The stage, when tissues and organs are growing, is very vulnerable to environmental influences, but it’s not clear how exposure during this time causes changes to the epigenome and increases the risk of hormone-related illnesses like uterine fibroids (UFs).Developmental reprogramming of myometrial stem cells (MMSCs), the putative origin from which UFs originate, was investigated in vitro and in the Eker rat model by RNA-seq, ChIP-seq, RRBS, gain/loss of function analysis, and luciferase activity assays.When exposed to the endocrine-disrupting chemical (EDC) diethylstilbestrol during Eker rat development, MMSCs undergo a reprogramming of their estrogen-responsive transcriptome. The reprogrammed genes in MMSCs are known as estrogen-responsive genes (ERGs) and are activated by mixed lineage leukemia protein-1 (MLL1) and DNA hypo-methylation mechanisms. Additionally, we observed a notable elevation in the expression of ERGs in MMSCs from Eker rats exposed to natural steroids after developmental exposure to EDC, thereby augmenting estrogen activity.Our studies identify epigenetic mechanisms of MLL1/DNA hypo-methylation-mediated MMSC reprogramming. EDC exposure epigenetically targets MMSCs and leads to persistent changes in the expression of a subset of ERGs, imparting a hormonal imprint on the ERGs, resulting in a “hyper-estrogenic” phenotype, and increasing the hormone-dependent risk of UFs.The online version contains supplementary material available at 10.1007/s00018-023-04919-0. Uterine fibroids (UFs) are the most common benign tumors of the reproductive tract and the most common indication for hysterectomy –3. By thMany risk factors have been recognized as contributors to the development of UF, including exposure to unfavorable environmental conditions . SpecifiMED12 observed in UFICs, are not present in MMSCs [MED12 in various cellular functions, the emergence of MED12 mutations may be considered as one of the key factors involved in the conversion of MMSCs to UFICs. Given the fact that the DNA repair machinery is extensively involved in genetic mutation and genome instability in several diseases [Considering that UFs are monoclonal tumors, it is possible that the dysregulation of committed cells that acquire stem-like features could be responsible for this benign condition . An incrin MMSCs . Due to diseases –23, we rdiseases . Our studiseases –27.Tsc2) tumor suppressor gene and are susceptible to developing UFs, to characterize potential gene-environment interactions. We isolated MMSCs from normal rat myometrium using Stro-1/CD44 surface markers. These Stro-1+ /CD44+ MMSCs also responded to environmental cues, contracting with age, and expanding in response to developmental exposures to EDCs that promote UF development [+ /CD44+ MMSCs as myometrial stem/progenitor cells are targets for ethnic and environmental factors contributing to the increased risk of UF development.With a full understanding of the mechanisms through which environmental EDCs affect the human epigenome, it can be easier to provide clear clinical guidance to address the potential health effects of low-level exposures that are commonly experienced within the general population. In addition, human studies that address environmental exposures are limited in feasibility by ethical concerns for human safety. Therefore, studies in animal models provide great opportunities to reveal links between early-life exposure to EDCs and related diseases . Recentlelopment . Translaelopment . TogetheAlthough MMSCs have been identified as the cells from which UFs originate , 28–31, Tsc-2Ek/+) were approved by the Augusta University Animal Care Committee and Baylor College of Medicine. In all cases, the estrous stage of the animals was determined by histological examination of the vagina and analyzing the levels of ovarian hormones in their blood serum. To determine the impact of early-life environmental exposure on MMSCs, neonatal female Eker rats obtained from an on-site colony were subcutaneously injected with 10 µg of diethylstilbestrol  per rat per day (n = 5) or with 50 µl of sesame seed oil  on days 10, 11, and 12 after birth (PND10-12) according to our previously described protocol [+/CD44+ stem cell isolation.All experiments with female Eker rats  at 37 °C in an incubator in a humidified atmosphere at 2% hypoxia. Characterization of rMMSCs was performed as previously described [Myometrial Stro-1escribed . BrieflyImmunofluorescence imaging of targeted proteins was performed as previously described , 33. EDC+ cells were grown in SmBM medium supplemented with 5% fetal bovine serum, 0.1% insulin, 0.2% hFGF-B, 0.1% GA-10000, and 0.1% hEGF (Lonza). The cells were maintained at 37 °C in an incubator in a humidified atmosphere at 2% hypoxia.Cell culture plates were treated with attachment factor protein  for 5–10 min. Eker rat myometrial Stro-1/CD44To determine the expression of ERGs in response to estradiol treatment, EDC-MMSCs and VEH-MMSCs were grown in endothelial cell growth basal medium  with 5% charcoal-stripped FBS (Thermo Fisher) for at least 3 days. First, the cells were plated in 6-well dishes. On the next day, 10 nM estradiol was added into the medium for 48 h. After treatment, the cells were collected for cDNA synthesis and qPCR analysis.The VEH-MMSCs and EDC-MMSCs were infected with Ad-ERE-luc overnight (16 h) (multiplicity of infection (MOI) = 10). The infection medium was then removed and replaced with the maintenance medium. 10 nM estradiol was added daily, and cells were treated for two days. Luciferase activities were determined using luciferase enzyme assay systems, according to the supplier’s protocol . After completing the estradiol treatment, the lysates were prepared using Glo Lysis buffer. The luciferase activity was adjusted based on the cell count, which was determined by measuring the luminescence signal using the LDH-Glo cytotoxicity assay (Promega). Two independent experiments were carried out, with each experiment performed in triplicate.+/CD44+ stem cells obtained from the uteri of 5-month-old animals that had been exposed to EDC on PND10-12. The RNeasy RNA isolation kit  was used to isolate RNA for RNA-seq analysis. RNA samples were treated with DNase I, and the purity and the quality of RNA were checked using a Bioanalyzer prior to cDNA synthesis. cDNA libraries were constructed using SPRI-works Fragment Library System I , and were then PCR-enriched and purified. For cluster generation, 10 pM cDNA was loaded into the paired-end flow cell on cBOT and then loaded on the HiSeq2500 platform  to generate single 36 bp sequence reads. Sequence reads were aligned to the rat reference genome rn6 using Hisat2, and aligned read counts were analyzed using EdgeR for differential gene expression.To identify developmentally reprogrammed EDC gene targets, whole-genome transcriptome sequencing and data analysis were performed using myometrial Stro-1Ct) was used to calculate the relative quantities of nucleic acid sequence.For qPCR, the same RNA used for RNA-seq was reverse-transcribed into first-strand cDNA with the Superscript III cDNA Transcription Kit (Invitrogen) using standard techniques. All assays were conducted in 96-well format, and each sample was run in triplicate. Real-time fluorescence detection of PCR products was performed using the following thermo-cycling conditions: 1 cycle of 95 °C for 2 min followed by 40 cycles of 95 °C for 5 s, and 60 °C for 30 s. The sequences of the primers used in this study are listed in Table S2. 18S ribosomal RNA was used as an endogenous control for gene expression. For data analysis, the comparative method  was employed following the instructions provided by the manufacturer. Cells were treated with 1.5% formaldehyde to crosslink proteins to DNA; then, they were incubated with 125 mM glycine and washed with chilled PBS. After centrifugation, cells were re-suspended in cell lysis buffer (PBS containing 0.5 mM EDTA and 0.05% Triton X-100) and collected via centrifugation. Each cell pellet was re-suspended in nuclear lysis buffer . Chromatin preparations were sonicated in a Bioruptor  to obtain fragments comprising 100–500 bp in length. ChIP-sequencing was carried out on the HiSeq 2500 platform (Illumina) at the Next-Generation Sequencing Core at the University of Texas MD Anderson Cancer Center Science Park. The Bioo Kit Option 2 protocol was utilized to prepare sequencing libraries, with 1.7–10 ng of DNA per sample.Chromatin immunoprecipitation sequencing (ChIP-seq) was performed as previously described , 35. To DNA isolated via ChIP was then analyzed by qPCR using SYBR Green Master Mix on Bio-Rad CFX96 real-time PCR system. Primers were designed to hybridize at the transcriptional start sites that exhibited differential DNA enrichment between VEH- and EDC-MMSCs; the sequences of the primers are listed in Table S2. The results are presented as the percentage of the input DNA (% input) co-precipitated with anti-H3K4me3 or control antibody. The antibodies used for ChIP-seq are shown in Table S1.q value < 0.01. Differential regions were annotated for nearby genes using BEDTOOLS; specifically, we considered genes with a differential region within 3 kb from its TSS. Venn diagram analysis of genes associated with differential peaks was carried out using the Python language scientific library. ChromHMM was used with the H3K4me3 ChIP-seq data to generate an epigenomic states partition of the rat MMSC epigenome. Epigenomic states were annotated based on the emission matrix following the approaches used by the Encode and the NIH Epigenome Roadmap consortia. Overlap of differential regions with epigenomic status was determined using BEDTOOLS. Odds-ratio enrichments for individual epigenomic states were then computed based on the cumulative genome-wise size of each epigenomic state. Odds ratios were graphed using GraphPad Prism 8.02.ChIP-seq reads were trimmed for low-quality base pairs using TrimGalore. Data was mapped to the rat genome build UCSC rn6 using the bowtie2 software. Duplicate reads were removed, then ChIP-seq tracks were prepared using bedtools, normalized to reads per million reads mapped (rpm). ChIPseq tracks were visualized using the Integrative Genome Viewer software. Differential ChIP-seq regions were determined using the diffReps software using