrichment for PPAR signaling pathway, peroxisome, and fatty acid metabolism A. Import K15-SKO B, sugges binding , B and CLF motif D, suggesnd Pdk4) E, streng complex F. AltogeA). PPARδ enrichment at 3060 PPARδ-binding sites remained unchanged. However, a comparison of binding intensity and degree of enrichment at all PPARδ-binding sites demonstrated an overall decrease in PPARδ binding in K15-SKO (B). Additionally, there was a change in PPARδ global distribution patterns in the absence of KLF15: PPARδ binding near transcription start sites was significantly decreased (C), and there was a shift away from promoter regions with a relative increased binding at distal intergenic regions (D). Interestingly, we observed decreased PPARδ binding at several important metabolic genes, especially those related to lipid metabolism (E and F). These results demonstrate that KLF15 is required for PPARδ recruitment to specific genomic loci, suggesting that endogenous KLF15 coordinates PPARδ-mediated transcriptional responses.These observations prompted us to further investigate whether PPARδ localization and function were dependent on the presence of KLF15. We performed PPARδ ChIP-seq in K15-SKO muscle and found that 1178 PPARδ-binding sites  showed decreased PPARδ enrichment in the absence of KLF15 A. PPARδ  K15-SKO B. Additiecreased C, and th regions D. Interetabolism , E and FFatp1, Cpt1b, and Slc25a20. Following silencing of KLF15, the induction of these genes by GW501516 was significantly attenuated (A and B). This effect was confirmed in vivo by gavaging MyoCre and K15-SKO mice with GW501516 for 10 days (5 mg/kg body weight per day) followed by tissue collection. qPCR analysis showed that GW501516-stimulated gene induction of the PPARδ targets Fatp1, Cpt1b, and Slc25a20 was attenuated in K15-SKO mice when compared to MyoCre controls (C). To assess whether KLF15 deficiency also affected PPARδ-dependent shifts in oxidative metabolism, we performed Seahorse profiling of cultured C2C12 cells in the setting of Klf15 silencing and GW501516 stimulation. Similar to our gene expression analyses discussed earlier in the study, Klf15 knockdown led to a decrease in basal palmitate oxidation and robustly inhibited the GW501516-stimulated augmentation of palmitate oxidation (D). Together, these data indicate that optimal PPARδ target gene induction and resultant increases in metabolic respiration are dependent on the presence of KLF15.Because basal PPARδ localization appears to be dependent on KLF15, we sought to assess whether ligand-mediated agonism of PPARδ was similarly affected by the presence/absence of KLF15. To this end, we utilized the selective PPARδ agonist GW501516. GW501516 has been shown to impart a number of beneficial metabolic effects through its actions in increasing energy expenditure, inducing lipid metabolism genes, and increasing fatty acid oxidation , 30, 31.tenuated , A and Bcontrols C. To assxidation D. TogethKlf15 locus, we were able to characterize the KLF15 cistrome in vivo in skeletal muscle and demonstrate that genes and pathways previously shown to be downregulated in the absence of KLF15 are generally bound by KLF15  can bind in proximity to KLF15.Due to the significant enrichment of PPREs near KLF15-binding sites and the similarities between the function of KLF15 and PPARδ in response to physiological cues, we turned to this family of NRs to illustrate KLF15’s potential action on proximally binding transcription factors. Our studies demonstrated that KLF15 and PPARδ colocalize on the skeletal muscle genome, can form a complex, and function in an interdependent manner to regulate lipid metabolism. In the absence of KLF15, PPARδ binding was lost near KLF15-binding sites, and PPARδ binding shifted away from promoter regions, suggesting that KLF15 helps recruit PPARδ to the correct transcriptional loci. However, given that KLF15 deficiency blunted downstream gene transcription rather than completely abolishing it, it is likely that other transcription factors or coactivators and corepressors act in tandem with the two under investigation here. In support of this, motif analysis of genomic regions flanking KLF15-binding sites demonstrated that other important metabolic factors  suggest that KLF15 critically interacts with other transcription factors in these metabolic networks. Additionally, previous studies have shown that cardiac KLF15 binds and recruits the chromatin-remodeling enzyme p300 to promoters of genes controlling nutrient flux, thus possibly influencing chromatin accessibility for other transcription factors to bind  facilitate the opening of chromatin that subsequently permits enhancer activation via the binding of additional transcription factors  mouse model was generated by mating the Myogenin-Cre (MyoCre) mouse line to the Klf15escribed . In brieis model . Age-matKlf15 , Cyclophilin B , Fatp1 , Cpt1b , Slc25a20 Tissue samples were disrupted in PureZOL in a Tissue-lyzer (Qiagen) using stainless-steel beads . Total RNA was isolated using Bio-Rad Aurum Total RNA Fatty and Fibrous Tissue Kit using the manufacturer’s instructions and transcribed to complementary DNA using iScript (Bio-Rad). Quantitative real-time PCR was performed using Taqman method and appropriate probes from Roche Universal Probe Library System. Gene expression was normalized to cyclophilin B and compared using ΔΔCt method. All primers were efficiency tested and validated. Primers used in this study include the following: The RNA sequencing data analyzed and discussed in this publication were from NCBI’s Gene Expression Omnibus dataset GSE160848. Significantly enriched Gene Ontology Biological Processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) were generated by iDEP .https://www.bioinformatics.babraham.ac.uk/publications.html). The ENCODE transcription factor ChIP-seq pipeline . For ChIP-Seq, for 