SFTPB, LAMP3, SFTPA1, SFTPA2) and lung tumorigenesis . Notably, the CRISPRi approach reveals that a portion of NKX2-1-binding sites are functionally indispensable while the rest are dispensable for the expression of the genes, indicating that functional roles of NKX2-1-binding sites are unequally yoked.The transcription factor NKX2-1/TTF-1 is involved in lung pathophysiology, including breathing, innate defense and tumorigenesis. To understand the mechanism by which NKX2-1 regulates genes involved in such pathophysiology, we have previously performed ChIP-seq and identified genome-wide NKX2-1-binding sites, which revealed that NKX2-1 binds to not only proximal promoter regions but also multiple intra- and inter-genic regions of the genes regulated by NKX2-1. However, the roles of such regions, especially non-proximal ones, bound by NKX2-1 have not yet been determined. Here, using CRISPRi (CRISPR/dCas9-KRAB), we scrutinize the functional roles of 19 regions/sites bound by NKX2-1, which are located in genes involved in breathing and innate defense ( The transcription factor NKX2-1 is a key regulator of lung pathophysiology, but the importance of its binding sites outside of proximal promoter regions is unclear. Here, William Stuart and Iris Fink-Baldauf et al. use CRISPRi to interrogate 19 NKX2-1 binding sites and identify specific sites important for breathing, innate immune defense, and tumorigenesis. Nkx2-1  deleted mice had impaired lung development, which results in death at birth due to a breathing defect1, the molecular mechanisms by which NKX2-1 regulates genes involved in lung development and physiology have been extensively pursued2. One of the genes critical for breathing at birth is SFTPB , since Sftpb-deleted mice also succumb to death at birth because of a breathing defect due to collapsed lungs3. Using gel-shift  and plasmid-based reporter assays, NKX2-1 has been shown to bind to the proximal promoter region of human SFTPB and stimulate its promoter activity5. A microarray analysis to determine the genome-wide NKX2-1 downstream target genes in human lung epithelial cells also identified SFTPB as well as LAMP3 , both of which co-localize at lamellar bodies (a cellular organelle) that process and secrete surfactant to reduce lung surface tension, which is required for proper lung function6. These studies indicate that NKX2-1 is indispensable for lung development involved in breathing in part through the regulation of SFTPB and LAMP3 expression.Ever since it was first shown that SFTPA , including SFTPA1 and SFTPA2 , is induced by NKX2-1 in human lung epithelial cells6, in part through the proximal promoters of SFTPA1 and SFTPA2 bound by NKX2-1, which has been reported using gel-shift and plasmid-based reporter assays9. Although SFTPA1/SFTPA2 belong to the same surfactant protein gene family as SFTPB, Sftpa1-deleted mice  are viable at birth and have no apparent lung abnormality10. However, Sftpa1-deleted mice are susceptible to bacterial, viral, and fungal infection15, indicating that NKX2-1 is involved in innate defense by the clearance of respiratory bacteria, virus, and fungus through SFTPA.The microarray analysis also identified that 16. Since then, the antibody has been used extensively in the clinic to distinguish primary lung adenocarcinomas from lung metastases18. Notably, NKX2-1 is not only highly expressed in primary human lung adenocarcinoma, but also its chromosomal locus (14q13.3) is highly amplified in ~10% of human lung adenocarcinomas22. Discovery of the 14q13.3 amplification led to subsequent studies to understand the functional roles of NKX2-1 and its downstream target genes that influence lung tumorigenesis. Importantly, such studies have revealed that NKX2-1 functions as a tumor promoter or a tumor suppressor in a context-dependent fashion. For example, NKX2-1 promotes EGFR-mutant lung tumorigenesis while it suppresses KRAS-mutant lung tumorigenesis29. Among the downstream target genes induced by NKX2-1, MYBPH, LMO3, and CD274  are of particular interest since they directly influence lung tumorigenesis30. MYBPH  inhibits cell motility and metastasis of lung adenocarcinoma24 while LMO3 promotes cell survival28. CD274/PD-L1, a suppressor of adaptive immunity, expressed on lung tumor cells binds to PD-1 on T cells and inhibits the anti-tumorigenic activity of T cells31. These reports indicate that NKX2-1 influences lung tumorigenesis in part through the regulation of MYBPH, LMO3, and CD274/PD-L1 expression.Development of a monoclonal NKX2-1 antibody (8G7G3/1) revealed that NKX2-1 is expressed in normal human lung epithelium as well as in human lung carcinomas, especially lung adenocarcinoma and small-cell lung carcinoma, but not colon or breast carcinoma27. ChIP-seq analysis by us and others indicated that NKX2-1 bound to not only proximal upstream regions but also distal upstream and/or downstream regions as well as intronic regions and intergenic regions in the genome32. Although ChIP-seq can reveal where NKX2-1 binds in the genome, it does not indicate whether NKX2-1-binding sites are functionally important or not. The ability to determine the functional relevance of transcription factor-binding sites has recently been achieved using CRISPR/Cas9 technology. Mansour and colleagues using a CRISPR/Cas9-mediated deletion approach, deleted a transcription factor MYB-binding site (identified by ChIP-seq) between TAL1 and STIL, and showed that the MYB-binding site is required for the expression of TAL133. We have previously used the same approach and determined that a transcription factor SPDEF-binding site (identified by ChIP-seq) between MUC5AC and MUC5B is required for the expression of MUC5B30. We have also recently used a modified CRISPR/Cas9 technology (CRISPRi) that carries a deactivated Cas9 fused to the repressor KRAB (CRISPR/dCas9-KRAB) to repress the transcription of genes without creating DNA double-stranded breaks. Gene-regulatory regions in the genome that are targeted by CRISPRi are selected by 20-mer sgRNA target sequences35. We thus targeted the SPDEF-binding region and found that the CRISPRi approach is more streamlined than the CRISPR/Cas9-mediated deletion approach since cell cloning is not required in CRISPRi36. In the present study, using the CRISPRi approach, we set out to determine whether NKX2-1-binding sites that were previously identified by ChIP-seq are indispensable for the expression of NKX2-1 downstream target genes, including SFTPB, LAMP3, SFTPA1, SFTPA2, MYBPH, LMO3, and CD274/PD-L1, that are involved in breathing, innate defense, and tumorigenesis  regions using gel-shift and plasmid-based reporter assays, which do not provide a chromatin-based and genome-wide mechanism of gene regulation. Subsequently, we used the ChIP (chromatin immunoprecipitation)-seq assay to determine unbiased genome-wide NKX2-1 binding sites in human lung epithelial cells30 indicate that NKX2-1 binds to five regions  of the locus of SFTPB, which is an indispensable gene for breathing , indicating that these three regions  are critical gene-regulatory regions for the expression of SFTPB induced by NKX2-1. The sgRNA targeting the first intronic region most extensively suppressed the expression of SFTPB compared to those targeting the upstream region and ninth intron  . Hi-C data from A549 cells obtained by ENCODE data37 indicate that the SFTPB locus on chromosome 2 interacts with other loci in different chromosomes, including chromosome 3, supporting a possibility of such trans inter-chromosomal interactions  Fig.  though, NKX2-1 while A549 cells do not  is regulated by NKX2-1 in H441 cells  of the locus of LAMP3, a product of which associates with SFTPB    Fig. . Unexpec05) Fig.  though tlls Fig.  since thSFTPA1 and its paralog SFTPA2, products of which are critical for innate defense against respiratory bacteria, viruses, and fungi, are located at chromosome 10q22.3. The ChIP-seq data30 indicate that NKX2-1 binds to a proximal upstream region of SFTPA1 and a distal upstream region of SFTPA1 and SFTPA2, which are located at ATAC-inaccessible regions   Fig. . These slls Fig. .Fig. 5NKSFTPA1 and SFTPA2, which is bound by NKX2-1, is in an intergenic region (opposed to intragenic) that is ~20 kb away from both SFTPA1 and SFTPA2, we