the Gtest, with significance achieved for a fold change exceeding 1.5 × , and an FDR-adjusted MspI, ligated to sequencing adaptors, treated with bisulfite, and amplified by PCR. The final libraries were quantitated with Qubit (ThermoFisher Scientific), and the average size was determined on a Fragment Analyzer . The libraries were diluted to 10 nM and further quantitated by qPCR on a CFX Connect Real-Time qPCR system  for accurate pooling of barcoded libraries and maximization of the number of clusters in the flowcell. Quantitated libraries were prepared for sequencing on a NextSeq MO flowcell according to Illumina recommendations. The final prepared libraries were loaded on the flowcell at 2 pM, and sequencing was performed with single reads of 100 bp with a 12 bp index read.Reduced Representation Bisulfite Sequencing (RRBS) libraries were constructed using the RRBS Methyl-Seq kit from Nugen . Briefly, 100 ng of high-molecular-weight DNA was digested with For targeted next-generation sequencing (NGS), amplicon sequencing on the NGS platform was used to determine the DNA methylation status of the promoter regions of ERGs as previously described . GenomicWestern blot analysis was performed as previously described . The levTasp1) expression. This was achieved using viral particles from three rat shRNA constructs . EDC-MMSCs were cultured in 6-well plates and incubated with lentiviral particles at a virus multiplicity of infection (MOI = 10) overnight. Subsequently, the medium was replaced, and the cells were allowed to grow for five days. Lysates and RNA were then prepared from the cells and used to measure the protein and RNA levels in MMSCs with Tasp1 rat shRNA lentiviral particles or scrambled controls.To determine methyltransferase specificity for changes in H3K4me3 in response to EDC, we conducted a transient knockdown of Taspase1 (Conditioned medium from MMSCs was prepared as previously described . DiffereThe MTT cell proliferation assay was performed as previously described .t test (GraphPad Prism) was used to determine whether there were significant differences between VEH and EDC treatment groups for gene expression , fluorescent signals (measured by immunofluorescence microscope), protein levels (measured by Western blot), luciferase activity (measured by luciferase activity kit), and histone modification (measured by ChIP-qPCR). p ≤ 0.05 was considered significant.Student’s t test was done to examine the correlation between EDC-regulated genes and the H3K4me3 enrichment/DNA methylation status of reprogrammed genes. In addition, the Wilcoxon test was used to examine the statistical significance in DNA methylation levels between EDC- and VEH-MMSCs.Fisher’s exact z‐score was calculated to infer the activation states of implicated biological processes. Over-representation Analysis (ORA) was used to determine over-represented biological processes in our experimentally derived gene list. The p value is calculated by hypergeometric distribution.Top biological functions and canonical pathways associated with the differentially expressed mRNA data set were identified with Ingenuity Pathway Analysis (IPA) (Qiagen). A To identify differences in gene expression that drive the pathogenesis of UFs in the context of early-life EDC diethylstilbestrol (DES) exposure, we used the Eker rat model . NeonataORA using Hallmark Gene Sets showed that genes whose expression was altered by EDC were enriched for multiple cellular pathways, including but not limited to estrogen response, metabolism, inflammatory response, androgen response, and hypoxia Fig. d. These Endocrine disruptors, including DES, are substances that interfere with the actions of natural hormones, such as estradiol, in the body responsible for development. Since the natural hormone of estradiol acts via several mechanisms, we focused on the effect of EDC exposure on estrogen responsiveness. Therefore, 299 ERGs from the Hallmark (MSigDB) early estrogen response and late estrogen response pathways were examined in EDC-MMSCs and VEH-MMSCs. We identified 87 ERGs whose gene expression was dysregulated in EDC-MMSCs compared to VEH-MMSCs. Among them, 60 ERGs were upregulated, and 27 ERGs were downregulated in EDC-MMSCs compared to VEH-MMSCs Fig. a. The top < 0.001), suggesting that ER-mediated signaling might be activated due to early-life exposure to EDC. To test this hypothesis, we infected the EDC-MMSCs and VEH-MMSCs with ad-ERE-luc and measured the luciferase activity of EDC-MMSCs and VEH-MMSCs. As shown in Fig. Pgr, Cd9, Bcl11b, and Mpped2. In contrast, the expression of Pgr, Bcl11b, and Mpped2 showed no changes, while Cd9 expression was increased, in VEH-MMSCs after estradiol treatment. Interestingly, we also observed that while the expression of Pgr, Cd9, and Mpped2 was higher in EDC-MMSCs  than VEH-MMSCs when grown in an estradiol-free medium, the fold changes in the expression of these ERGs were more significant in EDC-MMSCs when grown in an estradiol-containing medium  in EDC-MMSCs following estradiol treatment, estradiol downregulated the expression of Tgm2 encoding transglutaminase 2, which catalyzes the cross-linking of proteins, in EDC-MMSCs. These studies suggested that developmental EDC exposure reprogrammed the estrogen signaling pathway leading to hyper-estrogenic responsiveness in MMSCs.Since one of the main estrogenic activities is through estrogen receptor (ER), we determined the protein levels of ER and demonstrated that EDC exposure dramatically increased the ER protein levels in EDC-MMSCs compared to VEH-MMSCs .After discovering that exposure to EDC during the neonatal stage significantly modified the gene expression pattern of ERGs, we next sought to identify the underlying mechanism responsible for these EDC-induced changes in gene expression. In our investigation, we turned our attention to Mixed-lineage Leukemia1 (MLL1), an extensively studied mammalian counterpart of the yeast protein Set1, which is a component of the COMPASS complex (complex of proteins associated with Set1) . MLL1 is an initial 500-kDa protein cleaved by Taspase1 (Tasp1) to produce a mature MLL1 N320/C180 heterodimer . Specifip = 0.0002, Fisher’s exact test).To determine whether MLL1 plays a role in regulating the expression of ERGs after EDC exposure, whole-genome ChIP-sequencing (ChIP-seq) for H3K4me3 was performed. The results, depicted in Fig. Esr1) serves as the upstream master regulator of ERG expression, we first determined whether developmental exposure to EDC reprograms the Esr1 gene via H3K4me3. As shown in Fig. Esr1 gene was seen in EDC-MMSCs compared to VEH-MMSCs. Similar patterns of H3K4me3 enrichment were also found in other ERGs, including Cxcl12, Cd9, Mpped2, and Tgm2  was validated by qPCR . We next measured the RNA expression of H3K4me3-reprogrammed ERGs after Tasp1 knockdown. As shown in Fig. Tasp1 led to a substantial reduction in the expression of EDC-reprogrammed ERGs, including Cd9, Pgr, Ar, Cxcl12, and Tgm2 as well as Esr1. These results suggest that activated MLL1 plays a crucial role in reprogramming ERGs, leading to increased expression of ERGs in EDC-exposed MMSCs.Based on these findings, we hypothesized that blocking the cleavage of MLL1, mediated by Tasp1, would reverse the increased expression of reprogrammed ERGs by EDC exposure. To address this hypothesis, we knocked down Since DNA methylation is another key mechanism through which gene expression is regulated, we next determined whether developmental exposure to EDC alters ERG expression through altered DNA methylation status. Western blot analysis demonstrated that the protein levels of DNMT3A were significantly decreased in EDC-MMSCs as compared to VEH-MMSCs Fig. a. These Regarding the genes upregulated by EDC exposure, when applying a methylation threshold of 33%, approximately 25.58% displayed the expected mark, 14.59% exhibited the opposite mark, 7.53% showed mixed directions, and 52.15% did not exhibit any methylation mark . On the other hand, for the genes downregulated by EDC exposure, approximately 10.15% displayed the expected mark, 27% showed the opposite mark, 8.49% exhibited mixed directions, and 54.35% did not exhibit any methylation mark . The correlation between EDC-regulated gene expression and DNA methylation with 10% and 50% thresholds is also shown in Figure S3.Esr1, the master of estrogen signaling. As shown in Fig. Esr1 exhibited hypo-methylation in EDC-MMSCs compared to VEH-MMSCs, suggesting reduced DNA methylation at the CpG island of Esr1 may contribute to increased Esr1 expression in EDC-MMSCs .Since CpG island methylation status is linked to gene expression, we next used the targeted bisulfite NGS approach to measure the DNA methylation levels at the CpG islands of Ar, Cxcl12, Cd9, Bcl11b, Mpped2, and Tgm2, revealed hypo-methylation within the CpG islands in their promoter regions. Of the 17 CpG sites examined in Ar, all but one  showed decreased methylation levels in EDC-MMSCs compared to VEH-MMSCs (p < 0.05). Similar decreases in methylation levels were found in other ERGs, including Cxcl12, Cd9, Bcl11b, Mpped2, and Tgm2. Moreover, we measured the DNA methylation levels of the Pgr promoter regions and observed hypo-methylation of 7/19 (36.8%) CpG sites in the promoter region. The methylation levels of Cxcl12 and Pgr were lower in both EDC-MMSCs and VEH-MMSCs. Wilcoxon test analysis showed that the DNA methylation levels of Esr1 and those of 7 ERGs covering 102 CpG sites exhibited a significant difference between EDC- and VEH-MMSCs (p < 0.05) .We then determined whether other ERGs that exhibited enrichment of H3K4me3 were differentially methylated. Six ERGs, including Integration analysis of RRBS, ChIP-seq, and RNA-seq demonstrated that among 1474 EDC-upregulated genes with expected direction, 47.1% of genes were correlated with H3K4me3 enrichment. We also observed that 25.6% of genes with DNA hypo-methylation were associated with EDC-induced upregulation of RNA expression using a 33% methylation threshold. Among 1448 EDC-down regulated genes, 33.5% of genes associated with H3K4me3 reduction. We also observed that 10.2% of genes with DNA hyper-methylation correlated with EDC-down-regulated genes . Similar findings with significant differences were seen in a comparison of regulated gene numbers between the H3K4me3 