100,000 to 150,000 cells, 5 uL of anti-FLAG  or anti-PPARb/d  antibodies were added to 25 uL of Protein A/Protein G-coupled Dynabeads  in PBS with 0.5% BSA and incubated for 4 h at 4 degrees. Cells were sonicated for 12 cycles of 30 s on/30 s off on high power using a Bioruptor Pico (Diagenode). Triton X-100  and 50× cOmplete protease inhibitor  were added to neutralize the SDS. Samples were incubated at RT for 10 min and 5% of the aliquots were saved for preparation of input controls. Antibody-coated Dynabeads were washed with PBS, mixed with cell lysate, and incubated overnight at 4 degrees with rotation. Immunoprecipitated chromatin was washed with 150 ul of low-salt buffer, high-salt buffer, and LiCl buffer, followed by two washes with TE buffer and two washes with ice-cold Tris/HCl . For tagmentation, bead-bound chromatin was resuspended in 30 ul of tagmentation buffer. One microliter of transposase  was added, and samples were incubated at 37 degrees for 10 min, followed by two washes with low-salt buffer. For standard reverse cross-linking, chromatin complexes were diluted with 200 ul of ChIP elution buffer and 2 ul of 20 ug/ml proteinase K (Thermo Scientific), vortexed, and incubated overnight at 65 degrees. After reverse cross-linking, one ul of 20 ug/ml RNase (Sigma) was added and incubated at 37 degrees for 30 min. DNA purification was carried out using Qiagen MinElute PCR Purification Kit, and libraries were amplified and library cleanup was done using Agencourt AmPureXP beads (Beckman Coulter) at a ratio of 1:1. DNA concentrations in purified samples were measured using the Qubit dsRNA HS Kit (Invitrogen). Libraries were pooled, and single-end sequenced (50 cycles) using the NextSeq500 platform (Illumina). Initial quality check and adapter trimming was performed with Trim Galore (0.6.5) for ChIP-seq data  or GW501516 (100 nM) for 24 h. Cells were then harvested for RNA isolation and qPCR analysis. For in vivo experiments, mice were weighed and gavaged with either vehicle (DMSO in 5% carboxymethylcellulose) or GW501516 (in 5% carboxymethylcellulose) daily at 5 mg/kg body weight for 10 days. At the end of the experiment, animals were euthanized, and plasma and skeletal muscle were isolated for RNA isolation and qPCR analysis.For GSE199547.All data within the manuscript will be shared upon request (corresponding author). All ChIP-seq datasets have been deposited into NCBI’s Gene Expression Omnibus dataset S. M. H. is an executive, officer, and shareholder of Amgen and a scientific founder and shareholder of Tenaya Therapeutics. S. M. H. also serves on the Scientific Advisory Board for the German Centre for Cardiovascular Research of the German Ministry of Health (DZHK) on a voluntary and uncompensated basis. Z. J. is an employee of Biomarin. The other authors have declared that no conflict of interest exists.


Chromatin dynamics as well as genetic evolution underlies the adaptability of tumour cells to environmental cues. Three subtypes of tumour cells have been identified in glioblastoma, one of the commonest malignant brain tumours in adults. During tumour progression or under therapeutic pressure, the non‐mesenchymal subtypes may progress to the mesenchymal subtype, leading to unfavourable prognosis. However, the molecular mechanisms for this transition remain poorly understood. Here taking a TNFα‐induced cellular model, we profile the chromatin accessibility dynamics during mesenchymal transition. Moreover, we identify the JUN family as one of the key driving transcription factors for the gained chromatin accessibility. Accordingly, inhibition of JUN phosphorylation and therefore its transcription activity successfully impedes TNFα‐induced chromatin remodelling and mesenchymal transition. In line with these findings based on experimental models, JUN activity is positively correlated with mesenchymal features in clinical glioblastoma specimens. Together, this study unveils a deregulated transcription regulatory network in glioblastoma progression and hopefully provides a rationale for anti‐glioblastoma therapy. The current standard care of glioblastoma patients is maximal surgical resection, adjuvant chemo‐radiotherapy and neoadjuvant immunotherapy. So far, no efficacious treatment has been demonstrated to significantly increase median lifespan (12–18 months).To dissect this lethal disease at the molecular level, great efforts have been put into the genomic and transcriptomic profiling of glioblastomas in the past two decades.Nevertheless, emerging evidence from single‐cell RNA sequencing analyses has demonstrated that multiple subtypes may co‐exist even in a single glioblastoma sample.Epigenetic changes are the main underlying causes of cell fate transitions, for either normal development or malignant transformation. Here, by comparing chromatin accessibility in a TNFα‐driven MT model, we find that the chromatin regions with gained accessibility in the induced MES subtype are enriched with JUN transcription factor family. Moreover, we show that inhibition of JUN phosphorylation prevents the gain of MES‐specific accessibility and reverts TNFα‐induced MT process. Therefore, JUN transcription regulatory network is necessary to remodel chromatin accessibility and thereby promotes glioblastoma progression, which will hopefully provide a novel targeting therapeutic strategy.22.12. Cells have been tested for mycoplasma contamination by PCR and were verified to be mycoplasma free. TNFα was purchased from Sino Biological , and both compounds were dissolved in dimethyl sulfoxide .The glioblastoma cell line TPC 2–4 was obtained from Beijing Tiantan Hospital and were cultured in DMEM/Ham's F12  supplemented with 2% B27 Supplement , 1% P/S, 1% L‐Glutamine 200 mM , 20 ng/ml human fibroblast growth factor‐basic  and 20 ng/ml epidermal growth factor‐basic . Cells were maintained at 