also employed the CRISPR/Cas9-mediated approach to delete the distal upstream region and assessed the expression of SFTPA1, SFTPA2, and other NKX2-1 downstream target genes. Consistent with the results obtained by the CRISPRi approach, deletion of the distal upstream region by CRISPR/Cas9 most significantly repressed the expression of both SFTPA1 and SFTPA2 in A549 cells  of the locus of MYBPH, a product of which is involved in cell motility and metastasis of lung adenocarcinoma  in A549 cells   Fig.  though tlls Fig. , suggest30 indicate that NKX2-1 binds to three distinct regions (ATAC inaccessible) at the second intron of LMO3, a product of which is involved in the survival of lung adenocarcinoma cells  in both A549 and H441 cells  of the locus of CD274/PD-L1, a product of which inactivates the anti-tumorigenic function of T cells  and also at the protein level in A549 cells. In contrast, the sgRNA targeting the first intron did not suppress the mRNA expression of CD274/PD-L1  significantly suppressed the expression of CD274/PD-L1 more than twofold (P < 0.05) in H441 cells . Furthermore, the CRISPRi data indicate that both regions are critically important for the expression of LAMP3 induced by NKX2-1 , which is ~20 kb away from both genes . MYBPH is induced by NKX2-1 and acts as a tumor suppressor24. The molecular mechanism by which NKX2-1 regulates the expression of MYBPH was previously studied using reporter assay and ChIP-qPCR focusing on its promoter region located at the proximal upstream region, which indicates that NKX2-1 activates the promoter region24. This result is consistent with our ChIP-seq and CRISRPi data targeting the same region , suggesting that the same CRISPRi approach targeting the region located at the first intron in H3122 cells would address the functional relevance of the region.In contrast to MYBPH, which acts as a tumor suppressor, LMO3 (an NKX2-1 downstream target gene) promotes the survival of lung tumor cellsMO3 Fig. . This diKRASG12S ). Our CR31. The gene-regulatory mechanism of CD274/PD-L1 has been described previously in reviews43. Our ChIP-seq data indicate that NKX2-1 binds to two regions of the CD274/PD-L1 locus, ~ 2.7 kb upstream region and +4.8 kb first intron , which can be targeted by therapeutic antibodies that activate anti-tumorigenic T cells by blocking the interaction between CD274/PD-L1 and PDCD1/PD-1ron Fig. . To our lls Fig. . Since t45. Future studies are required to understand the mechanism by which NKX2-1 accesses such chromatin-inaccessible genomic regions or to develop a means to assess chromatin accessibility other than ATAC-seq.In the present study, taking advantage of ENCODE database that harbors ATAC-seq datasets from A549 cells, we assessed whether NKX2-1 binds to chromatin-accessible genomic regions that are defined as such by ATAC-seq. To our surprise, more than half of the NKX2-1-binding sites were located at genomic regions that are considered to be chromatin inaccessible by ATAC-seq Fig. . To our SFTPB and SFTPA1/SFTPA2 in lung pathophysiology have been particularly well studied using Sftpb- or Sftpa1-knockout mice15. Notably, a region bound by NKX2-1 in the first intron of human SFTPB is highly conserved with the one in the upstream region of mouse Sftpb (84% similarity). Likewise, a region bound by NKX2-1 in the proximal upstream region of SFTPA1 is highly conserved with the one in the upstream region of mouse Sftpa1 (74% similarity). Using CRISPR/Cas9-mediated deletion, Simeonov et al. have assessed the in vivo role of a region in the first intron of mouse Il2ra in blood cells, a region of which is highly conserved with a gene-regulatory enhancer region of human IL2RA46. In future studies, targeting such conserved gene-regulatory regions in mice using CRISPR/Cas9 and/or dCas9 will further determine the importance of NKX2-1-binding sites as gene-regulatory regions in lung pathophysiology in vivo.Here, we investigated the roles of NKX2-1-binding sites in the expression of NKX2-1-regulated genes in human lung epithelial cells in vitro. Among such NKX2-1-regulated genes, the in vivo roles of 30 (GSE86959), was visualized using the UCSC Genome Browser47. Supplementary Figs. 48. Overlapped peaks among the three ATAC-seq replicates were considered as ATAC-seq peaks, and overlapped peaks between the two NKX2-1 ChIP-seq replicates were considered as NKX2-1 ChIP-seq peaks.ChIP-seq of NKX2-1 in an A549 human lung epithelial (carcinoma) cell line, which we previously performed twice independentlyENCSR662QKG) were downloaded from ENCODE web portal. Inter-chromosomal interactions with the SFTPB locus (including 10 kb upstream of the transcription start site and 10 kb downstream of the transcription end site) were extracted at the resolution of 50 kb using the “dump” function of Juicer tools (version 1.22)49. Inter-chromosomal interactions with at least two observed counts were visualized in a circular layout using BioCircos50.Mapping quality thresholded chromatin interactions (.hic files) of four replicates of ENCODE Hi-C on an A549 cell line  ordering tool . Non-targeted gRNA (sgRNA) was used as a negative control .Synthetic sgRNAs targeting each locus (36. A549 cells stably expressing dCas9-KRAB were infected by lentivirus carrying either rat Nkx2-1 or backbone control (PGK-IRES-EGFP)51. A549 and H441 cells expressing dCas9-KRAB were transfected with synthetic sgRNAs  or siRNAs targeting human NKX2-1  or human SFTPB   using Lipofectamine RNAiMAX Reagent .A549 lung carcinoma cells and H441 lung epithelial (papillary adenocarcinoma) cells  that stably express dCas9-KRAB were created and maintained as ATCC instructed36 using probes . A probe for dCas9-KRAB was custom designed by ThermoFisher as described in ref. 36.Gene expression analyses were performed using TaqMan gene expression analysis36 was performed, in which RNA was obtained from A549 cells constitutively expressing dCas9-KRAB that were transiently transfected with synthetic sgRNA targeting the first intron of SFTPB (#2) or non-targeted control gRNA. After quality assessment and pre-processing of RNA-seq reads36 were performed, gene expression was measured using htseq-count. Differential expression analysis for each comparison was performed using Bioconductor DESeq2 package. Differential expression with at least log2 fold change ≧1 or ≦−1 and padj<0.05 was considered significant.RNA-seqSFTPA1 and SFTPA2 was deleted in A549 cells using CRISPR/Cas930, in which sgRNA target sequences described in Supplementary Fig. An NKX2-1-binding site located at the intergenic region of 52 were performed using rabbit anti-NKX2-1 (TTF-1) antibody , rabbit anti-MYBPH , rabbit anti-CD274 (PD-L1) antibody , rabbit anti-Cas9 (dCas9) antibody , and rabbit anti-ACTA1 antibody .Immunoblot assayst-test on transfected vs. control sgRNA transfected cells' relative expression with a minimum P value < 0.05. GraphPad Prism 8 was used for graphing and statistical analysis. Raw numbers for all replicates that generated the results are shown in Supplementary Data Realtime qPCR results were normalized against non-targeted sgRNA samples in independent biological triplicates. Results are expressed as the mean ± SD of the triplicates for each group. Undetermined Ct values due to low or no expression were set as Ct=40. Statistical relevance was determined using a two-sided Student’s Further information on research design is available in the Supplementary informationDescription of Additional Supplementary FilesSupplementary Data 1–9Reporting Summary