enrichment and reduction group, as well as between the hypo-methylation versus hyper-methylation group  plays a dominant role in regulating gene expression in MMSCs genome-widely in response to EDC exposure. We also noticed that both DNA methylation and H3K4me3 co-regulate some of the same targets in response to EDC exposure. As shown in Fig. S4, 23.1% of EDC-upregulated genes exhibited overlap between H3K4me3 and RRBS using 33% methylation thresholds. On the other hand, 11.5% of EDC-down-regulated genes showed an overlap between H3K4me3 and RRBS. We also compared the number of genes with the integration of DNA methylation and H3K4me3 status. We found a significant difference between the number of genes exhibiting H3K4me3 enrichment and hypo-methylation versus those showing H3K4me3 reduction and hyper-methylation .The status of H3K4me3 enrichment, DNA methylation, and RNA expression of SCs Fig. f. These −/CD44−) from the myometria of Eker rats developmentally exposed to DES. As shown in Fig, 8a, out of four ERGs  with enriched H3K4me3, all were significantly altered. Notably, two genes (Bcl11b and Tgm2) showed downregulated expression in DES-exposed DMCs compared with DES-exposed MMSCs, while the other ERGs (Cd9 and Cxcl12) exhibited upregulation in DMCs compared to MMSCs. Given the fact that these four genes are upregulated in DES-MMSCs compared to VEH-MMSC, one may consider that some of the reprogrammed ERGs are MMSC-specific  was upregulated in DMCs in the presence of CM from EDC-MMSCs as compared to CM from VEH-MMSCs , we isolated adult rat DMCs from normal myometrium and exposed them to conditioned medium (CM) from VEH-MMSCs or EDC-MMSCs Fig. c. First,SCs Fig. e.Accumulating evidence demonstrates that environmental exposures to EDCs can reprogram the epigenome of developing tissues in such a way as to increase susceptibility to disease later in life , 40, 41.Tsc2) tumor suppressor gene and are susceptible to the development of UFs. By isolating myometrial Stro1+/CD44+ stem cells (MMSCs), the type of cells from which potential UFs originate, we could examine the effect of early-life exposure to EDC through multi-omics analyses. Our proof-of-concept using combined ChIP-seq, DNA methylation, and RNA-seq analyses in this model demonstrates that both DNA methylation and histone modification at H3K4me3 in MMSCs, are highly associated with the increased expression of ERGs that have been linked to increased risk of UFs.Here, we used Eker rats that carry a germ-line mutation in the tuberous sclerosis complex 2  before UF development, which typically occurs in late adult/old age in response to developmental EDC exposure Fig. . This alEsr1 and Pgr), as well as newly characterized regulators, such as Cd9 and Cxcl12, were identified. Over-representation analysis revealed the signaling pathways that were altered in EDC-exposed MMSCs, including estrogen response, androgen response, Inflammation, apoptosis, hypoxia, and metabolism, among others. Crucially, the expression of numerous estrogen-responsive genes (ERGs) associated with both estrogen activity and disease progression significantly rose due to exposure to EDC during early life. Similarly, like stem cells found in other hormone-dependent tissues, such as the breast and prostate [Changes in the gene expression of both known uterine developmental regulators  and H3K4me3 in MMSCs. TASP1, a protein responsible for regulating MLL-1 activity and the production of MLL1C, was found to be involved in this process. Importantly, when Tasp1 was knocked down, it reversed the EDC-induced increase in the expression of reprogrammed genes, indicating its crucial role in mediating the effects of EDC exposure. To our knowledge, this is the first demonstration that EDC exposure targets a histone methyltransferase, an enzyme that leads to epigenomic reprogramming in MMSCs. In a previous investigation focusing on prostate development, it was discovered that exposure to EDCs resulted in the activation of MLL1, leading to an increase in the presence of H3K4me3 at genes associated with prostate cancer. These changes persisted into adulthood. However, it is unclear how EDCs target the cells of origin in prostate carcinogenesis. In this study, we addressed this critical question related to the initiation of UFs. By isolating MMSCs from exposed and unexposed myometrium, we were able to identify specific epigenetic changes that occur in response to EDC exposure in MMSCs, the cells from which UFs originate. Here, we used DES as a proof of concept for EDC exposure. In a prior study, it was documented that exposing individuals to genistein, an EDC present in soybeans, during their developmental stages facilitated the growth of UFs, leading to an increase in both tumor incidence and multiplicity . In thisIt is widely recognized that multi-omics analyses can provide a comprehensive understanding of the epigenomic network that regulates the transcriptomic landscape and the biological phenotype , 47. In Comparing the epigenetic pattern between normal myometrial (MyoN) and myometrium from the uterus with UFs (MyoF) will help to determine the epigenetic risk signatures. A few studies –51 deterTsc2 allele in the myometrium of Eker rats is rarely found in humans, the Eker rat model may be considered a phenocopy of the human situation in that MED12 mutations occur in over 80% of patients with UFs. Given the fact that MED12 mutations may appear due to a dysfunctional DNA repair system, one may consider that early-life exposure to EDCs hijacked epigenomic plasticity leading to hyper-responsiveness to estrogen hormones, eventually resulting in genomic abnormalities that may include MED12 mutations. It is well documented that DNA damage is linked to hormone signaling pathways contributing to cancer development [Although the loss of the elopment , 53. Steelopment . We prevelopment , and vitelopment . Crosstaelopment .In this study, we also identified a paracrine effect of EDC-MMSCs/VEH-MMSCs on DMCs. The secretome from EDC-MMSCs enhanced DMC proliferation compared to that of VEH-MMSCs, concurrent with the activation of β-catenin signaling, one of the key pathways contributing to the growth and development of UFs. The paracrine effect of DMCs on a side population of human UF cells has been described previously . EstrogePgr is epigenetically reprogrammed (H3K4me3 enrichment), concomitant with increased expression of Pgr in EDC-exposed MMSCs. Moreover, labor requires increased expression of genes that mediate myometrial activation and optimal responsiveness to uterotonic agonists, such as stimulatory prostaglandins and oxytocin [Pgr, we found that the oxytocin receptor (Oxtr) involved in labor was also H3K4me3-dependently reprogrammed in EDC-MMSCs.Notably, our studies demonstrate that developmental exposure to EDC regulates multifaceted signaling pathways, possibly impairing uterine function, and leading to other uterine diseases. One such disease is preterm labor, which the progesterone receptor pathway can regulate. Changes in the ratio of progesterone receptor isoforms could impair the function of progesterone, and the premature onset of labor is associated with increased PR-A expression , 58. Ouroxytocin . In addi (Ptgs2), another gene involved in labor, was observed in EDC-MMSCs compared to VEH-MMSCs. However, the H3K4me3 enrichment at Ptgs2 was not observed, suggesting that an alternative mechanism is responsible for the altered expression of this gene. Additional studies are needed to determine whether Ptgs2 expression is regulated by DNA methylation or other epigenetic mechanisms such as histone marks or miRNA. These abnormal reprogramming and increased expression of labor-related genes in MMSCs may promote preterm labor due to environmental exposures.Moreover, increased expression of prostaglandin-endoperoxide synthase 2Several other EDCs, such as phthalates, have been shown to correlate with UF pathogenesis , 59. GivThis study used a pooled testing strategy combining MMSCs from multiple defined rats and testing them as a group for numerous cellular and molecular analyses. Since progenitor-type cells consist of a small percentage of populations in the myometrial and UF tissues , 19, 45,We present compelling evidence demonstrating that xenoestrogens (EDCs) directly target and affect the epigenetics of MMSCs, which are the cell source for UFs. Through epigenomic profiling, we unveil new insights into the mechanisms that regulate the transcriptional landscape of MMSCs. Exposure to EDCs during early life, such as DES, causes significant changes in the expression patterns of multiple genes, including various estrogen-responsive genes (ERGs), mediated by histone and DNA methyltransferases in MMSCs. These alterations in gene expression modify the defining characteristics of MMSCs, leading to a "hyper-estrogenic" phenotype characterized by reduced DNA repair capacity and an elevated risk of hormone-dependent uterine diseases like UFs.Supplementary file1 (XLSX 18 kb)Supplementary file2 (XLSX 18 kb)Additional File 3: Table S3, The methylation sites for ERGs (XLSX 19 kb)Additional File 4: Fig. S1. ERG expression in EDC-MMSCs and VEH-MMSCs. a) Differential expression of ERGs in EDC-MMSCs compared to VEH-MMSCs identified by RNA-seq and bioinformatics analysis. B) Overlap of EDC-regulated genes in MMSCs with estrogen early and late response genes. Additional File 5: Fig. S2. Diagram of COMPASS complex and its pathway. Diagram showing the Compass complex linking to the active chromatin via H3K4me3 mark (left panel), and how MLL1 epigenetic pathways are activated by the protease Taspase 1 (right panel). Additional File 6: Fig. S3. Bisulfite NGS. Targeted bisulfite NGS: the quantitative methylation levels of 7 ERGs including Cxcl12, Ar, Bcl11b, CD9, Mpped2, Tgm2, and Pgr were measured by bisulfite NGS in VEH- and EDC-MMSCs. The Wilcoxon test was used to determine significant difference in the DNA methylation levels of eight ERGs covering 102 CpG sites around the promoter regions of these genes in EDC- and VEH-MMSCs. ***p<0.001. Additional File 7: Fig. S4. Integration of H3K4me3/DNA methylation near EDC-regulated genes. a) The Peaks/methylation near EDC-regulated genes. b) The overlap between H3K4me3 and RRBS (PDF 351 kb)Below is the link to the electronic supplementary material.