37°C in a humid incubator with 5% CO2.2The paraffin‐embedded glioblastoma tissue microarray (TMA) was used for IHC staining,2.34 TPC2‐4 cell suspension  re‐suspended with DMEM/F12. The lower chamber was filled with 5% foetal bovine serum contained‐DMEM/F12 medium. Cells in the upper chamber were carefully removed 24 h later. The migrated and invaded cells in lower chamber medium were imaged and calculated.Twenty‐four‐well Transwell chambers with 8‐mm pore size (Corning Costar) were used to perform migration assay. The upper chamber was inoculated with 5 × 102.4rPO was used as a reference gene for all qRT‐PCR experiments and analyses. The ΔΔCt method was used for quantification analysis. Gene‐specific primers were as follows:hTNFAIP3 forward: 5′‐TCAACTGGTGTCGAGAAGTCC‐3′,hTNFAIP3 reverse: 5′‐ CAAGTCTGTGTCCTGAACGC‐3′;hNFKBIA forward: 5′‐ GTCCTTGGGTGCTGATGT‐3′,hNFKBIA reverse: 5′‐ GAGAATAGCCCTGGTAGGT‐3′;CD44 forward: 5′‐ AGGAGACCAAGACACATTCCAC‐3′,CD44 reverse: 5′‐ CACCTTCTTCGACTGTTGACTG‐3′;CDH1 forward: 5′‐GAAATCACATCCTACACTGCCC‐3′,CDH1 reverse: 5′‐GTAGCAACTGGAGAACCATTGTC‐3′.As previously described,2.5Total RNA was extracted with Trizol Reagent  according to the instructions. RNA‐seq libraries were sequenced on the Illumina PE150 platform by Novogene. The data analysis was carried out as described.2.64 TPC2‐4 cells were precipitated to prepare samples according to previous description.2, 0.1% [v/v] igepal CA‐630) and incubated on ice for 10 min. After centrifuge at 500 g for 10 min at 4°C, the nuclei were re‐suspended with 100 μl wash buffer . Followed by washing twice, the transposable reaction was performed. Subsequently, DNA library was generated accordingly with TruePrep DNA Library Prep Kit V2 . The successfully prepared libraries were sequenced on the Illumina PE150 platform (Novogene).For ATAC‐seq, 5 × 102.7Gene‐set enrichment analysis (GSEA) was performed with the public application from the Broad Institute. FKPM values for all human genes generated from RNA‐seq data were used for expression datasets. KEGG pathway gene sets were used for analysis. False discovery rate (FDR) was calculated by repeating sample permutations 1000 times. To be able to subtype GSC lines, a single sample gene set enrichment analysis (ssGSEA) was performed using 50‐gene signatures for each subtype as defined previously.2.8t‐tests are presented as mean ± SD during comparison between unpaired two groups, and one‐way anova was applied for multi‐group data comparison. Bivariate correlation analysis (Pearson's r test) was used to examine the correlation of two variables in human specimens.All grouped data are presented as mean ± SD. Unpaired Student's 33.1p‐value 0.0649; ssGSEA‐value of PN was 861.15, p‐value 0.8481; ssGSEA‐value of MES was 6293.51, p‐value 1) , NF‐κB pathway‐associated genes Nuclear Factor Kappa B Subunit 1 (NFKB1A) and the MES marker gene CD44 and significantly downregulated expression of epithelia marker gene CDH1 (E‐CAD).To establish an in vitro cellular model for MT, we took advantage of a patient‐derived GSC TPC2‐4, which was validated closely resemble to CL subtype according to its transcriptome profiles  score of CL subtype was 5,069.88, ) Figure . By treaWhen we compared the transcriptomic changes at this condition through RNA‐seq analysis, we found that the expression levels of defined mesenchymal genes were significantly induced by TNFα treatment, while a significant decrease of CL signature gene expression was observed Figure . In addi3.2ITGB3 and SAMD4A is significantly increased in GSCs after being treated with TNFα , a kinase inhibitor specific for JNK that phosphorylates c‐JUN.t Figure . Taken t3.4kRasmu/P53−/− adenovirus) showed that JNKi significantly decreased the migratory rate of both cells  were significantly coincided with p‐JUN positivity , while the significant correlation with expression levels of total c‐JUN was not detected  staining in a glioblastoma tumour microarray with 40 clinical WHO grade IV glioblastoma specimen serial paraffin‐embedded sections.d Figure . Overall4Cell plasticity such as epithelial‐mesenchymal transition (EMT) in solid cancers has been a well‐recognized mechanism for tumour progression and often occurs upon treatment and recurrence, which predicts poor prognosis and treatment resistance. Emerging evidences link chromatin changes to this cell reprogramming.Actually, numerous previous studies have characterized transcription factors that may drive MT in glioblastoma. For instance, STAT3 and C/EBPβ as master regulators of MT were associated with increased tumour infiltration and glioblastoma recurrence.In addition to transcription factors, chromatin regulators actively participate in these cell fate transitions. However, their pathological functions are generally context‐specific. For instance, though Polycomb group proteins have been well known to be deregulated in malignancies, it seems that their roles differ in difference cancers and even at different stages of the same cancer type.In conclusion, this study demonstrates that activated JUN is necessary and sufficient to remodel chromatin accessibility and trigger the activation of mesenchymal subtype‐specific transcription regulatory network, and thereby promotes glioblastoma progression. Considering of the overall activation of AP‐1 in solid cancers, it will be interesting to find out whether it has broader significance in cellular plasticity beyond glioblastoma.Xuejiao Lv: Data curation (lead); formal analysis (lead); methodology (lead); writing – original draft . Qian Li: Formal analysis ; visualization . Hang Liu: Methodology . Meihan Gong: Formal analysis . Yingying Zhao: Formal analysis . Jinyang Hu: Resources (supporting). Fan Wu: Resources (supporting). Xudong Wu: Conceptualization (lead); funding acquisition (lead); writing – original draft ; writing – review and editing (lead).The authors declare no conflicts of interest.Appendix S1Click here for additional data file.