We introduce Expected Pattern Effect and Differential Expected Pattern Effect, two black-box methods that can interpret genome regulatory sequences for cell type-specific or condition specific patterns. We show that these methods identify relevant transcription factor motifs and spacings that are predictive of cell state-specific chromatin accessibility. Finally, we integrate these methods into framework that is readily accessible to non-experts and available for download as a binary or installed via PyPI or bioconda at  Within the genome are the instructions to build all the cell types that make up the human body. However, understanding these instructions and how and when these instructions go wrong in cancer or genetically inherited disease is an open problem. Deep neural networks provide powerful models to learn the relationship between DNA sequence and functional consequence across many different cell types, such as whether a particular stretch of DNA is accessible and genes in that region can be expressed or is inaccessible and therefore genes are inactive. Despite these advances, a major setback in deep learning is that it is challenging to understand what patterns of DNA sequence that a deep learning model has learned to associate with a particular genomic function, whether these patterns are significant, and how to determine whether these patterns are specific to a particular cell type or are general “housekeeping” patterns that function across many cell types. We introduce Expected Pattern Effect and Differential Expected Pattern Effect, two methods which allow us to evaluate the significance of particular patterns of DNA sequence features on models trained to predict function across multiple cell types, and apply this to problems of transcription factor binding and DNA accessibility across multiple cell types. PLOS Computational Biology Methods paper.This is a de novo DNA sequence motifs that influence local cell type-specific accessibility in the context of a massively parallel reporter assay [Neural networks have been increasingly helpful in understanding and predicting cellular function from genomic sequence in tasks such as transcription factor binding ,2, chromer assay . Here, wer assay . A cell Recovering interpretable biological hypothesizes from trained deep neural network models has been a challenge given the complexity of the models. Contemporary approaches for interpreting deep learning models of genomic sequence features utilize three main approaches :Interpretation of model and individual sequences for important nucleotides using model saliency and motif generation from input sequences –11 or saInterpretation of model convolutional filters as position weight matrices ,4,12,13.Model response to in silico “designed” sequences ,14–17.a priori that particular combinations of features (patterns) that could have important effects, such as transcription factor motifs. Interpretation methods make use of our a priori knowledge about the patterns of features such as DNA motifs and relationships between patterns such as spacing to investigate what features the model has learned are overall most important through evaluating model response to “designed” DNA sequences in silico. However, these approaches have yet to explore how to assign the significance of these effects and how to interpret whether these effects are differential between two conditions, such as cell types, when applied to multi-task classification models.Methods in class 1 such as DeepSHAP ,18 and EHere we present a method of assigning both effect scores and significance values to DNA sequence feature patterns of interest, such as transcription factor motifs or grammars such as particular transcription factor motifs and motif spacings, that differentiate between cell types or conditions from a multi-task neural network . Signifiy, over N training examples, we can estimate the overall expected probability of y to be:In contrast to methods that analyze a single input sequence or a model, we build upon the use of “designed sequences” to create a computational method to measure the learned differential effect of a transcription factor motif or other DNA sequence pattern on the output of a multi-task classification model . These pp, such as a transcription factor motif  as the signed-rank test statistic of the ratios:c1 and c2 based upon the average log2 ratio of predictions:We then compute the Differential Expected Pattern Effect between two classes Rm represents the rank of the absolute log ratio of the expected pattern effect of the pattern, p, differentially between two classes c1 and c2, on a sequence m.In addition to computing the magnitude of the DEPE between two classes, we compute the significance of this effect:Our goal is to apply the DEPE to discover cell type-specific transcription factor activity from chromatin accessibility, but to first validate that DEPE can correctly identify differential transcription factor activity, we apply the DeepAccess architecture to predict DNA binding from ChIP-seq data. In contrast to chromatin accessibility where multiple or specific combinations of transcription factors may be responsible for activity, ChIP-seq should produce sequences enriched by the binding of a single DNA binding protein. Therefore, if a DeepAccess model has successfully learned differential DNA sequence patterns between two ChIP-seq experiments, DEPE should identify the DNA binding motifs of those transcription factors.We evaluated the effectiveness of the DEPE by evaluating how well it could identify sequence motifs that were responsible for differential ChIP-seq binding. We trained a single DeepAccess model to predict the DNA sequences bound by six ChIP-seq experiments for the transcription factors ELF1, MEF2A, CTCF, USF1, TCF12, and NRF1 in mouse erythroleukemia (MEL) cells from ENCODE . The traWe then used EPE and Differential EPE to investigate chromatin accessibility. First, we hypothesized that we can use EPE to investigate preferential spacing of Oct4 (Pou5f1) and Sox2 motifs within accessible DNA in stem cells, as these two transcription factors are known to cooperatively bind. We tested the EPEs for multiple spacings of Oct4 and Sox2 motifs  and findWe have increased the utility of our method by providing an executable version of DeepAccess for the Linux operating system as well as a python package available via PyPI and bioconda . Using KThe DeepAccess system, including methods for estimating EPE and Differential EPE, is available as a PyPI package and on bioconda. We also provide a binary of the DeepAccess system which avoids typical installation problems with the evolution of Python packages by packaging all dependencies including the Python interpreter within our software. Even when using package managers such as pip and conda which are present with frameworks such as Selene , Kipoi , and pyyWhile previous work has explored the contributions of neural network architecture, training methodology, and model interpretation to learn the relationship between DNA sequence and differential DNA binding , these nWhile DeepAccess was developed specifically for identifying cell type-specific sequence features from chromatin accessibility, Differential Expected Pattern Effect can be used to discover condition-specific sequence features from many types of experimental genome-wide sequencing data. We expect that DeepAccess will provide a useful approach for discovering sequence features that drive differential cell fates, and that the system we provide will make this capability accessible to a wide audience.Reads were trimmed for adaptors and low-quality positions using Trimgalore (Cutadapt v0.6.2)  and aligWe used as input to DeepAccess 200bp regions centered at the GPS-called ChIP-seq binding event. DeepAccess then uses these regions to create a training data set of 87,114 100bp sequences which can be labeled as binding events for any combination of the 6 tested transcription factors, including regions where none of the 6 transcription factors are predicted to bind. We define a region as a ChIP binding event for a transcription factor if more that 50% of the 100bp region overlaps the 200bp region centered at a GPS binding event. The DeepAccess model was then trained for 5 epochs with default parameters.https://zenodo.org/record/4908895#.YL6YpR0pDfY.Methods for DeepAccess and differential nucleotide importance with DeepAccess were previously described in Hammelman et al., 2020. Briefly, we trained DeepAccess on ATAC-seq data from ten cell types: stem cell, fibroblast, hepatocyte, endoderm, beta pancreatic cell, alpha pancreatic cell, cardiomyocyte, skeletal muscle, dopaminergic midbrain neuron, and spinal motor neuron  using biFor ATAC-seq processing, reads were trimmed for adaptors and low-quality positions using Trimgalore (Cutadapt v0.6.2) . Reads wChIP-nexus binding data for Oct4, Sox2, and patch cap control  were dowFor all experiments we used as our synthetic distribution sequences 24 randomly selected genomic locations that were not overlapping ATAC-seq accessible regions in all ten mouse cell types. We provide these regions with the DeepAccess executable as default backgrounds for new experiments.DeepAccess executable was generated with pyinstaller (v4.1).S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 FigScatter plots of GIA (A-C) or EPE/DEPE (D-F) of effect sizes of 356 HOCOMOCOv11 mouse motifs on predictions of CTCF binding from a trained DeepAccess model on balanced class data (x-axis) compared to 2x, 10x, and 30x more CTCF binding sites: USF1 binding sites (y-axis) (A) GIA of CTCF binding (B) GIA of USF1 binding (C) GIA of CTCF vs USF1 differential binding (D) EPE of CTCF binding (E) EPE of USF1 binding (F) DEPE of CTCF vs USF1 binding.