It is unclear to familial screen time in early childhood is associated with the subsequent diagnosis of attention-deficit and hyperactivity disorder (ADHD). Our study is to evaluate the association between screen time during early childhood in families and the incidence of ADHD.We conducted a population-based birth cohort study by using the Taiwan Birth Cohort Study, which recruited 24 200 mother–child pairs when children were 6 months old. Screen time exposure for children and parents were collected at the age of 18 and 36 months. Whether the child has ever been diagnosed with ADHD was determined at a follow-up interview at age 8. Factors including socioeconomic factors and screen time were analyzed using logistic regression to determine their association with the rate of ADHD.A total of 16 651 term singletons were included in the final analysis. Of them, 382 (2.3%) were diagnosed as having ADHD before the age of 8 years. No significant relationship between children’s or fathers’ screen time and ADHD was noted. When compared to children whose mothers spent less time on screens, those whose mothers spent more than 3 h a day on screens when the child was 3 years old exhibited a higher incidence of ADHD .Higher maternal screen time when the child was 3 years old was associated with an increased incidence of ADHD in this population-based study. However, children’s screen time did not find related to ADHD. We found that it was the mother’s screen time, who typically serves as the primary caregiver in our study participants, not the child’s, that mattered. In addition to superficial screen use time, future research is needed to replicate the findings and clarify mechanisms underlying this association.The online version contains supplementary material available at 10.1186/s12888-023-05242-5. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) introduced a new diagnostic category termed neurodevelopmental disorders (NDDs) in 2013 [DSM-5 criteria was 5–11% in individuals aged < 18 years [The 18 years . A natiotion DSM- introduc18 years . This buEmerging evidence suggests a relationship between screen time and ADHD symptoms. Using a US national longitudinal sample, a study published in 2004 determined the association between early television viewing at the ages of 1 and 3 years and subsequent inattention at the age of 7 years . A meta-Although studies have demonstrated the association between screen time and ADHD development, the role of screen time in ADHD remains elusive due to the presence of heterogeneous evidence across different studies. Furthermore, parenting should not be overlooked when evaluating the effects of media use in early childhood, especially when media can serve as a substitute for parent–child interactions . Kids inTo the best of our knowledge, no study had evaluated the association between screen time and ADHD by using a Taiwanese population sample. Therefore, this study examined the association between screen exposure in not only children but also parents and ADHD development during early childhood by using a representative birth cohort sample in Taiwan.The Taiwan Birth Cohort Study (TBCS) is a national prospective longitudinal cohort study. In this study, a 2-stage stratified random sampling method was used to select infants born in 2005 in Taiwan from the National Birth Report database. We included 24 200 representative mother–infant pairs, which represented approximately 12% of all births in Taiwan in 2005. The details are described previously . StructuSeveral interviews were conducted when children were aged 6 months, 18 months, 3 years, and 8 years. We collected information on maternal conditions during pregnancy, infant birth outcomes, child health conditions, parental socioeconomic status, prenatal traffic-related air pollution , and othThe status of parent-reported community diagnoses of ADHD was determined using questionnaires administered by trained interviewers. The response “yes” from the main caregiver was selected when ADHD was diagnosed by a physician, psychologist, or special educator by the age of 8 years.Covariates that were adjusted for included the child’s sex, maternal age at childbirth, maternal education level, urban living, household income, marital relationship, family supports after childbirth, maternal unintentional pregnancy and postpartum smoking, including secondhand smoke , 18. InfAdditionally, primary daytime and nighttime caregivers were surveyed, and their responses were combined to identify the caregivers responsible for the children throughout the day when they were 18 months and 36 months old. The frequency of exposure to violent screen content, including scenes of fighting and quarrels, as well as the consumption of pornographic and sexual content, was documented by the main caregiver when the children were 36 years old.The binary outcome variable was the parent-reported community diagnosis of ADHD in children, and the main predictors were children’s and parents’ screen time in early childhood. The relationship between children’s and parents’ screen time was estimated using the Spearman rank-based correlation test. Furthermore, a logistic regression model was used to determine the association of children’s and parents’ screen time when the children were aged 18 months and 3 years with the rate of ADHD after adjustment for confounders. Furthermore, the relationships between media content and the subsequent incidence of ADHD were also assessed. The findings are reported as the adjusted odds ratio (aOR) and 95% confidence interval (CI) for the occurrence of ADHD. Statistical analyses were performed using JMP . A 2-sided P value of < 0.05 indicated statistical significance.Among the 16 651 mother–infant pairs included in the final analysis , the mother alone (36.2%), the parents themselves (2.1%), and about 18% of the children were mainly raised by relatives, including grandparents. Furthermore, at about 36 months, 28.8% of the children were mainly taken care of by the mother alone, 23.8% were cared for by the mother and relatives, 20.6% went to kindergarten or nurseries during the day, and 12.1% were raised mainly by relatives. Only 6.7% of the participants were primarily cared for with the help of babysitters . The WHO defines it as “time spent passively watching screen-based entertainment , and it does not include active screen-based games where physical activity or movement is required.” During 2005 to 2008 when the TBCS was conducted and data on screen time were collected, the research sample was less than 3 years old. Taiwanese families then did not use mobile electronic devices, such as smartphones and tablets, on a daily basis. Although the iPhone was first launched in 2007, it was not officially available in Taiwan for purchase until the end of 2008. The iPad was launched in 2010. Therefore, at that time, toddlers younger than 3 years mainly watched television, videotapes, DVDs, and some computers. The average exposure time was 1 h and 45 min in 18-month-old children and 2 h 42 min in 3-year-old children. This screen time of 3-year-old children is similar to that of more than 2 h per day, including computers, communication, and consumer electronics, determined from KIT in 2013 to 2014 , and in Studies have reported a relationship between screen time, including television viewing, game playing, video watching, and computer using, and ADHD symptoms or behaviors and externalizing behaviors , 10, 25.The Differential Susceptibility to Media Effects Model (DSMM) is a theory in media studies that suggests individuals vary in their susceptibility or responsiveness to media messages and content . This moWe assumed that the adverse effect of longer exposure among mothers during early childhood fits the social susceptibility hypothesis in the DSMM, such as parenting style and media-specific parenting. As demonstrated in Supplementary Table The strength of this study is that it provides new insights into the relationship between early childhood screen time among children and mothers and ADHD onset. The study benefited from a representative sample of Taiwanese infants born in 2005 with a population-based cohort design that responded well to detailed data on covariates. Furthermore, the independent variable was collected when children were aged 18 and 36 months, which minimized recall bias for the target dependent disease or symptom collected years later. A limitation of the current study is that ADHD and screen time were assessed on the basis of parental reports, which may have contributed to reporting bias. The prevalence of this diagnosis is similar to that in the same age group in Taiwan’s NHIRD. Thus, our sample of children with ADHD is likely representative of the community-based population. That said, self-reactive overdiagnosis was unlikely to occur in our study sample. Despite concerns regarding the underdiagnosis of ADHD, ADHD diagnosed by the age of 8 years might reasonably represent a proportion of more severe cases, and some children with ADHD may be observed in subsequent years. The study may be, therefore, biased toward children with more severe ADHD symptoms. Moreover, exact data on the amount of screen time and the time children spent gazing at the screen were not available. Information on pattern, such as continuous exposure and interval rest periods, or parenting factors while watching television/videotapes were not available in our dataset. Thus, the variation in these parameters cannot be explored in the current study. Obtaining such information would have been impractical in a large cohort study conducted more than 10 years ago. People are likely to underestimate the amount of TV they or their children watch, especially those with more screen time. In theory, this misclassification might reduce the observed association. Nonetheless, the observation of a significant relationship between maternal screen time and ADHD diagnoses suggests that the actual effect may be stronger. Additionally, 8% of survey participants were not followed up when their child was approximately 8 years old and did not provide a reason for the refusal. Therefore, whether rejection is associated with clinical features related to ADHD remains unclear. Overall, the impact of bias is acceptable because of the high participation rate.Nowadays, we are expecting to see improvements in measurement and inspection equipment to more accurately assess screen viewing, for example, using portable devices and identifying the wavelength and brightness of screen light sources. Besides, different forms of screens, content, whether there is parental guidance to watch, watching TV alone or with adults, etc. are all aspects worthy of further research and exploration. Moreover, the causes and mechanisms behind screen time that cause health problems need to be further clarified. Nevertheless, long-term health effects, dosage effects, recommendations for appropriately limiting screen time or duration per session, appropriate timing of viewing  all require research for further clinical application. Last but not the least, with the proliferation of smartphones, tablets, and other multifunctional devices in our daily lives, detailed information on screen exposure should be captured in the near future.The current study suggests that increased maternal screen time when the child was 3 years old was associated with an increased incidence of ADHD in children by the age of 8 years. Evidence of a link between a child’s screen time during early childhood and the later diagnosis of ADHD was not observed. The findings may suggest a connection between the screen time of the primary caregiver and subsequent ADHD diagnosis, given that the primary caregiver is typically the mother in our sample. Further research with more detailed measurements for screen time is warranted to clarify these findings.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3