Systemic drug reaction (SDR) is a major safety concern with weekly rifapentine plus isoniazid for 12 doses (3HP) for latent tuberculosis infection (LTBI). Identifying SDR predictors and at-risk participants before treatment can improve cost-effectiveness of the LTBI program.We prospectively recruited 187 cases receiving 3HP (44 SDRs and 143 non-SDRs). A pilot cohort (8 SDRs and 12 non-SDRs) was selected for generating whole-blood transcriptomic data. By incorporating the hierarchical system biology model and therapy–biomarker pathway approach, candidate genes were selected and evaluated using reverse-transcription quantitative polymerase chain reaction (RT-qPCR). Then, interpretable machine learning models presenting as SHapley Additive exPlanations (SHAP) values were applied for SDR risk prediction. Finally, an independent cohort was used to evaluate the performance of these predictive models.Based on the whole-blood transcriptomic profile of the pilot cohort and the RT-qPCR results of 2 SDR and 3 non-SDR samples in the training cohort, 6 genes were selected. According to SHAP values for model construction and validation, a 3-gene model for SDR risk prediction achieved a sensitivity and specificity of 0.972 and 0.947, respectively, under a universal cutoff value for the joint of the training (28 SDRs and 104 non-SDRs) and testing (8 SDRs and 27 non-SDRs) cohorts. It also worked well across different subgroups.The prediction model for 3HP-related SDRs serves as a guide for establishing a safe and personalized regimen to foster the implementation of an LTBI program. Additionally, it provides a potential translational value for future studies on drug-related hypersensitivity. By integrating clinical samples, bioinformatic techniques, and explainable machine learning, we provide a blood-based prediction model to foresee rifapentine-based treatment–related systemic drug reaction, leading to increased public acceptance of public health policy on latent tuberculosis infection intervention. Treatment for latent tuberculosis infection (LTBI) is crucial for tuberculosis (TB) elimination . A shortThe associated phenotypic risk factors for 3HP-related SDRs include female sex , age >35Some studies have proposed gene signatures for LTBI and TB diagnosis . HoweverBetween January 2017 and January 2020, 447 participants who ever took at least 1 dose of 3HP in 2 medical centers were enrolled. Among them, 44 developed SDRs during 3HP treatment. All of the 44 SDR cases and 143 of the 404 non-SDR cases were selected to identify SDR-related biomarkers. The 8 SDR and 12 non-SDR cases enrolled between January 2017 and June 2019 were grouped as the pilot cohort. The remaining cases, enrolled after July 2019, were randomly divided 80% into the training cohort (28 SDR and 104 non-SDR cases) and 20% into the testing cohort (8 SDR and 27 non-SDR cases).The study consisted of 4 main parts . First, 2, which inevitably resulted in a low dosage of isoniazid and rifapentine), malignancy under treatment, living with human immunodeficiency virus, and acute illness with inflammatory symptoms and signs were excluded.Individuals were eligible for enrollment if they were aged ≥13 years and received ≥1 dose of 3HP treatment for LTBI, which was diagnosed using QuantiFERON-TB Gold In-tube . Participants with active TB, a history of LTBI treatment, obesity (BMI >30 kg/mEligible participants received 12 doses of weight-adjusted weekly high-dose rifapentine plus isoniazid under supervision . Within SDR phenotypes included hypotension (systolic blood pressure <90 mm Hg), urticaria, angioedema, acute bronchospasm, or conjunctivitis and more than 4 of the following symptoms occurring concurrently (more than 1 of which had to be grade ≥2): weakness, fatigue, nausea, vomiting, headache, fever, aches, sweating, dizziness, shortness of breath, flushing, or chills .P < .05 (t test) were set to identify 1243 differentially expressed genes (DEGs) before and after 3HP treatment (For RNA-seq data (GSE174552) obtained from the pilot cohort , we usedreatment . To inveIG) to calculate the importance of each DEG  tlidation . Finallylidation .sklearn.ensemble.RandomForestClassifier) [To build a robust prediction model with a universal cutoff value, the training cohort was used to develop interpretable RF models with SHAP values . First, ssifier) .Next, the SHAP method was applied to these models to reveal the impact of each gene on each sample. The default values were then used to build simple and robust models. The sum of SHAP values of a sample’s input features was used to compare universal cutoff values for predicting 3HP-related SDRs. Finally, the independent testing cohort was used to evaluate the performance of the models based on sensitivity (sen), specificity (spe), and their geometric mean  .We assessed the performance of the selected SDR predictive models in the joint population of training and testing cohorts as well as various subpopulations, stratified by age, sex, BMI, presence of systemic comorbidity, and renal function  of sensitivity and specificity. Standard normal distribution was used to calculate the 95% CI of the area under the receiver operating characteristic curve.The demographic data, comorbidity status, laboratory data, treatment course, and all ADRs were programmatically collected. The intergroup difference was analyzed using the Mann–Whitney The study was approved by the institutional ethics committees of National Taiwan University Hospital and Kaohsiung Medical University Hospital. Each participant provided informed consent before enrollment.Among the 44 SDR cases and 143 non-SDR cases , some haThe baseline characteristics of the SDR and non-SDR participants were similar, except that the SDR group in the training cohort had a lower