(TIF)Click here for additional data file.S3 Fig(A) EPE of Hoxc9 motif on EPE from DeepAccess model trained to predict motor neuron chromatin accessibility shows EPE of Hoxc9 motifs are low for positions in the beginning of the DNA sequence and high for positions at the middle and end of the DNA sequence. (B) Scatter plot of EPEs for 356 HOCOMOCOv11 mouse transcription factor motifs when insertion is at position 0 compared to position 50 indicate early insertion site in DNA sequence results in different EPE values for transcription factors, with some having lower EPEs and others having higher EPEs. (C) Scatter plot of EPEs for 356 HOCOMOCOv11 mouse transcription factor motifs when insertion is at position 70 compared to position 50 indicate early insertion site in DNA sequence results in similar EPE values for all transcription factors. (D) Heatmap of Pearson’s r of EPEs for DeepAccess motor neuron accessibility for insertions at all starting positions within the DNA sequence shows most insertion positions result in highly similar EPEs except for early insertion positions. (E) Heatmap of Pearson’s r of DEPEs for DeepAccess motor neuron vs fibroblast differential accessibility for insertions at all starting positions within the DNA sequence shows most insertion positions result in highly similar EPEs except for early insertion positions. (F) Row z-score normalized EPEs for DeepAccess motor neuron accessibility at each position show most motifs have higher EPEs when inserted at a position in the center of the DNA sequence. Some motifs (like Sp motifs) have higher EPEs at the beginning of the DNA sequence.(TIF)Click here for additional data file.S4 Fig(A) Estimates of EPE for 356 HOCOMOCOv11 transcription factor motifs on predicted motor neuron accessibility from a learned DeepAccess model. M sequences are selected randomly from regions closed in all 10 cell types, open in all 10 cell types, promoters which are closed in all 10 cell types, and promoters which are open in all 10 cell types. Shade indicates distribution size . Boxplot box indicates median and quartiles and whiskers extend to 1.5 times the inter-quartile range. Dots are outliers outside 1.5 times the inter-quartile range. (B) Estimates of significance of EPE for size and type of sequence used to estimate total sequence distribution. (C) Comparison of EPEs for 356 HOCOMOCOv11 transcription factor motifs for default (randomly selected 24 closed) compared to randomly selected closed (row 1), randomly selected open (row 2), promoter closed (row 3), and promoter open (row 4) shows while values of EPE dependent on selection of sequence distribution, rank remains relatively invariant.(TIF)Click here for additional data file.


Schizosaccharomyces pombe. This association is RNase sensitive, correlates with Pol II transcription and mRNA expression levels. Changes in Pol II occupancy were detected in a Upf1 deficient (upf1Δ) strain, prevalently at genes showing a high Upf1 relative to Pol II association in wild-type. Additionally, an increased Ser2 Pol II signal was detected at all highly transcribed genes examined by ChIP-qPCR. Furthermore, upf1Δ cells are hypersensitive to the transcription elongation inhibitor 6-azauracil. A significant proportion of the genes associated with Upf1 in wild-type conditions are also mis-regulated in upf1Δ. These data envisage that by operating on the nascent transcript, Upf1 might influence Pol II phosphorylation and transcription.Although the RNA helicase Upf1 has hitherto been examined mostly in relation to its cytoplasmic role in nonsense mediated mRNA decay (NMD), here we report high-throughput ChIP data indicating genome-wide association of Upf1 with active genes in  Upf1 is a conserved protein of eukaryotes that has so far been primarily studied for its key role in nonsense-mediated mRNA decay (NMD). NMD is a translation-coupled cytoplasmic mechanism believed to recognise and rapidly destroy mRNAs carrying a premature termination codon or other features that place a stop codon in abnormal sequence contexts . HoweverUpf1 belongs to the 1B superfamily (SF1B) of helicases that are involved in a diverse range of cellular activities in all domains of life. These are characterised by conserved sequence motifs and the ability to translocate in a 5′ to 3′ direction on both RNA and DNA ,12. SpecIn yeast, as in other organisms, Upf1 is typically most abundant in the cytoplasm. For this reason, it is assumed that Upf1 operates on mRNAs only after their nuclear export. However, there is evidence to show that Upf1 traffics in and out of the nucleus in mammalian cells ,19. It wSchizosaccharomyces pombe with the aim to understand what roles Upf1 may have in the nucleus. The data demonstrate Upf1 binding to chromatin and indicate that this occurs primarily at active genes. This association is genome-wide and positively correlates with RNA Pol II loading and mRNA expression. Notably, this interaction is RNase sensitive and Upf1 does not co-purify with Pol II directly. These data therefore indicate that Upf1 binds the nascent transcript. Upf1 depleted cells show abnormalities in Pol II loading, CTD phosphorylation and are also hypersensitive to the 6-azauracil, a drug that can affect transcription elongation. Genes that are associated with Upf1 in wild-type conditions are more likely to be mis-regulated in upf1Δ cells. Cumulatively these findings predict that Upf1, by operating on nascent mRNA Pol II genes, can regulate their transcription.The putative association of Upf1 with chromatin was examined by chromatin immunoprecipitation (ChIP) in S. pombe strains used in this study is shown in Supplementary Table S1. Fission yeast transformation was carried out as described earlier (The complete list of  earlier . The tar earlier . All PCRS. pombe cells was done as described before (Protein extraction from d before . Membran600 = 0.5) were fixed for 5 min at room temperature with 1% formaldehyde (Sigma Aldrich) followed by 10 min incubation with a further addition of glycine to stop the cross-linking following published yeast ChIP protocols  (S. pombe genome coordinates (ftp://ftp.sanger.ac.uk/pub/yeast/pombe/GFF). The average enrichment was calculated between the start and end coordinates of enriched genomic regions, thereby giving each enriched region a score based on fold enrichment. Identification of significantly bound genomic features and enrichment score calculation was done using the statistical computing language R (http://www.R-project.org/). Functional annotation of the enriched regions was done using DAVID  software to analyse the Affymetrix hybridization data . ChIP iner (IGB) . When 50ng DAVID .600 0.5) of healthy S. pombe cells were pelleted down for 10 min at 5000 rpm at 4°C . The pellet was washed with buffer 1  and resuspended in lysis buffer . Small droplets of the lysate were made in liquid nitrogen and immediately kept at −80°C, they were typically processed the day after. Cells were subjected to grinding with SPEX SamplePrep 6775 freezer mill . An equal volume of lysis buffer and 110 U/ml of DNase  was added to the ground cell lysate followed by incubation for 1 h at 4°C. The sample was centrifuged at 16 000g for 10 min and the supernatant was transferred to a fresh tube. The supernatant (input) was incubated with 5 μg of anti-Flag antibody-coated Dynabeads for 1 h at 4°C on a rotator. After incubation, Dynabeads were washed 6 times for 10 mins each with wash buffer . Beads were incubated with elution