Observational studies have found Attention Deficit Hyperactivity Disorder (ADHD) to be associated with an increased risk of adverse outcomes as well as with early risk factors; however it is not clear whether these associations reflect causal effects. Alternatives to traditional observational studies are needed to investigate causality: one such design is Mendelian randomization (MR), which uses genetic variants as instrumental variables for the exposure.In this review we summarise findings from approximately 50 studies using MR to examine potentially causal associations with ADHD as either an exposure or outcome.To‐date, few MR ADHD studies have investigated causal evidence with other neurodevelopmental, mental health and neurodegenerative conditions but those that have suggest a complex relationship with autism, some evidence of a causal effect on depression and limited evidence of a causal effect on neurodegenerative conditions. For substance use, MR studies provide evidence consistent with a causal effect of ADHD on smoking initiation, but findings for other smoking behaviours and cannabis use are less consistent. Studies of physical health suggest bidirectional causal effects with higher body mass index, with stronger effects for childhood obesity, as well as some evidence of causal effects on coronary artery disease and stroke in adults and limited evidence of causal effects on other physical health problems or sleep. Studies suggest bidirectional relationships between ADHD and socio‐economic markers and provide some evidence that low birthweight may be a causal risk factor for ADHD, while bidirectional evidence has been found for some environmental factors. Finally, there is emerging evidence of bidirectional causal links between ADHD genetic liability and biological markers of human metabolism and inflammation.While MR has advantages over traditional observational designs in addressing causality, we discuss limitations of current ADHD studies and future directions, including the need for larger genome‐wide association studies (and using samples of different ancestries), and for triangulation with different methods. Observational studies have found ADHD to be associated with an increased risk of adverse outcomes as well as with early risk factors; however it is not clear whether these associations reflect causal relationships. In this review we summarise findings from approximately 50 studies using Mendelian randomization (MR) to examine causality with ADHD as either an exposure or outcome. Observational studies have found ADHD to be associated with an increased risk of both adverse outcomes and early risk factors: it is not clear whether these reflect causal evidence.We summarise findings from approximately 50 studies using MR to examine causality with ADHD as either the exposure or outcome.MR studies have examined causal evidence between ADHD and (i) neurodevelopmental, mental health, neurodegenerative conditions and personality, (ii) substance use, (iii) a range of physical health and sleep measures, (iv) cognitive ability test scores and indicators of socio‐economic status, (v) birthweight, gestational age and environmental exposures, and (vi) biological markers.MR has advantages over traditional observational designs in examining causal evidence in ADHD, which could have prevention and treatment implications.Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition that typically onsets in early in development  studies have identified several risk factors for ADHD. ADHD is highly heritable – twin studies estimating 70%–80% heritability and limited evidence for shared familial environmental contributions  as instrumental variables (proxies) for the exposure: as shown in Figure In this paper we will summarise findings from studies using MR to examine potentially causal evidence with ADHD as either an exposure or outcome. We focus on 2‐sample MR studies, in which data on the exposure and outcome of interest are identified from summary statistics of two different GWAS (one for the exposure and one for the outcome), although some alternative designs are briefly covered. Our search strategy for identifying MR studies of ADHD is outlined in the Supporting Information.Despite substantial phenotypic overlap, few MR studies have investigated causal evidence between ADHD and other conditions involving the brain, mainly due to the challenges of identifying causal effects with genetic instruments that display extensive pleiotropic effects across different conditions. Two studies of ADHD and autism spectrum disorder (ASD) suggested a complex relationship, with evidence of a causal effect of genetic liability to ASD on ADHD and of genetic liability to ADHD on ASD, with effects in both directions showing substantial heterogeneity     inflammatory bowel disease . At the moment, there is limited evidence of ADHD genetic liability causing other physical health problems  or affecting sleep.Three MR studies investigating ADHD and cognitive ability test scores using the same cognitive ability GWAS have found evidence of a causal effect of genetic liability to higher cognitive ability test scores on lower risk of ADHD, with little evidence of horizontal pleiotropy  and two measures of sexual behaviour  with bidirectional effect for the latter  with genomics to investigate causal roles of blood biomarkers on ADHD.One 2‐sample MR study integrated metabolomic and genomics and used data from a GWAS of 486 metabolites  have been tested for causal effects on psychiatric disorders, including ADHD, with the MR study not identifying any potential causal effects , ADHD MR study findings – whether ADHD is the exposure or outcome – have theoretical, prevention and treatment implications. Where study findings are consistent with a causal effect of ADHD on outcomes, such as have been observed for depression and smoking initiation, these suggest that effective treatment of ADHD may reduce the risk of these outcomes, although triangulation of findings is needed (see below). Multivariable MR can provide indications of potential causal pathways of these relationships, for example, findings implicating childhood obesity as a mediator of the causal link between ADHD and coronary artery disease (Leppert et al., Where MR studies have investigated ADHD as the outcome, findings consistent with a casual effect on ADHD, such as have been observed for low birthweight, suggest that targeting these exposures in those at risk of ADHD may reduce risk of ADHD. However, it is not clear what causal effects of phenotypes such as low birth weight capture, maybe other underlying foetal problems. Finally, emerging evidence of bidirectional causal links between ADHD genetic liability and biological markers of human metabolism and inflammation may provide clues as to the biological underpinnings of ADHD, which could help refine treatment options.While MR studies have advantages over traditional observational designs in minimising unmeasured confounding and reverse causation, this is based on MR assumptions (Davey Smith & Hemani, Population stratification is another source of bias in MR: many of the studies we reviewed restricted the ADHD GWAS used to European origin samples (which comprise the majority of samples included in the largest ADHD GWAS; Demontis et al., Finally, triangulation between MR findings and those of other methods is required as different designs will have different sources of bias (Lawlor et al.,  In conclusion, to‐date approximately 50 studies have been published examining potentially causal evidence with ADHD as either the exposure or outcome. Few MR studies investigated causal effects between ADHD and other conditions involving the brain, but those that have suggest a complex relationship with ASD, some evidence of a causal effect on depression and limited evidence of a causal effect on neurodegenerative conditions. Mendelian randomization studies of substance use provide evidence consistent with a causal effect of ADHD on smoking initiation, but findings for other smoking behaviours and cannabis use are less consistent. Several studies have examined causal evidence with different aspects of physical health: these suggest bidirectional causal effects with higher BMI, with stronger effects for childhood obesity, as well as some evidence of causal effects on coronary artery disease and stroke in adults, but limited evidence of causal effects on other physical health problems or sleep. Studies suggest bidirectional relationships between ADHD and socio‐economic markers, which could reflect an increased likelihood for ADHD symptoms being impairing in the absence of sufficient or additional resources and that ADHD can lead to barriers in attaining higher education and income. Studies have also provided some evidence that low birthweight may be a causal risk factor for ADHD, while bidirectional effects have been found for some environmental factors. Finally, there is emerging evidence of bidirectional causal links between ADHD genetic liability and biological markers of human metabolism and inflammation. While MR has advantages over traditional observational designs in addressing causality it is not without limitations and triangulation with different methods is needed to increase confidence in causal association.Lucy Riglin: Data curation, Investigation, Writing – original draft. Evie Stergiakouli: Investigation, Writing – original draft.The authors have declared that they have no competing or potential conflicts of interest.No ethics approval was required for this article as no new data were created or analyzed in this study.Supplementary MaterialClick here for additional data file.


Prevailing frameworks propose that a key feature of attention-deficit/hyperactivity disorder (ADHD) is lower motivation. An important component of motivation is the willingness to engage in cognitively or physically effortful behavior. However, the degree to which effort sensitivity is impaired in ADHD has rarely been tested, and the efficacy of stimulant medication in ameliorating any such impairments is unclear. Here, we tested 20 individuals with ADHD  who were managed with amphetamine-based medication , and 24 controls . Individuals with ADHD were tested over two counterbalanced sessions, ON and OFF their usual amphetamine-based medication. In each session, participants performed an effort-based decision-making task, in which they were required to choose how much cognitive or physical effort they were willing to engage in return for reward. Our results revealed three main findings. First, individuals with ADHD had lower motivation relative to controls to invest effort in both the cognitive and physical domains. Second, amphetamine increased motivation uniformly across both domains. Finally, the net effect of amphetamine treatment was to mostly restore motivation across both domains of effort relative to healthy controls. These data provide clear evidence for a heightened sensitivity to both cognitive and physical effort in ADHD, and reveal the efficacy of amphetamine-based drugs in restoring effort sensitivity to levels similar to controls. These findings confirm the existence of reduced motivational drive in ADHD, and more broadly provide direct causal evidence for a domain-general role of catecholamines in motivating effortful behavior.SIGNIFICANCE STATEMENT A core feature of attention-deficit/hyperactivity disorder (ADHD) is thought to be a heightened aversion to effort. Surprisingly, however, the degree to which effort sensitivity is impaired in ADHD has rarely been tested. More broadly, the relative efficacy of catecholamines in motivating the investment of cognitive and physical effort is unclear. We tested 20 individuals with ADHD ON and OFF amphetamines, and compared their behavior on an effort-based decision-making task to 24 controls. When tested OFF medication, the ADHD group was less cognitively and physically motivated than controls. However, amphetamines led to a comparable increase in motivation across both domains. This demonstrates the efficacy of catecholamines in facilitating domain-general effort, and highlights the broader potential of such drugs to treat disorders of motivation. Attention-deficit/hyperactivity disorder (ADHD) is a disorder of catecholamine dysregulation that affects multiple aspects of cognition . ContempA common approach to studying motivation in ADHD is with subjective self-reports, which are limited in their capacity to provide a mechanistic account . ExperimThe idea that impaired effort allocation is a key feature of ADHD was first advanced nearly 20 years ago . In thatIn the broader neuroscience literature, dopamine and noradrenaline are known to play causal roles in motivated behavior. Lower levels of these catecholamines are associated with lower motivation to invest effort , and exoIn this study, we tested whether individuals with and without ADHD differ in their motivation to exert cognitive and physical effort and, if so, the degree to which amphetamines are capable of ameliorating such differences. We recruited individuals with ADHD being managed with amphetamine-based drugs , and tested them over two counterbalanced sessions, once on their usual treatment, and the other following a 72-h washout. Our paradigm involved training participants on cognitively and physically effortful tasks, before asking them to decide how much effort they were willing to invest in each domain for particular rewards. This allowed us to compare cognitive and physical effort-based decisions across the ADHD and non-ADHD groups, and to determine the efficacy of amphetamines in ameliorating any such differences.We recruited 20 individuals with ADHD  and 24 individuals without ADHD . ADHD participants were recruited through practicing clinicians, local community support groups, and social media. Our inclusion criteria for ADHD participants included: (1) a diagnosis of ADHD confirmed and managed by a psychiatrist; (2) a score >65 on any of Section E, F, or G on the Conners’ Adult ADHD Rating Scale CAARS; ; (3) a sOf the 20 participants with ADHD, eight were taking dexamfetamine (mean dose 35.6 mg ± SD 14.3 mg), and 12 lisdexamfetamine (55.4 ± 19.7 mg). To determine the effect of stimulant medication on behavior, individuals with ADHD were tested in two counterbalanced sessions. In one session, they were tested ON their usual medication . In a separate session, they were tested after at least 72 h OFF medication to allow for washout . Drug orIn addition to the CAARS and WURS, we assessed trait measures of apathy and depression with the Dimensional Apathy Scale , and HosA,B) or physically effortful . In each task, we varied demands in the target domain , while keeping those in the other  constant. Task order was counterbalanced. The Reinforcement phases were followed by a “Choice” phase, during which participants were asked to choose between a fixed low-effort/low-reward option, and a variable high-effort/high-reward offer . This allowed us to quantify the willingness of individuals to exert distinct types of effort.This task utilized a paradigm that has been previously applied in healthy adults  and clinFig. 