prevalence of hypertension and diabetes mellitus and a lower level of serum alanine transaminase .IG and BS scores, we identified 19 candidate genes with significantly higher expression in the 16 SDR samples (orange) than in the 20 non-SDR samples  were involved in the neural-related pathways in Kyoto Encyclopedia of Genes and Genomes  and a low proportion of monocytes  before 3HP treatment were significantly associated with SDRs . Second, SHAP models significantly outperformed RF models in various parameter settings, including default, 500 trees, 6 genes, 5 samples required to be at a leaf node, and 5 samples required to split an internal node  has similar performance, with a G-mean of approximately 0.7 for unbalanced and balanced datasets . ConversThe expression profiles by RT-qPCR of all 6 genes were significantly higher in the SDR group compared with the non-SDR group in the training cohort . SystemiATP5PF and DDT were similar in the 132 training samples. Furthermore, SPCS1 and GABARAPL2 had a similar expression level  regulates a diverse range of physiological functions related to innate immunity and inflammation [SPCS1  is involved in the post-translational processing of the structural proteins of Japanese encephalitis virus, Zika virus, and hepatitis C virus [ATP5PF encodes ATP synthase-coupling factor 6 and enhances oxidative phosphorylation in mitochondria during inflammation [The underlying pathogenetic mechanism of the 3HP-related hypersensitivity reactions remains unclear. phocytes . GABARAPABARAPL2 , 28 and ATP6V0E1  both encammation . SPCS1   suggest By using RNA sequencing to create a global picture of cellular function across all expressed genes, bioinformatic tools to select candidate genes, and machine learning to interpret RT-qPCR results, we provided SDR prediction models with a universal cutoff value to foresee 3HP-related SDRs and provide an aid for treatment decisions before preventive therapy. The SDR prediction model represents a major step forward for precision medicine in TB preventive therapy and may speed up the global uptake of public health programs against LTBI for TB elimination.Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.Supplementary materials are available at ciac003_suppl_Supplementary_MaterialClick here for additional data file.


Drosophila and mammals as metazoan model organisms.Lamins are components of the nuclear lamina, a protein meshwork that underlies the nuclear membrane. Lamins interact with chromatin in transcriptionally silent regions defined as lamina-associated-domains (LADs). However, recent studies have shown that lamins regulate active transcription inside LADs. In addition, ChIP-seq analysis has shown that lamins interact with lamin-dependent promoters and enhancers located in the interior of the nucleus. Moreover, functional studies suggest that lamins regulate transcription at associated-promoters and long-range chromatin interactions of key developmental gene programs. This review will discuss emerging, non-canonical functions of lamins in controlling non-silent genes located both inside and outside of LADs, focusing on transcriptional regulation and chromatin organization in  Drosophila, which has one B-type (lamin) and one A-type lamin (lamin C) gene, respectively [lamin B1 and lamin B2 [An intriguing question in biology is how do cells differentiate and maintain their status over successive rounds of cell division. During development, gene expression is tightly regulated to execute specific gene programs. Among the different layers of regulation, spatial genome organization has emerged as a key mechanism ,2. The nectively . The majectively . The twolamin B2 ,7. A-typlamin B2 . The biolamin B2 . In addiin situ hybridization (FISH) experiments that confirmed that certain transcriptionally silent regions associated with heterochromatin are located in the nuclear periphery interacting with NL. More recently, genome-wide assays based on DNA adenine methyltransferase identification (DamID) and ChIP-seq, mostly performed to profile LaminB, identified genomic regions that frequently make close contact with the NL. These domains referred to as lamina-associated domains (LADs) are characterized by showing low transcriptional activity and heterochromatin features [Functionally, lamins have been implicated in a large repertoire of cellular mechanisms including mechanotransduction, modulation of nuclear stiffness and elasticity, chromatin organization, DNA replication and repair, and transcriptional regulation . Pioneerfeatures .Spatial partitioning of the genome is also evident in interaction maps generated by chromosome conformation capture technologies such as Hi-C ,19. GenoD. melanogaster and mammals as metazoan model organisms.Functionally, compartments can be transcriptionally sub-classified as A-type (open) or B-type (closed) depending on their transcriptionally active or inactive status, respectively. DamID and Hi-C data from mouse embryonic fibroblasts (MEFs) demonstrated that LaminB LADs correspond to the B-compartment . ImportaDrosophila and later by ChIP-seq and tyramide signal amplification (TSA)-sequencing (TSA-seq) mostly using LaminB as target protein [Drosophila with similar characteristics to the human genome but 5-fold smaller, consistent with its much more compact genome [LADs were first mapped by DNA adenine methyltransferase (DamID) identification (DamID) in  protein ,28–31. I protein ,15,17. It genome .Drosophila and mammalian cells.LADs can also be classified according to their epigenetic compositions and cell type-specific genome interactions. Across species and cell types, LADs are categorized into regions that associate with the NL in all cell types  and regions that facultatively associate with the NL in a cell type-specific manner  ,16,33. cThe majority of the aforementioned studies have shown that genes