buffer  for 30 min at 4°C on a rotator. The elution fraction was collected by separating the beads using a magnet.Exponentially growing cultures (ODhttp://tools.thermofisher.com/content/sfs/brochures/sadd_whitepaper.pdf) in order to obtain a signal value for each gene for each array. The published Ser5 Pol II ChIP-chip datasets were downloaded from https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-18/ . The positions of each probe within their corresponding gene were obtained by matching the probe sequence to that of the gene's fasta sequence including coding sequence, introns and UTRs (https://www.pombase.org/downloads/genome-datasets). The probe position was calculated as the percentage through the gene of the 5′-most base's mapping, from the TSS (0%) up to 24 bp upstream of the TES (100%) since the probes are 25 bp in length. We constructed metagenes based on two Upf1 IP arrays, one with asynchronous and one with S-phase synched cells and a pair of corresponding control arrays, as well as a pair of Ser5 Pol II IP arrays along with their pair of corresponding control arrays. Firstly, for each probe in each array, we calculated the ratio of perfect match (PM) and mismatch (MM) probe signal values. Then for the pairs of Upf1 and Pol II control arrays we calculated the mean of the PM / MM signal ratio from each probe to obtain Upf1 and Pol II control mean probe signals. The asynchronous and S-phase Upf1 probe signals were then divided by the Upf1 control mean probe signals to obtain their normalised values, which were used in for plots. For the pair of Pol II IP arrays, each one was normalised by dividing by the Pol II control mean probe signals, and the mean of these normalised values was taken for each probe to obtain the mean normalised Pol II signal. The metagene plots themselves show the average of the values calculated from probes mapping to each 0.1% block of gene bodies.The individual probe signal values from the CEL file corresponding to each of the previously discussed arrays were extracted and associated with their probe sequence and the gene they map to, using the Sp20b_M_v04 chip description file , EDTA 1 mM (pH 8.0), NaCl 150 mM, Triton X-100 1%, Na-Doc- 0.1% and 1X protease inhibitor . Immunoprecipitations were performed using Protein G Dynabeads (Life Technologies) coated with 10 μg monoclonal anti-Flag M2 antibody (Sigma). Both IP and Input DNA were purified using MinElute PCR Purification Kit (QIAGEN). 10 ng of DNA was used for DNA library construction with the NEBNext Ultra II DNA Library Prep Kit (New England Biolab E7645L), indexed using NEBnext Multiplex Oligos for Illumina Dual Index Primers (New England Biolabs E7600S) and sequenced simultaneously using a Illumina HiSeq4000 System.Exponential cultures of S. pombe genome build (Ensembl), which was downloaded from (https://emea.support.illumina.com/sequencing/sequencing_software/igenome.html?langsel=/gb/). The alignment was carried out using Bowtie2 (upf1Δ cells) were averaged to obtain one set of Pol II coverage values for each strain.The sequence reads in the FASTQ files were trimmed using Trimmomatic to remove low quality reads . The SE  Bowtie2 , which wS. pombe RNA-seq datasets were downloaded from the Gene Expression Omnibus, with sample IDs GSM2803075 and GSM2803077 from the series with ID GSE104546 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104546), previously described  from 7054 total annotated genes in the version of the genome analysed; (ii) sampling 543 genes from 5280, as previously tested  falls in the distribution. P-values were also calculated using a Fisher's exact hypergeometric test approach as described in https://rdrr.io/bioc/GeneOverlap/man/GeneOverlap.html.Previously published whole-genome microarray RNA expression data of was used . Differea 1% FDR . The ovey tested ; (iii) c600 (∼0.7) and treated with 150 μg/ml 1,10-phenanthroline (Sigma). Cultures were removed at different time points  after addition of the drug, and immediately transferred to falcon tubes containing 40 ml of ice-cold water prior to RNA extraction.Total RNA was extracted using the hot acid-phenol method . ExtractS. pombe . LMB inhibits the CRM1-dependent protein and some pathways of RNA export, yet most mRNAs are exported by CRM1-independent mechanisms in yeast and human  epitope by homologous recombination and ChIP was performed using an HA antibody. This allowed for genome-wide enrichment profiles which were determined by hybridisation of the immunoprecipitated DNA to genomic tiling chip arrays . We examined both asynchronous and S phase synchronised cell cultures, in duplicate experiments. Significantly enriched regions were identified using the Model-based Analysis of Tiling Arrays (MAT) software . A total of 594 and 696 genes that are significantly enriched by Upf1 ChIP in 50% or more of their sequence were identified in asynchronous and S phase cultures, respectively  in nd human ,39. Thery Figure . Most ofy Figure .The ChIP-chip data also showed enrichment of repetitive sequences such as centromere and telomere regions, as well as Tf2 retrotransposons . The association was sensitive to RNase treatment of the chromatin . Unlike in other organisms, Ser5 Pol II is not restricted to promoter-proximal regions in ve genes ,40. Therve genes .Next, we compared Upf1 enrichment values to gene expression by comparing the Upf1 ChIP-chip signal values with RNA-seq FPKM values for all genes . Again, a positive correlation is observed between the Upf1 enrichment values from both asynchronous and S phase samples and the RNA-seq FPKM values (based on the top 90% expressed genes according to the RNA-seq data), with a Spearman's rank correlation of 0.38 and 0.42 in asynchronous and S phase respectively Figure . These cpma1, met26, ght5, mug106 and tpi1 , to 1kb downstream of the transcript end site (TES) and for the gene body of all annotated protein coding genes . This analysis indicated the expected Pol II gene loading for S. pombe in both wild-type or upf1Δ with the characteristic increased signal of Pol II downstream of the TES  high Upf1 signal relative to Pol II (587 genes), 2) low Upf1 signal relative to Pol II (124 genes) or 3) similar levels of Upf1 and Pol II . We identified a total of 543 genes differentially expressed between the wild-type and upf1Δ using these parameters. Of these, 159 show reduced mRNA levels, whereas almost double this number (384) of genes show increased mRNA levels in upf1Δ . Notably, two of these genes are met26 and ght5, which as described above show increased Ser2 signal, and in the case of ght5, also increased total Pol II in upf1Δ compared to wild-type. Both genes are upregulated in upf1Δ  located in the 5′ UTR of pma1 is downregulated in upf1Δ . It has previously been reported that strains carrying mutations in components of the RNA polymerase II transcription elongation machinery are hypersensitive to 6AU . The upf1Δ strain also shows a slow growth phenotype in the presence of 6AU, in both liquid cultures and on agar plates . The overall cellular level of Ser2 seems lower in upf1Δ barring in the densest culture examined . There might also be less of the cross-reacting faster migrating bands in upf1Δ; these could represent Rpb1 cleavage products of different sizes. Consistent with this interpretation, the largest of these products is also prominent in the SDS-PAGE of affinity purified Pol II fractions and its identity was validated by mass-spectrometry , at others it might be primarily due to Ser2 CTD hyperphosphorylation. Consistent with hyperphosphorylation, the band corresponding to Ser2 phosphorylated Pol II (Rpb1) but not that corresponding to unphosphorylated Rpb1 migrates slower in upf1Δ compared to wild-type. It is therefore likely that more of the CTD repeats are phosphorylated in wild-type compared to upf1Δ. Ser2 hyperphosphorylation has previously been linked to slow transcription elongation in mammalian cells  SuperSeries record: GSE169425 -https://github.com/Brogna-Lab/PombeUpf1.The description of the bioinformatics pipelines used, custom-made scripts for correlation and metagene plots, raw data files and processed data tables are available at the GitHub repository: gkab1249_Supplemental_FilesClick here for additional data file.