1A,B) and press a button whenever they detected the target letter, T. We parametrically manipulated cognitive demand by increasing the number of letter streams from one to six. In the least effortful condition (level 1), a single stream was presented centrally. In the more effortful conditions (levels 2–6), between two to six streams were positioned equidistantly from fixation. The target letter could appear randomly in any stream, and the timing of the target stimuli was pseudorandom such that they could not appear in consecutive stimulus frames . Each effort level comprised 24 stimulus frames, each of which lasted 416 ms, for a total trial duration of 10 s.The cognitive effort task was based on a rapid serial visual presentation (RSVP) paradigm A,B and pEach trial of the Reinforcement phase commenced with a blue pie chart, which cued the level of cognitive effort required on that trial. After completing the required level of effort, participants received feedback about their success. They were rewarded with one point if they were able to complete each trial above a threshold level of performance ; otherwise, they were not rewarded. Participants were instructed that their task was to maximize the number of points won. They completed two blocks of 30 trials , with a brief rest period after each block. These experimental blocks were preceded by a practice block of 12 trials (two per effort level). Responses were registered on a Cedrus button box, and the task was implemented on Presentation software .C,D; In the physical effort task, participants exerted one of six levels of force on a hand-held dynamometer  using their dominant hand  in MATLAB (The MathWorks).Each trial in the physical effort task commenced with a red pie chart, which cued the level of physical effort required on that trial. Participants then had to initiate and maintain their contraction above the required effort level for ≥50% of the total trial duration to be successfully rewarded. The physical effort task was identical to the cognitive effort task with respect to trial durations (10 s per effort level); number of trials per effort level; and overall block structure. The physical effort task was implemented on Psychtoolbox . In contrast, the variable offer was the option to exert a higher amount of effort (levels 2–6) for a greater reward (2–10 points). In order to separate individuals’ cognitive and physical motivation, each choice was always made between two options in the same domain. We sampled the entire effort-reward space evenly and randomly across both domains over a total of 150 trials. Participants made their selection with a button press, and trials were self-paced. To reduce the impact of fatigue on subsequent decision-making, participants were not required to execute their choices, but simply indicate their preferred option. They were explicitly told that their decisions were hypothetical, in that points did not alter remuneration, but that they should select the option that was most preferable to them. This protocol is consistent with previous studies .post hoc pairwise comparisons were corrected for multiple comparisons with the Bonferroni approach.In the reinforcement phase, we compared differences in performance between groups (ADHD vs controls) or drug (ADHD ON vs OFF) with separate repeated-measures ANOVAs. Specifically, when comparing group differences, we analyzed performance with a Group  × Effort (levels 1–6) ANOVA. We compared the effect of Drug with an analogous Drug (ON vs OFF) × Effort ANOVA. Violations to the assumption of sphericity were managed with Greenhouse–Geisser corrections, and fitglme function in MATLAB with a logistic link function and Laplace fit method.In the choice phase, our core approach to analyze the choice data were with a generalized linear mixed effects model, which allowed us to assess all variables of interest on every trial. We used a logistic transform to analyze Choice  as the outcome variable. Across all analyses, we entered Domain , Effort (levels 2–6), and Reward (levels 2–6) as fixed effects. In separate analyses, we included either Group  or Drug (ON vs OFF) as an additional fixed effect to compare the effect of each on choice. Predictor variables were Z-scored, and we modeled the full range of fixed effects and interactions. Individual subjects were fit with random intercepts. Data were analyzed using the First, we considered the capacity of the control and ADHD groups (both ON and OFF medication) to perform each level of the cognitive and physical effort tasks .d’ [Z(Hits) – Z]. To determine the effect of Drug on d’ in the ADHD group, we performed a within-subjects repeated-measures ANOVA on the factors of Drug  and Effort (levels 1–6). As expected, there was a significant main effect of Effort, indicating an expected decrement in performance with increase in task load  = 15.2, p < 0.001). Importantly, however, neither the main effect of Drug, nor the Drug × Effort interaction, were significant  = 0.48, p = 0.50; Drug × Effort, F = 0.41, p = 0.84).We quantified cognitive performance as target detection sensitivity, F = 20.7, p < 0.001; Group, F = 1.43, p = 0.23; Group × Effort, F = 0.77, p = 0.57; Controls vs ADHD OFF: Effort, F = 24.0, p < 0.001; Group, F = 2.66, p = 0.11; Group × Effort, F = 1.80, p = 0.11).We then compared the performance of controls against the ADHD groups ON and OFF medication using separate two-way ANOVAs with the between-subjects factor of Group , and within-subjects factor of Effort. Both ANOVAs revealed the same pattern of results – there was a significant main effect of Effort, which reiterated the decrement in performance with increasing load. However, there were no significant Group differences  = 1.73, p = 0.20; Effort, F = 0.45, p = 0.81; Drug × Effort, F = 0.83, p = 0.53; Controls vs ADHD ON: Group, F = 0.72, p = . 40; Effort, F = 2.10, p = 0.067; Group × Effort, F = 0.28, p = 0.93; Controls vs ADHD OFF: Group, F = 0.95, p = 0.34; Effort, F = 0.34, p = 0.89; Group × Effort, F = 1.31, p = 0.26). Thus, participants in all groups were reinforced equally well across all effort levels.A complementary measure of performance is the capacity of individuals to perform each level to the threshold required for reward. For each level, we defined reinforcement rate as the proportion of trials for that level in which participants were able to successfully reach or exceed the reward threshold. To determine the effect of Drug (in ADHD participants) or Group (Control vs ADHD) on reinforcement rates, we performed the analogous ANOVAs as above. None of these analyses revealed any significant main effects or interactions  between the ADHD participants ON and OFF medication, or between the ADHD group and controls. This implies that any subsequent differences in the willingness of one group to exert higher levels of cognitive effort than another cannot be attributed to a differential capacity in task performance.In sum, these analyses revealed no significant differences in performance  = 7.25, p = 0.014). The main effect of Effort was also significant, indicating that performance declined with increasing effort  = 24.4, p < 0.001). The two-way interaction was not significant  = 0.65, p = 0.57).We performed the analogous set of analyses on the physical effort task. In this task, we operationalized performance as the proportion of the 10 s window in which individuals were able to sustain their force at or above the target effort level. When comparing the performance of ADHD participants ON and OFF medication, the main effect of Drug was significant, such that the ADHD group were able to sustain their force for longer when ON versus OFF medication  = 45.6, p < 0.001; Group, F = 1.30, p = 0.26; Group × Effort, F = 0.71, p = 0.46; Controls vs ADHD OFF: Effort, F = 49.8, p < 0.001; Group, F = 0.52, p = 0.48; Effort × Group, F = 0.55, p = 0.56).When comparing Controls against the ADHD group, the pattern of results was the same regardless of whether the ADHD participants were ON or OFF medication. In both cases, there was a significant main effect of Effort, but there were no statistically significant differences in task performance between the groups  = 5.49, p = 0.015), but neither the main effect of Drug nor the two-way interaction was significant  = 1.77, p = 0.20; Drug × Effort, F = 1.40, p = 0.26). This same pattern was reiterated in the ANOVAs comparing controls to ADHD ON and OFF medication  = 9.01, p = 0.001; Group, F = 0.48, p = 0.49; Effort × Group, F = 0.67, p = 0.47; Controls vs ADHD OFF: Effort, F = 11.8, p < 0.001; Group, F = 0.35, p = 0.56; Effort × Group, F = 0.30, p = 0.71).When examining reinforcement rates in the ADHD group ON versus OFF medication, there was a significant main effect of Effort  as the outcome variable, and entered Domain , Effort (levels 2–6), and Reward (2–10 credits) as fixed effects. Individual subjects were fit with random intercepts. Predictor variables were Z-scored, and we modeled the full range of fixed effects and interactions. Data were analyzed using the t = −4.97, p < 0.001) and Effort × Reward  interactions, and a borderline Domain × Effort × Reward interaction . The Domain × Effort interaction revealed that individuals were more likely to engage in cognitive versus physical effort at all but the lowest level of Effort (levels 3–6). The Effort × Reward interaction revealed that individuals were less inclined to exert higher levels of effort , but that this effect was greatest at the lowest levels of reward, and minimal at the highest levels of reward . The borderline three-way interaction suggested that this effect was more pronounced in the physical domain, such that cognitive effort discounting was present across all rewards, but physical effort discounting only at lower rewards .The analysis of control data revealed a pattern of results similar to that described in previous studies . All maiNext, we examined the effect of stimulant medication on improving motivation in the ADHD group. We analyzed the ADHD data using a similar hierarchical generalized linear mixed effects model on the fixed effects of Domain, Effort and Reward, but now with the inclusion of Drug  as an additional fixed effect. We again modeled the full range of fixed effects and interactions.t = 11.5, p < 0.001; Domain, β = −0.80, t = −12.4, p < 0.001; Effort, β = −1.23, t = −19.9, p < 0.001; Reward, β = 1.69, t = 26.5, p < 0.001). Importantly, the effect of Drug was not qualified by any higher order interactions involving Domain . Rather, the effect of Drug was only involved in significant two-way and three-way interactions with Effort and Reward . This pattern of results indicates that Drug had a significant effect on choices, and in a similar manner across both the cognitive and physical domains.All simple effects were significant . The only other significant result was independent of Drug. Specifically, the Domain × Effort × Reward interaction was significant , and simply reiterated the corresponding interaction in Controls, such that effort discounting was present at all reward levels in the cognitive domain, but only at the lower levels of reward in the physical domain. No other interactions were significant . Together, these results indicate that Drug had a significant effect on both Effort and Reward, but that these effects of Drug did not differentially affect one domain over another.The three-way Drug × Effort × Reward interaction indicated that the ADHD group were generally more motivated to exert effort when ON versus OFF at the lower effort levels (levels 2–4). However, at the higher effort levels (levels 5–6), the effects of Drug were more pronounced at intermediate levels of reward . We then included Performance as an additional fixed effect to the reference model above. Importantly, however, this more complex model with Performance as an additional fixed effect led to a significantly poorer overall model fit . Furthermore, this more complex model revealed the same pattern of significant results as in the reference analysis. Specifically, the effect of Drug was not qualified by any higher order interactions involving Domain . Again, Drug was only involved in significant two-way and three-way interactions with Effort and Reward .Recall that, in the training phase of this study, the ADHD group were less able to sustain their physical force when OFF versus ON medication. To ensure that the lower physical motivation in the ADHD group OFF versus ON medication was indeed because of the aversiveness of effort, rather than a decrement in the capacity to sustain a more effortful contraction, we performed a more complex GLM, with the inclusion of “Performance” of the more effortful option as an additional fixed effect. To do so, we first normalized performance