located at the nuclear periphery are transcriptionally repressed, whereas detachment from LADs is correlated with gene activation . MoreoveDrosophila, it has been shown that during neuroblast differentiation the transcription factor Hunchback is susceptible to be transcribed during a competence window. During that time window, the hunchback gene (hb) is repressed by transcriptional repressors before its movement to the nuclear periphery where it becomes permanently silenced [hb gene to the nuclear periphery, extends the transcriptional window competence and decreases hb transcriptional silencing. Overall, these results suggest that lamins play a role as primary hierarchical corepressor by maintaining the silenced state, at least for the hb locus.In silenced . ImportaIt has been shown that ∼10% of genes are able to escape transcriptional repression despite being located along LADs . In thisHistorically, it has been proposed that genes embedded within the NL are necessarily repressed. This paradigm has been mostly based on single locus scale analysis. However, the advent of genome-wide studies has challenged this consensus. Strikingly, recent experiments showed that lamins are able to modulate active transcription inside LADs. For instance, global run-on sequencing (GRO-seq) experiments showed that a significant fraction of promoters located inside LADs are actively transcribed in mammals ,40. ThesRecently, a comprehensive study using TALE-VP16 and CRISPR tethering approaches to activate LAD genes showed that NL detachment takes place along the entire gene body, which is more pronounced near the promoter region compared with the downstream transcription unit in mouse embryonic stem (ES) cells . InteresDrosophila Kc167 cells (Kc) showed that LaminB depletion enhances transcription in domains known as RED chromatin located within LADs [Drosophila and corresponds to transcriptionally active regions characterized by the lack of the elongation histone mark H3K36me3. In addition, RED chromatin is associated with developmentally regulated genes or those susceptible to induction by external stimuli [Drosophila reinforce the interplay between lamins and RNA Pol II to modulate transcription inside LADs likely by controlling transcriptional pausing, as has been suggested in mammals (see above).In agreement with mammalian studies, genome-wide transcriptional analyses by nascent euRNA-seq (neuRNA-seq) in hin LADs . In brie stimuli . Interes stimuli ,45. In t stimuli . OverallNuclear lamins have also been observed in the interior of the nucleus ,47. The As mentioned above, LADs have initially been discovered by DamID assays. In addition, large chromatin domains with similar properties have been identified by chromatin immunoprecipitation (ChIP) coupled to high-throughput sequencing (ChIP-seq). The datasets reveal significant overlap in genomic coverage of lamin-interacting domains discovered by DamID and ChIP. However, a recurrent topic of debate in the field relies on the technical challenges to isolate and profile nuclear-interior lamins. As discussed above, recent ChIP-seq genome-wide studies have shown that lamins contact euchromatin in the interior of the nucleus ,54–56. Iin vivo, in contrast with ChIP, which assays interactions after cell crosslinking. Finally, it should be noted that DamID has been extensively used to profile transcription factors in euchromatin [One of the most important differences between the two approaches is that ChIP provides a snapshot of protein occupancy at a single point in time. On the other hand, DamID relies on DNA methylation that occurs over a time window of several hours. DamID shows chromatin-binding events that occur hromatin . Howeverhromatin ,49. Perhcis regulatory elements, transcription is modulated by distal enhancers which provide complex and precise control of gene expression through long-range interactions. As mentioned above, nuclear-interior pS22-LaminA binds sites with chromatin features of active enhancers in human skin fibroblasts [In addition to roblasts . Importaroblasts . Moreoveroblasts . Togetheroblasts . HoweverIn contrast with A-type, B-type lamins have been shown to remain tightly associated with the nuclear membrane. However, recent LaminB ChIP-seq analysis on euchromatin enriched fractions showed a significant association between LaminB binding and transcription start sites (TSSs) that do not overlap with canonical LADs in mouse mammary gland cells . Importade novo established after fertilization in mouse embryos [de novo LAD establishment in the two-cell stage that remains as LADs during embryo development occurs before B compartment formation, thus raising the interesting possibility that for certain genomic regions LADs may contribute to prime the formation of B compartments [An intriguing question of the field relies in understanding the interdependence between spatial genome organization and the establishment of chromatin topology. In this regard, it has been recently shown that LADs are undetectable in oocytes but become  embryos . Interesartments . Finallyartments . This isartments ,58–62. IDrosophila somatic cells [nht gene, encoding a transcription factor implicated in the spermatogenesis gene program, is also inactive and embedded within LADs [nht becomes up-regulated upon LaminB depletion without being repositioned towards the interior of the nucleus. Chromosome conformation capture assays showed that LaminB controls the overall chromatin topology of the LAD and TAD in which nht is located [nht and other sites within the TAD decrease in interaction frequency in LaminB-depleted cells. In addition, increased interactions are observed between the nht promoter and sequences located in other chromatin domains beyond 1 Mb away. These large alterations in topology in LaminB-depleted cells could explain the resultant transcriptional