Hnf4a gene in mouse liver causes associated reductions in the genome-wide distribution of core clock component BMAL1 and accessible chromatin marks (H3K4me1 and H3K27ac). Ectopically expressing HNF4A remodels chromatin landscape and nucleates distinct tissue-specific BMAL1 chromatin binding events, predominantly in enhancer regions. Circadian rhythms are disturbed in Hnf4a knockout liver and HNF4A-MODY diabetic model cells. Additionally, the epigenetic state and accessibility of the liver genome dynamically change throughout the day, synchronized with chromatin occupancy of HNF4A and clustered expression of circadian outputs. Lastly, Bmal1 knockout attenuates HNF4A genome-wide binding in the liver, likely due to downregulated Hnf4a transcription. Our results may provide a general mechanism for establishing circadian rhythm heterogeneity during development and disease progression, governed by chromatin structure.Transcription modulated by the circadian clock is diverse across cell types, underlying circadian control of peripheral metabolism and its observed perturbation in human diseases. We report that knockout of the lineage-specifying  Genome-wide occupancy of the master circadian clock transcription factor BMAL1::CLOCK varies across tissues and is reprogrammed in cancers, but how specificity is governed is not known. Here the authors show BMAL1::CLOCK in liver tissue is guided by chromatin accessibility remodeled by HNF4A, shedding new lights onto mechanisms of dysregulated circadian rhythms in hepatocarcinoma. Period , Cryptochrome , and Rev-erb  genes at the beginning of the feedback cycles. Protein dimer formed by PER and CRY suppresses the transcriptional activity of BMAL1::CLOCK, closing the first feedback loop. The second feedback mechanism is achieved by the nuclear receptor REV-ERBs to repress the transcription of the Arntl  gene (and to a lesser extent on Clock gene)1.The circadian clock is a molecular oscillator that aligns behavior and physiology with daily light–dark cycles. The core of the mammalian circadian clock, composed of two interlocked transcriptional feedback loops, relies on chromatin occupancy of the master transcription factor heterodimer BMAL1::CLOCK at the E-box DNA element. BMAL1::CLOCK positively regulates expression of the 6, contributing to organ-specific physiology and disorders associated with circadian misalignment7. However, the molecular mechanisms underlying heterogeneous circadian rhythms remain unclear. Tissue-specific chromatin occupancy of the core clock transcription factors BMAL1::CLOCK and REV-ERBα has been described, identifying co-occupancy of tissue-specific transcription factors10. In the context of these prior studies, it is of interest to apply genetic approaches to ascertain whether tissue-specific TFs influence clock TFs’ loading onto chromatin or the other way around.While many peripheral organs have circadian clocks, the identities of rhythmic outputs are considerably divergent across tissues11. Chromatin remodeling that opens condensed chromatin structures is initiated by the recruitment of lineage-specifying pioneer transcription factors to their target DNA sequences at enhancers. The pioneer TFs recruit histone methyltransferases MLL3/4 to deposit histone mark H3K4me1, whereby the condensed DNA wrapped around histones is loosened12. Completely activated enhancers feature bimodal distribution of histone modifications H3K4me1 and H3K27ac, nucleosome depletion, and recruitment of other transcription factors and coactivators13. Instead of being simply correlated with chromatin accessibility, H3K4me1 has an active regulatory role by serving as docking sites for chromatin remodelers14. Due to the activity of ATP-dependent chromatin remodelers15 and three-dimensional chromatin folding16, chromatin remodeling commonly creates extended accessibility beyond the central nucleosomes pioneer TFs bind.In multicellular organisms, cells from different tissues exhibit specialized gene expression profiles in part achieved by physically sequestering unnecessary genes into heterochromatin. Genes that are required for particular tasks of a cell type display accessible chromatin structure allowing for the binding of necessary machinery to facilitate gene expression4. The hepatic circadian transcripts are highly organ-specific and involved in most principal functions of the liver, including glucose homeostasis, lipogenesis, bile acid synthesis, mitochondrial biogenesis, oxidative metabolism, amino acid turnover, and xenobiotic detoxification. Indeed, environmental or genetic disruption of the circadian clock exacerbates the development of liver diseases such as non-alcoholic fatty liver disease (NAFLD), hepatitis, cirrhosis, and hepatocellular carcinoma (HCC)17. The hepatocyte nuclear factor 4A (HNF4A) is a nuclear receptor specifically expressed in the liver, kidney, pancreas, and intestinal tracts18. Mutation or dysregulation of the Hnf4a gene is associated with human diseases such as maturity-onset diabetes of the young (MODY) and HCC20. Whole-body Hnf4a knockout resulted in embryonic lethality, and liver-specific knockout mice displayed severe hepatocyte differentiation defects and premature death by 8 weeks of age23. We previously demonstrated that HNF4A modulates peripheral circadian clocks in cell cultures24. Here, we further interrogate the interface between HNF4A and the circadian clock in the liver tissue where they both play critical roles. We find that HNF4A supervises BMAL1 chromatin binding seemingly by remodeling chromatin accessibility. Synchronized with HNF4A recruitment24, mouse liver displayed increased genome-wide chromatin accessibility during the night. Furthermore, the circadian clock contributes to chromatin remodeling likely through regulating HNF4A. Our results reveal a collaborative effort between HNF4A and the clock machinery in shaping tissue-specific chromatin landscape and circadian rhythms that are vital for liver biology.With most (~16%) transcripts exhibiting circadian expression, the liver is the primary organ controlled by the circadian clock24. While physical interactions and genome-wide co-occupancy between the diurnal regulatory machinery and tissue-specific transcription factors have been reported27, to our knowledge, how the tissue-specific factors may affect BMAL1::CLOCK recruitment has not been studied. To investigate the influence of HNF4A on BMAL1::CLOCK chromatin occupancy and circadian rhythms, we crossed Hnf4a floxed mice22 with Albumin-Cre mice and Per2-luciferase mice in the same C57BL/6J background to generate liver-specific Hnf4a knockout  and control  mice (see the “Methods” section). In the HKO liver, RT-qPCR confirmed a ~75% decrease in Hnf4a transcript level accompanied by downregulation of the classic HNF4A target genes ApoC3, Fabp1, Ppara, and Hnf1a  of the three ChIP-seq replicates revealed clustering of samples from the same genotype , enhanced (3 peaks), or not significantly changed (1384 peaks). On average, BMAL1 peaks of higher intensity tended to be more responsive to Hnf4a ablation  a simultaneous loading