for the cognitive and physical tasks separately . As in the reference analysis, the only significant interactions involving Drug were two-way and three-way interactions with Effort and Reward .For completion, we performed a complementary analysis on Reinforcement Rates to ensure that effort aversion was not driven by the capacity to perform each level to the required reward threshold. We included the normalized Reinforcement Rates of the more effortful option as an additional fixed effect to the reference model. Again, however, this more complex model revealed a poorer overall fit , and the pattern of the results remained unchanged. Again, the effect of Drug was not qualified by any higher order interactions involving Domain , and the inclusion of these additional terms did not alter the pattern of the remaining effects and interactions. Specifically, the effect of Drug was not qualified by any higher order interactions involving Domain , and the only significant interactions with Drug were two-way and three-way interactions with Effort and Reward .Finally, to test for potential session order effects (ON-OFF vs OFF-ON), we modified the reference model by including fixed effects of Order, and an Order × Drug interaction term. Importantly, this model did not substantially improve the model fit relative to the reference model . Furthermore, the Order × Drug interaction term was not significant . Notably, Group did have a differential effect on motivation across the two domains. A significant Group × Domain × Effort interaction  indicated that controls were more motivated than ADHD OFF medication at the higher levels of Cognitive effort (levels 3–6), and lower levels of Physical effort .We then considered the behavior of the ADHD group OFF medication relative to healthy controls . We perft = −5.31, p < 0.001; Effort × Reward, β = 0.22, t = 3.97, p = 0.0001; Domain × Effort × Reward, β = 0.13, t = 2.41, p = 0.016; all other interactions, p > 0.15).The only other significant results were independent of Group, and involved a series of two-way and three-way interactions including Domain, Effort and Reward, which reiterated the same pattern of cognitive and physical effort discounting as described in the preceding analyses . Importantly, however, the pattern of significant main effects and interactions was the same across both GLMs and the reference model. Thus, the results above were not modulated by the capacity of individuals to be able to perform the higher levels of cognitive or physical effort.t = −0.01, p = 0.99; Domain, β = −0.90, t = −12.8, p < 0.001; Effort, β = −1.38, t = −20.0, p < . 001; Reward, β = 1.82, t = 25.8, p < 0.001). Notably, these effects were involved in higher-order two-way and three-way interactions that indicated a significant differential effect of Group on choices in the cognitive and physical Domains . These interactions showed that controls and ADHD ON medication did not differ in physical motivation; rather, any group differences were in cognitive motivation. Specifically, the Group × Domain × Effort interaction was driven by controls being more motivated than the ADHD group ON medication at higher levels of cognitive effort (levels 4–6). The Group × Domain × Reward interaction indicated higher cognitive motivation in controls than ADHD ON at the lowest level of reward (two credits).Finally, we considered the efficacy of drug treatment by comparing the ADHD group ON medication to the control group. We implemented the same model as reported for ADHD OFF versus controls, but this time using data from the ADHD ON group. Again, the main effects of Domain, Effort and Reward were significant  and Group × Effort × Reward  interactions. These indicated that controls were more motivated than the ADHD group only at the highest levels of effort at the intermediate level of Reward (6 credits). Finally, there were significant Domain × Effort  and Domain × Effort × Reward  interactions, which simply reiterated the domain-specific patterns of effort discounting described in the preceding analyses.The other significant interactions involving Group were independent of Domain. There were significant Group × Effort . These control models also revealed the same pattern of results as the reference model.p > 0.10; physical task, |ρ| < 0.14, p > 0.55; both tasks, |ρ| < 0.27, p > 0.25). We also tested for a correlation between acceptance rates, and each subscale of the CAARS and WURS. Again, however, none of the correlations were significant .As supplementary analyses, we probed for potential relationships between the willingness to engage in effortful behavior, and measures of trait apathy. Specifically, we used a Spearman’s ρ to correlate acceptance rates in the ADHD group (averaged across both drug sessions), against scores on each subscale of the DAS. We analyzed this relationship for acceptance rates in the cognitive and physical tasks separately, as well as when averaged across both tasks. However, none of these correlations were significant (cognitive task, all |ρ| < 0.38, all In this study, we asked whether individuals with ADHD demonstrated altered effort sensitivity relative to controls, and, if so, how these changes can be ameliorated by amphetamines. Our data revealed three main findings. First, ADHD participants OFF medication were less motivated than controls to invest effort in both the cognitive and physical domains. Second, amphetamines increased motivation to a similar degree in both domains. Finally, the net effect of amphetamine treatment was to restore motivation to levels very similar to controls, with only minor group differences persisting at the highest level of cognitive effort. Overall, our data provide clear evidence that effort sensitivity is impaired in ADHD, and indicate the efficacy of amphetamine-based medications in treating this impairment.An aversion to cognitive effort is thought to play a key role in the cognitive inefficiencies in ADHD . Indeed,Our data also showed that individuals with ADHD had a clear aversion to physical effort when tested OFF medication relative to controls. At first glance, this may appear to contrast with the only other studies that have examined physical effort in ADHD, none of which have shown strong support for effort aversion . HoweverPrevailing frameworks propose that a motivational deficit is one of the key characteristics of ADHD . Some haIn our study, we operationalized motivation as a reduced willingness to engage in cognitively or physically effortful behavior . NotablyOur data also showed that amphetamines had a domain-general effect on improving motivation. The only other study to have examined the effect of stimulants on effort-based decisions showed that the motivation to invest physical effort was higher in ADHD participants on methylphenidate versus placebo . ImportaOverall, our data provide causal support for the importance of catecholamines in overcoming both cognitive and physical effort costs in pursuit of a goal. Dopamine and noradrenaline both play critical roles in facilitating effortful behavior. For example, a large body of work indicates that dopamine incentivizes and invigorates approach behavior for reward . TraditiAlthough much of the focus in ADHD has been on dopamine and noradrenaline dysfunction, it is important to note that serotonin is another monoamine neurotransmitter that is important in goal-directed behavior. In our study, ADHD participants were managed with either dexamfetamine, or its prodrug, lisdexamfetamine . The priIn summary, we provide evidence for effort aversion as an important mechanism for the reduced motivation in ADHD individuals, and show that treatment with amphetamine-based drugs is capable of restoring motivation to levels comparable to those in healthy individuals. Our data indicate that the heightened effort aversion is experienced across multiple domains of effort, and is independent of the capacity of the individual to actually perform the task at hand. This provides further confirmatory evidence that the motivational deficits in ADHD are not secondary to dysfunction of particular cognitive domains; rather, it is likely to represent a primary dysfunction in these individuals. More broadly, our data add to accumulating evidence for the causal role of catecholamines in increasing motivated behavior, and provide further evidence for the in-principle efficacy of such drugs to improve apathy in appropriate clinical populations.


Winter wheat is used as forage at the tillering stage in many countries; however, the regrowth pattern of wheat after mowing remains unclear. In this study, the growth patterns of wheat were revealed through cytological and physiological assessments as well as transcriptome sequencing. The results of agronomic traits and paraffin sections showed that the shoot growth rate increased, but root growth was inhibited after mowing. The submicroscopic structure revealed a decrease in heterochromatin in the tillering node cell and a change in mitochondrial shape in the tillering node and secondary root. Analysis of the transcriptome showed the number of differentially expressed genes (DEGs) involved in biological processes, cellular components, and molecular functions; 2492 upregulated DEGs and 1534 downregulated DEGs were identified. The results of the experimental study showed that mowing induced expression of DEGs in the phenylpropanoid biosynthesis pathway and increased the activity of PAL and 4CL. The upregulated DEGs in the starch and sucrose metabolism pathways and related enzyme activity alterations indicated that the sugar degradation rate increased. The DEGs in the nitrogen metabolism pathway biosynthesis of the amino acids, phenylpropanoid biosynthesis metabolism, and in the TCA pathway also changed after mowing. Hormone content and related gene expression was also altered in the tillering and secondary roots after mowing. When jasmonic acid and ethylene were used to treat the wheat after mowing, the regeneration rate increased, whereas abscisic acid inhibited regrowth. This study revealed the wheat growth patterns after mowing, which could lead to a better understanding of the development of dual-purpose wheat. Dual-purpose wheat refers to wheat being mowed at the tillering stage to use the shoot biomass as livestock forage, with the grain harvested in summer. Ideally, dual-purpose wheat provides greater forage for livestock in winter without decreasing grain yield in summer. This wheat management system is found across different regions and countries, such as Australia, the Americas, and Europe ,2,3, butSolanum tuberosum), tomato (Solanum Lycopersicum), and tobacco (Nicotiana Tabacum), abscisic acid (ABA) can induce the expression of the injury response gene PINII [LEC1, FUS3, and WUSCH1, increasing the number of cotyledon embryos [LOX2 is relieved, and the JA content increases in response to the injury [ACS2,6,7,8 responded rapidly to injury in the leaves of Arabidopsis thaliana [In plants, growth patterns and organ function are continually adjusted after damage, such as herbivory, and this regrowth mechanism is vital for plant survival ,11. The ne PINII , which i embryos . Recent  embryos ,22. Whene injury . JA stime injury . Ethylene injury . It has thaliana ,27.A previous study has proved that the transcriptome is analyzed after mowing to reveal the regrowth mechanism of the plants. There are several changes in the expression of many genes in grasses, with the majority of these changes concentrated on cellular components, molecular functions, and biological processes . One of The application of dual-purpose wheat not only relieves the forage shortage in winter but also increases the effectiveness of land use and provides environmental protection. Plant regrowth is the key factor for dual-purpose wheat used as forage in the tillering stage . Dual-puDuring winter, wheat root growth was found to accelerate, whereas the leaves almost stopped growing. However, mowing the shoots resulted in a different developmental pattern and caused the root diameter of some secondary roots to be smaller than that of the control group (CK) A,B; afteThe length of the longest root and the number of root primordia and secondary roots in CK and T were also measured. These results also showed that mowing restrained root growth . Within Paraffin sections were analyzed to ascertain the wheat leaf growth patterns after mowing. As shown in The tillering node (TN) structure remained unchanged even when the pace of fresh leaf development increased. As shown in Root paraffin sections were analyzed to determine the root development patterns after mowing. The results showed that the structure of the primary root did not change after mowing, although the primary root length in T was shorter than that in CK . HoweverTo analyze internal alterations in the cells, the subcellular structures of the TNC and secondary roots were observed. The results showed that the chromatin filaments in TN cells were packed and folded tightly and presented as agglutinated heterochromatin at 0 d A. After The sub-microstructure of root cells was similarly affected by mowing. As shown in The transcriptome of TN was examined within 3 d of mowing for the results of the sub-microstructure and agronomic traits changed at the 3 d