increase in nht by allowing interaction with inappropriate enhancers or otherwise creating a more permissive transcriptional environment [In concordance with the role of lamins in transcription and chromatin organization described in mammals, it has been shown that certain spermatogenesis gene clusters are associated with the nuclear periphery and become de-repressed and reposition toward the nuclear interior upon depletion of LaminB in ic cells . Intereshin LADs . However located . Specifiironment   . Intriguin situ hybridization and genome-wide DamID profiles [The classical view in the nuclear organization field assumes that lamins are restricted to LADs. These domains have been characterized to interact with heterochromatin regions located at the nuclear periphery. Initially, the interplay between lamins and chromatin has been analyzed by profiles ,15,17,65profiles ,66,67. Hprofiles ,52. Impoprofiles ,50.Drosophila LaminB modulates active transcription associated with RED chromatin, a hallmark associated with pausing and stimulus-responsive transcription, and chromatin topology [Currently, it is unclear how lamins regulate transcription at associated TSSs. GRO-seq analysis suggested that LaminA restricts transcriptional elongation likely by promoting pausing . In additopology . In thisLamins have been traditionally seen as components of the NE associated with heterochromatin and transcriptional silencing.Recent genome-wide approaches show that lamins are associated with promoters and enhancers outside LADs and regulate transcription and chromatin topology of key differentiation gene programs.Further experiments will shed light on understanding how lamins regulate promoters and enhancers and their therapeutic potential as novel cofactors to treat diseases such as ageing and cancer.


Our genomes are highly organized spatially in three-dimensions (3D). In interphase nuclei, the genome is anchored and regulated by various nuclear scaffolds and structures, including the nuclear lamina at the nuclear edge, and nucleoli located more internally within the nucleoplasm. Recently, great effort has been made to understand the intricacies of 3D genome organization and its relevance to genomic and nuclear function. Over the years, many concepts, mathematical models, visual and biochemical methods, and analysis pipelines have been presented to study various aspects of this organization in a multidisciplinary manner, such as is also reflected within this collection. However, in recent years, the development of chromatin conformation capture (3C) based proximity mapping techniques has revealed that the genome is indeed separated into compartments, and furthermore into self-interacting genomic regulatory units termed topologically associating domains (TADs)4. Further improvements to 3C based methods have allowed for the detection of typical enhancer-promoter loops. Most of these higher resolution approaches are based on averages derived from cell populations in asynchronous cultures. Development of super-resolution microscopy techniques5 and advances in new probe technologies, particularly the Oligopaint approach combined with automated sequential hybridization, have led to a better understanding of these proposed chromatin structures at a single cell level with improved resolution6.These efforts have led to the discovery of new structures at the chromatin level or improved our understanding of genomic organization at an even higher resolution than before. Initially, individual chromosomes were shown to occupy distinct territories within interphase nuclei using fluorescence in situ hybridization technique7. Li et al. introduced a chromatin packing domain structure, based on their observations employing chromatin scanning transmission electron microscopy (ChromSTEM), together with DNA-specific staining8. The authors used a polymer physics-based algorithm to analyze their data and detected the higher order chromatin packing domains of 60–160 nm in diameter. They concluded that chromatin packing was higher at the nuclear periphery8. Parteka-Tojek et al. were able to push the limits of in situ hybridization technologies using Oligopaint probes with interferometric photoactivated localization microscopy and visualized the spatial conformation of a 13 kb chromatin loop with a 120–300 bp genomic resolution and 4–22 nm microscopic precision in X-, Y-, Z-dimensions in human lymphoblastoid cells. The authors suggested that the method could allow for the visualisation of the loop extrusion process by taking snapshots in single cells9. Combined with other approaches, this should help to elucidate the connection between chromatin looping and transcription.In this 3D Genome Organization Collection, several groups have presented technical developments that increase the resolution power to interrogate the structure of the genome at a more detailed level. Hirata et al. presented an imputation-free computational method for individually reconstructing the 3D architecture of each chromosome by integrating allele-specific single nucleotide variation data for each chromosome pair into a Hi-C dataset from single diploid cells10.A second aspect of this special Collection focuses on the biological consequences of perturbations in cellular 3D organization. Korolev et al. studied the elasticity of the genome after physical expansion and compaction of nuclei. They performed Hi-C and microscopy following either swelling the nuclei under low salt concentration or extending and retracting the chromatin physically. While slight changes were observed in short distance interactions, higher order structures such as loops, TADs compartments, and chromosome territories maintained their global organization. The authors conclude that this robustness of genome topology offers resilience against physiological changes that take place in nuclear