mediated by protein–protein interactions; (2) a sequential loading that requires a pioneer TF to open up local chromatin for other factors to bindtes Fig. . Moreovetes Fig. . Therefo13. Therefore, the intensity of H3K4me1 and H3K27ac defines chromatin landscape and is indicative of pioneer TFs’ activity. In agreement with our prediction, we observed a clear reduction in genome-wide H3K4me1 and H3K27ac deposition upon Hnf4a knockout , relative to the unchanged H3K4me1 sites  in Fig. 36. We found that ectopically expressing the fetal isoform HNF4A8 induced tissue-specific BMAL1 binding likewise . We mapped genome-wide DNA binding of HNF4A at ZT16 in liver samples collected from liver-specific Bmal1 knockout  or control mice , identifying about 14%  of total HNF4A ChIP-seq peaks reduced and about 1%  enhanced in BKO liver  or H3K27ac (n = 134) peaks identified the ROR response element (RORE), binding motif of transcriptional repressors REV-ERBs  or H3K27ac (n = 101) peaks, we did not identify the E-box element but instead found an enrichment of nuclear receptor binding sites, with the HNF4A-binding motif top-ranked   Fig.  and downT6) Fig. . The aduver Fig. , the chr38  by Hnf4a knockout 22 were crossed with Albumin-Cre mice (The Jackson Laboratory #003574) and Per2-luciferase reporter mice (The Jackson Laboratory #006852)67 to obtain Hnf4afl/+;Alb-Cre+/−;Per2-luc+/+; and Hnf4afl/fl;Alb-Cre−/−;Per2-luc+/+ mice, which were then mated to obtain Hnf4afl/fl;Alb-Cre+/−;Per2-luc+/+ (HKO) and Hnf4afl/fl;Alb-Cre−/−;Per2-luc+/+ (Control) littermates. Bmal1 floxed mice (The Jackson Laboratory #007668)51 were crossed with mice expressing Albumin-Cre (The Jackson Laboratory #003574) to obtain Arntlfl/+;Alb-Cre+/− and Arntlfl/fl;Alb-Cre−/− mice which were then mated to obtain Arntlfl/fl;Alb-Cre+/− (BKO) and Arntlfl/fl;Alb-Cre−/− (Control) littermates. In all experiments, male mice between 10 and 12 weeks of age were used. In all experiments except the jet lag treatment, mice were housed in a room with controlled temperature of 21–23 °C and humidity of 35–40% under a 12-h light/12-h dark (LD) cycle with free access to food and water. The chronic jet lag treatment was performed by housing experimental mice in the light-tight circadian cabinet and switching lighting conditions between two light onset schedules which are apart by 8 h every 3 days from 7 to 11 week of age.All animal care and experiments were performed under the institutional protocols approved by the Institutional Animal Care and Use Committee  at the University of Southern California. EGFP, Hnf4a2 , and Hnf4a8  genes were subcloned into the lentiviral vector pLV-EF1a-IRES-Ametrine. The constructed vectors were then co-transfected with the envelope and packaging plasmids into HEK 293T cells for lentivirus production. Viral supernatants were collected twice, at 48 and 72 h after transfection, pooled and filtered through 0.45 μm filters, and then added to U2OS cells for transduction. The positively transduced U2OS cells were selected by FACS sorting according to the Ametrine signals.Hnf4a knockout and MODY Hnf4a(R85W) mutation in Hep3B cells expressing Bmal1-Luc reporter were generated using CRISPR-Cas9. HEK 293T, U2OS, and Hep3B cells were grown in complete DMEM (Life Technologies cat. #11995065) supplemented with 10% FBS and 1% penicillin/streptomycin. HepG2 cells were grown in Ham’s F12 (Corning Cellgro 10-080-CV) supplemented with 10% FBS, and 1% penicillin and streptomycin. All cells were grown in a 37 °C incubator maintained at 5% CO2. The Hep3B cell line was a gift from Dr. Michael Karin at UCSD, originally purchased from ATCC (HB-8064). HEK 293T, U2OS, and HepG2 cell lines were directly purchased from ATCC .62. In brief, cell culture media was replaced with HEPES-buffered phenol-free DMEM media containing 100 nM dexamethasone and 100 μM D-luciferin. Dishes were covered with 40 mm glass coverslips (Fisher Scientific) and sealed with vacuum grease to prevent evaporation. Luminescence signals were monitored every 10 min using the LumiCycle luminometer (Actimetrics) at 37 °C without supplementary CO2. Results shown are representative of at least three independent experiments.For circadian assays, Hep3B cells were plated on 35-mm dishes and synchronized as previously described by a dexamethasone shock2. The circadian period was calculated using the LumiCycle software (Actimetrics). Results shown are representative of at least three independent experiments.Mice were anesthetized with isoflurane and euthanized by cervical dislocation, and then livers were rapidly removed and kept on ice. Liver sections (2–3 mm*2–3 mm) were cultured in explant medium . Dishes were covered with 40 mm glass coverslips (Fisher Scientific) and sealed with vacuum grease to prevent evaporation. The luminescence signals of reporter cells were monitored every 10 min using a LumiCycle luminometer (Actimetrics) at 37 °C without added CO24. Antibodies used in the western blots are anti-BMAL1 , anti-HNF4A , and anti-TUBULIN .Frozen mouse liver tissue was homogenized in RIPA buffer containing 1× EDTA-free protease inhibitor cocktail (Roche) by using Omni Tissue Homogenizer . The concentration of total protein was determined by Bio-Rad Protein Assay and then equalized to 15 μg/μl. 25 μg of total protein was used for western blot assay which was performed as previously describedhttps://www.idtdna.com/PrimerQuest). Primer sequences are:Liver tissues of 10–12-week-old male mice were harvested at indicated Zeitgeber times. Total RNA was isolated using TRIzol Reagent according to manufacturer’s instructions (Life Technologies cat. #15596026), and then reverse transcribed to cDNA using iScript cDNA Synthesis kit (Bio-Rad cat. #1708891). We designed real-time primers spanning the exon–intron junctions using the IDT primer-designing software PrimerQuest .24. Briefly, blood perfused liver tissues were dissected and snap-frozen in liquid nitrogen. Frozen liver tissues were minced with razor blades and homogenized by pushing through 18 G needle followed by 21 G needle to release nuclei. Nuclei were immediately cross-linked with 1% formaldehyde for 10 min at room temperature and then quenched with glycine. After two washes, the nuclei were lysed in nuclear lysis buffer  containing 1× EDTA-free protease inhibitor cocktail (Roche) and kept on ice for 5 min. Immediately, the lysates were sonicated 17 times for 30 s by using Bioruptor (Diagenode). After centrifugation at 17,000×g for 15 min at 4 °C, fragmented chromatin was diluted 10-fold with IP dilution buffer  supplemented with 1× EDTA-free protease inhibitor cocktail, and incubated with antibody overnight at 4 °C. Dynabeads Protein G (Invitrogen cat. #10004D) was added to the chromatin and incubated for another 3 h. Afterwards, the beads were washed once with low salt wash buffer  and once with high salt wash buffer . IP elution buffer  was applied to the bead pellet and incubated at 30 °C for 15 min. The eluate was added with NaCl to the final conc. of 200 mM and reverse-crosslinked at 65 °C overnight. To remove RNA and protein, RNase A and proteinase K