after mowing. Additionally, TN samples at 2 h after mowing were selected to analyze the effects of mowing on wheat at an early stage. After filtering the low-quality data, approximately 18.6 Gb of sequence data for each sample were obtained. A total of 4476 DEGs  were identified between CK and T (T1-T3), including 2942 upregulated and 1534 downregulated genes. As shown in PAL1 in DEGs in the PAL metabolism pathway was measured, and the results showed that it was significantly upregulated in TN and SR after mowing  and β-glucosidase (bglX/B) metabolism pathways and 50 genes were upregulated at 24 and 72 h in other metabolic pathways . The genr mowing .NR1 and GS1 in the nitrogen metabolism pathway were determined through qRT-PCR. The results showed that the expression patterns of NR1 and GS1 in TN were consistent with those observed in the transcriptome. These two genes were upregulated after mowing , 9 in aminotransferase (ilvE), 6 in aroF, 6 in malic dehydrogenase (MDH), and 7 in the L-diaminopimelate aminotransferase metabolism pathway (EC:2.6.1.83) . The uprDifferent amino acid metabolic pathways were analyzed to better understand amino acid metabolism after mowing. The results showed that one gene in the tyrosine aminotransferase (TAT) metabolism pathway was upregulated at 2 and 24 h after mowing, suggesting that the rate of tyrosine and phenylpyruvate metabolism increased . As showMDH1 and CS1 in the TCA metabolism pathway reflected the enzymatic activity   A. Howeve2O) A. Althou2O) B. The SP2O) B. The st2O) C,D.After treatment with various hormones, the expression patterns of genes associated with IAA, CTK, and GA synthesis and transport were altered. Nearly all genes showed varying degrees of upregulation or downregulation in response to ETH, MeJA, or AA treatments .Dual-purpose wheat is grown in many countries, but its growth pattern after mowing remains unclear ,39. WithVariations in shoot and root biomass reflect the carbon distribution patterns in plants after mowing. In this study, agronomic trait analysis showed that mowing stopped root growth while accelerating shoot growth, indicating that nutrition in wheat was primarily redistributed for shoot regeneration in the early period after mowing. This finding indicates that shoot regrowth after mowing is promoted at the expense of underground growth . HoweverPlant leaf development involves the production and differentiation of leaf primordia, which eventually develop into mature leaves . In the PAL metabolism pattern to upregulate and catalyze phenylalanine to produce cinnamic acid, which is then converted into lignin and phenol catalytic action with 4CL [PAL gene was upregulated under cold conditions [PAL upregulation might also be related to low temperatures. In the sugar metabolism pathway, the genes were upregulated, indicating that the rate of sugar degradation increased in TN after mowing. The β-Bmybase and β-glucosidase activities and related genes expression pattern in the TN and SR also reflected the utilization rate of starch and reducing sugar increased. After mowing, wheat let regeneration takes precedence over root growth. It was previously reported that β-glucosidase-mediated hydrolysis of glucose increases after plant tissue wounding, and this process is important in cell wall loosening and elongation [Mowing was found to induce the genes in with 4CL ,51. In twith 4CL . PAL actwith 4CL . In the nditions . In the ongation . In thisongation .The DEGs in the nitrogen metabolism pathway were upregulated after mowing, suggesting that additional nitrogen elements were required for wheat regrowth. It has been reported that nitrogen metabolism is accelerated under abiotic stress . Two maiAmino acid metabolism is essential for plant regrowth. In this study, the genes of amino acid biosynthesis were upregulated in TN during the regrowth process, such as ilvE, IVD, and fadA, suggesting that the rate of branched-chain amino acid degradation accelerated, and the precursor substances for glucosinolate biosynthesis increased . MethionARF, auxin synthesis genes YUCC and GH3.1, were upregulated during leaf regeneration, indicating that the synthesis rate of auxin increased. The expression of CTK metabolism-related genes, CISZOG and IPT, and cytokinin content increased, indicating that the CTK synthesis rate might be higher than the CTK consumed. The expression of the GA20ox2 and GA30ox2 genes was upregulated for the sequential oxidation of GA into biologically active gibberellin, which is important for the elongation and growth of cells [GAMYB gene expression was also upregulated, which also reflected an increase in the gibberellin synthesis rate after mowing. These three hormones were also compounded in the root, while their content decreased, which might account for the hormones being transported into the TN for shoot repair.Wheat regrowth after mowing involves both leaf repair and active growth. In this process, endogenous contents of IAA, CTKs, and GA changed in the TN and roots of the wheat, similar to previous studies ,68. Duriof cells . In the ERF gene expression and ETH was rapidly produced, serving as a proxy for wound signal-induced auxin biosynthesis [ASC2, was rapidly upregulated within 30 min after leaf wounding in Arabidopsis [ERF3, ERF8, and ACS2 was upregulated in TN, which is consistent with the results of previous studies. JA has been shown to be involved in plant regeneration, and could be a long-distance injury signal that transmits the wound signal from the wound site to the meristem, ultimately promoting IAA synthesis [ERF115 expression and stimulates apical regeneration [OPR and LOX [After plant injury, ETH, JA, and ABA contents rapidly increase and play essential roles in plant defense and regrowth ,21,25. Iynthesis . In a prbidopsis . In the neration . Many ge and LOX . Our res and LOX . A previ and LOX .Arabidopsis [In this study, we used JA, ETH, and ABA to treat wheat after mowing and found that ETH and JA stimulated wheat regeneration. Previous research has shown that JA-pretreated hypocotyl explants potentiate de novo shoot regeneration in bidopsis  and ETH-bidopsis . The treThe wheat line “XiNong 136” was chosen for this study because of its large shoot biomass in winter and strong regeneration capacity after mowing . The wheThe agronomic traits of the shoots and roots were investigated to reveal the growth patterns of wheat after mowing. In this study, the agronomic traits of 15 plants subjected to  were compared with the traits of 15 plants under normal growth conditions  at each time point. The entire root was removed from the pot and washed with running water until the root was clean. The leaf length of regeneration was measured with a Vernier caliper, and the surface area and root number were measured using a root scanner . The root length was determined using ImageJ software (version:1.8.0) . The wheTo analyze the internal growth pattern of the shoots and roots, ten primary root tips (PRT) and ten secondary root tips (SRT), as well as five main stems of wheat were collected at 0, 3, 6, 12, 18 and 24 d after mowing. The sample lengths of the root tips and main stems were set to 0.5 cm. The TN linked to the main stem was collected and a paraffin section of the TN was prepared. The process for paraffin section preparation was as follows. All samples were fixed in 70% FAA stationary liquid for more than 8 h. After immobilization, the samples were rinsed three times with water and stained with hematoxylin for three days. Afterwards, the samples were dehydrated with alcohol, made transparent with xylene, dipped in paraffin, and sliced using a slicer . To obtain the cross-cut samples of TN, the wheat leaves were stripped and counted, leaving only SAM and TN for the paraffin sectioning. When cross-cutting the TN paraffin section, the cuts began at the SAM and continued until the SAM section disappeared. The following TN paraffin sections were used to analyze changes in TN. The section thickness was set to 15 μm. The cells were photographed using a stereomicroscope .For submicroscopic structural observations, five SRTs and five TNs (linked to the main stem) were collected 0, 3, 6 and 12, d after mowing. The site near the SAM was chosen for the TN samples to minimize submicroscopic structural errors, and the site 1 cm from the root tip was chosen as the SRT sample. The samples were fixed with 2.5% glutaraldehyde solution (PH = 7.3) for more than 5 h and then washed four times with 0.1 M phosphate buffer. Samples were then fixed with 1% osmic acid  for 3 h, washed with phosphate buffer for 15 min, and the process was repeated four times. Subsequently, the samples were dehydrated twice using a gradient of ethanol for 10 min and repeated once more. Dehydration was followed by permeation with a resin before embedding the samples. The samples were then solidified in a drying oven. The temperature of the drying oven was set to 50 °C, and the solidification time was more than 48 h. After solidification, samples were sliced using an ultramicrotome  and stained with 2% uranium dioxide acetate for 15 min and lead citrate for 5 min. A transmission electron microscope  was used to examine the samples. This experiment was performed at the large-scale instrument sharing platform for life sciences at Northwest A&F University .TNs were collected at 0 (T0), 2 (T1), 24 (T2), and 72 h (T3) after mowing, with 3 biological replicates for each treatment; each replicate contained 15 wheat and a total of 12 RNA sequencing samples. Total RNA was extracted and purified using the TRNzol Universal kit . A NanoDrop ND-1000  and Bioanalyzer 2100  were used to analyze the quantity, purity, and integrity of the RNA. To build the library, only RNA samples that met certain criteria  were used. mRNA and non-coding RNAs (ncRNAs) were retained after rRNAs was removed using the Ribo-Zero rRNA Removal Kit . Short fragments of the enriched mRNAs and ncRNAs were created using fragmentation buffer and then reverse-transcribed into cDNA using random primers. cDNAs was used to create libraries. The protocols for library construction and sequencing followed the standard procedures provided with the NEBNextUltra RNA Library Prep Kit (Illumina). Sequencing was performed using an Illumina HiSeq 4000 at Gene De novo Biotechnology Company .https://cutadapt.readthedocs.io/en/stable/) (accessed on 25 February 2023) [http://urgi.versailles.inra.fr/download/iwgsc/IWGSC_Ref-Seq_Assemblies/v1.0/) (accessed on 28 February 2023) using HISAT2 software  (accessed on 12 March 2023) and assembled with default parameters using StringTie (http://ccb.jhu.edu/software/stringtie/) (accessed on 15 March 2023) [https://ngdc.cncb.ac.cn/?lang=en) (accessed on 23 April 2023) and BioProject ID is PRJCA018312.Reads with adaptor contamination were removed using Cutadapt (ry 2023) . Screenech 2023) . Gene Onhttp://www.bioconductor.org/packages/releases/bioc/html/DESeq2.html) (accessed on 24 March 2023), DEGs were chosen with |log2 (fold change) | ≥ 1 and a p-value < 0.05 [https://www.omicshare.com) (accessed on 25 April 2023). The high enrichment of KEGG allowed the identification of the main biochemical, metabolic, and signal transduction pathways implicated in the DEGs.Gene expression levels were measured according to the number of kilobytes per million mapped reads (RPKM) in RNA-Seq analyses . The DESe < 0.05 . The DEG−∆∆Ct method [Real-time quantitative PCR (qPCR) was used to detect gene expression patterns. TNs and SRTs were collected at 0, 2, 24, and 72 h after mowing, according to physiological and biochemical determinations. The samples of TNs were collected at 2, 24, and 72 h after hormone treatment, five TNs were collected as one sample at each time point and this was repeated three times. RNA extraction and qRT-PCR were performed as previously described . The whet method . Real-tiPhysiological changes in TN and SRT were analyzed in this study. Samples were collected at 0, 2, 24, and 72 h after mowing, and three wheat samples were collected, mixed as one sample, and repeated three times at each time point. The samples were washed with water until clean and then stored in liquid nitrogen. In this study, PAL and 4CL activities, amylase and glucosidase activities, nitrate reductase (NR) and glutamine synthetase (GS) activities, malate dehydrogenase (MDH) and citrate synthase (CS) activities, and starch and reducing sugar content were detected according to Li with some modifications . Each phEndogenous hormones are essential for plant growth. Hormone content was determined using HPLC-liquid chromatography  at the College of Horticulture of Northwest A&F University . Approximately 0.5 g wheat tissue (TN and SRT) was collected and detected according to Almeida-Trapp with some modification . In thisDual-purpose wheat has been used in many countries, but the wheat regeneration mechanism is still unclear. In this experiment, we revealed the regeneration pattern through the analysis of cell, transcriptome, and related genes expression patterns. The results indicate that mowing causes the wheat growth pattern changed with nutrients and energy being preferentially supplied to shoots for regeneration. Hormone content and related gene expression profiles were also altered during the wheat regeneration process.