morphologyLMNA, leading to the production of a mutant form of lamin A termed Progerin. The authors found that expression of Progerin altered 3D telomere organization and altered the chromatin state and the replication timing of the telomeres. They suggested that Progerin expression increases the contacts between the telomeres themselves and the lamina, which led to replication stress, resulting in shortening of telomere size. This telomere shortening triggered premature growth arrest in cells, leading to premature senescence. The HGPS cells were found to have low nucleoside pools and the addition of dNTPs permitted some rescue of the replication stress11. Gallant et al. introduced a high-throughput automated visual assay to detect DNA damage (HiIDDD) based on measurement of 53BP1 and ƴ-H2AX levels. This assay can obtain data from a large number of cells concomitantly, and may be particularly useful in cancer, inflammation, drug development, and ageing studies12.As more clinical data are accumulated, 3D genome organization is revealing itself as a spatial epigenetic mechanism to fine-tune gene expression. The nuclear lamina acting both as an anchor and a regulatory site plays a fundamental role in this organization. Kychgina et al. studied Hutchinson-Gilford Progeria Syndrome (HGPS), a premature ageing condition caused by aberrant splicing and processing of We are still a long way from understanding how 3D genome organization is linked precisely to genome function. A concerted multi-disciplinary effort is needed to develop new tools and computational prediction methods, multi-target chromatin imaging techniques in live-cells, and efficient manipulation methods for 3D genome structures. These efforts should be accompanied by the collection of 3D genome data from different diseased and healthy cells and tissues in humans, as well as a range of model organisms. Our increased knowledge of 3D folding of the genome will lead to a better appreciation of the regulatory potential of the linear genetic sequence. 3D genome organization emerges as a cell type specific epigenetic mechanism and gives us clues about the regulatory effect of the non-coding genome in the 3D context. This understanding will allow for enhanced interpretation of genetic variants and their potential phenotypic effects. Finally, such studies will bring new 3D insights into diagnostics and therapies for different conditions including cancer, developmental diseases, ageing, and related disorders.


Transposable elements (TEs) are recognized for their great impact on the functioning and evolution of their host genomes. They are associated to various deleterious effects, which has led to the evolution of regulatory epigenetic mechanisms to control their activity. Despite these negative effects, TEs are also important actors in the evolution of genomes by promoting genetic diversity and new regulatory elements. Consequently, it is important to study the epigenetic modifications associated to TEs especially at a locus-specific level to determine their individual influence on gene functioning. To this aim, this short review presents the current bioinformatic tools to achieve this task. For many years, the presence of transposable elements (TEs) has been acknowledged in the genomes of all living organisms, not only because of their large prevalence in some of them but also because they have a profound impact on their functioning and evolution . TEs corDrosophila melanogaster , we can cite for the most recent TIP_finder . The first tool takes advantage of the weight-mapping method Salmon  which are specifically designed for the TE regulation. However, such a type of analyses comes with more technical difficulties. Reads corresponding to piRNAs are even shorter than those corresponding to mRNAs. Indeed, piRNAs have sizes comprised between 21 and 35 bp , which uUrQt , designeUrQt  which alUrQt . The TEtpipeline  proposespipeline  performsPro tool  which usEpiTEome  which drives to transcription silencing . For exaCrunch  considerMapRRcon was developed to allow the identification of proteins binding to TE sequences by mapping ChIP-seq reads to TE consensus sequences after whole-genome alignment (PatChER (There are very few tools that propose to assign histone modifications to TEs . The firlignment . This ap(PatChER . These r(PatChER . In thei(PatChER .HiC-TE (https://gitlab.fi.muni.cz/lexa/hic-te) was proposed to study interactions in nuclei of different repeat sequences, and in particular TEs, in long distance or interchromosomal interactions. The tool identifies and quantifies the interactions of repeats in the 3D organization of the genome using as an input HiC-seq data. It can work according two modes, reference-based and reference-free, to enhance the robustness of the results. Using HiC-seq data can also allow the identification of new TE insertions as proposed by the tool HiTea (Chromosome interactions may also be interesting to study on the TE point of view. Since it has been shown that some TEs may be associated to particular 3D conformation , they maol HiTea . This apAdvances in sequencing technologies have made it possible to develop new bioinformatic tools for more specific analysis of TEs. The most recent tools are beginning to be able to analyze in a finer way, i.e. at the level of the individual copy, the epigenetic modifications associated with them. However, there is still a lot of progress to be made in this field, in particular with the consideration of structural variants between the samples studied independently of a reference genome. Cellular heterogeneity within a tissue must also be taken into account, particularly in the case of cancer studies. For this last possibility, a new methodological development was made in this direction that allows the locus-specific expression analysis of TEs in single cells . New bio