were subsequently applied by incubating at 45 °C for 1 h. Finally, DNA was purified with QIAquick PCR Purification columns.ChIP-seq experiments with mouse liver were performed as previously describedChIP-seq experiments with human cell lines were similar. The differences are the cells were crosslinked in culture dishes with 1% formaldehyde, and then scraped off for cell lysis and chromatin fragmentation. Antibodies used in the ChIP-seq experiments are anti-HNF4A , anti-BMAL1 , anti-H3K4me1 , anti-H3K27ac , and anti-FOXA2 .Single-end ChIP-seq reads were trimmed using Trimmomatic (v0.36) and then aligned to hg38 or mm10 genome with Bowtie2 (v2.3.4.1). BAM files were processed using SAMtools (v1.10) and PCR duplicates were removed with PicardTools (v2.18.3). Peaks were called in MACS2 (v2.1.2) using default settings and IgG mock ChIP files for normalization. BAM files of replicate samples were merged using SAMtools. BIGWIG track coverage files were generated from merged BAM files using the DeepTools (v3.3.0) bamCoverage command with RPGC normalization.q-value of 0.05.Heatmaps and metaplots were generated by the computeMatrix, plotHeatmap, and plotProfiles functions of DeepTools (v3.3.0) using BIGWIG files (replicates merged) and scaled regions. DiffBind (v3.2.7) was used to make PCA plots. Statistically significantly differential peaks were called and MA plots were generated by using the DESeq2 method within DiffBind, which selected differential regions based on ChIP signals in each replicate and FDR-corrected https://david.ncifcrf.gov).HOMER (v4.11.1) mergePeaks program was used to identify overlapping binding loci of two transcription factors. In order to define the sites as “overlapping”, peak centers of the two binding sites must be at a distance ≤500 bp. Note that the peak numbers may not add up exactly since the function automatically resolves redundant overlaps by dropping one fragment during analysis. Motif enrichment analysis was performed using HOMER findMotifsGenome.pl command and scanned ± 200 bp from the peak center for binding sites of transcription factors, and ± 750 bp for histone modifications. HOMER annotatePeaks.pl command was used to make annotations of genomic features. The functional analyses of GO term  and KEGG pathway were performed using the clusterProfiler package in R or DAVID  and then mapped to mm10 mouse genome using Bowtie2 (v2.3.4.1). SAMtools (v1.10) was used to generate BAM files, remove PCR duplicates, and remove mitochondrial DNA. MACS2 version 2.1.2 was used for peak calling with the following parameters: --nomodel --broad --shift -100 --extsize 200 --keep-dup all.t-test (two-tailed), with significant differences at p < 0.05. For motif analysis, HOMER findMotifsGenome.pl calculated p-values using the cumulative binomial distribution. For GO term and KEGG pathway analyses, clusterProfiler calculated p-values using a hypergeometric distribution which was then adjusted for multiple comparison.The significance of differences between peak distance, period length, and gene expression was evaluated by unpaired Student’s Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Reporting Summary


SET and MYND domain-containing protein 1 (Smyd1) is a striated muscle-specific histone methyltransferase. Our previous work demonstrated that deletion of Smyd1 in either cardiomyocytes or the outflow tract (OFT) resulted in embryonic lethality at E9.5, with cardiac structural defects such as truncation of the OFT and right ventricle and impaired expansion and proliferation of the second heart field (SHF). The cardiac phenotype was accompanied by the downregulation of ISL LIM Homeobox 1 (Isl1) and upregulation of atrial natriuretic factor (ANF). However, the mechanisms of Smyd1 regulating Isl1 and ANF during embryonic heart development remain to be elucidated. Here, we employed various biochemical and molecular biological approaches including chromatin immunoprecipitation polymerase chain reaction (ChIP-PCR), pGL3 fluorescence reporter system, and co-immunoprecipitation (CoIP) and found that Smyd1 interacted with absent small homeotic-2-like protein (ASH2L) and activated the promoter of Isl1 by trimethylating H3K4. We also found that Smyd1 associated with HDAC to repress ANF expression using trichostatin A (TSA), a deacetylase inhibitor. In conclusion, Smyd1 participates in early heart development by upregulating the expression of Isl1 and downregulating the expression of ANF. Congenital heart disease affects 0.8–1 child per 100 live births and is responsible for the majority of prenatal deaths . AlthougHistone modifications are the main components of epigenetics and play important roles in regulating mammalian development . These min vitro . All experiments were performed in compliance with the guidelines of the Animal Care and Use Committee of Hainan Medical University.Floxed–s Nkx2.5 . Homozygin situ hybridization was carried out as previously described  on a microtome. Sections were incubated with bovine serum albumin for 20 min at 37°C and then incubated with either rabbit anti-human ASH2L polyclonal antibody  or mouse anti-human Smyd1 (Santa Cruz Biotech) at 4°C for overnight. Samples were then washed in PBS three times for 15 min at room temperature and incubated with the secondary antibody Alexa Fluor 488 goat anti-mouse IgG (Abcam) and Alexa Fluor 568 goat anti-rabbit IgG (Abcam). Sections were stained with  (DAPI) for 10 min and mounted on slides with 50% glycerol in PBS. The IF images were captured by a confocal microscope .Smyd1 Cko embryos were homogenized for H3K4me3 and H3K27me3 ChIP analysis. Antibodies against Smyd1 were provided by Dr. Philip Tucker. Anti-H3K4me3 and anti-H3K4me3 antibodies were purchased from Abcam. RNA polymerase II was provided by Upstate EZ ChIP kit. IgG (Santa Cruz) was used as a negative control in ChIP. The sequences of primers used for ChIP assays were shown in TM PCR Systems (Stratagene). For ChIP experiments, 1 μl diluted (1:5) ChIP DNA or input DNA was used as a template, and relative fold enrichments were determined by the 2ΔCT method described in the Applied Biosystems User Bulletin. Ratios were determined from three independent qPCR.Chromatin immunoprecipitation assays were carried out using Upstate EZ ChIP kit (Upstate Biotechnology) according to the protocol provided by the manufacturer’s protocol with slight modifications. Briefly, heart tissues from 45 E9.5 control and Isl1 promoter, a 1.4-kb genomic DNA fragment (−1203 to +204) of the mouse Isl1 5′-promoter was subcloned into the KpnI and XhoI sites of the pLG3-basic vector (Promega). The primers wereTo generate the luciferase reporter of the mouse 5′ primer 5′-CCCGGGGGTACCGGCAAAAGTTACCGGT GAGA-3′ and3′ primer 5′-CCCGGGCTCGAGAGAGAGGATGCTGGTGC TGT-3′.Isl1 5′-promoter were generated using the QuikChange sited-directed mutagenesis kit (Stratagene) according to the manufacturer’s protocol. The primers for point mutations at site SB3 were:Point mutations of the Smyd1-binding site in the ANF 5′-promoter region were purchased from IGE Biotechnology, and mutations on the 5′ primer 5′-CTCTTCTTTTGCAACAGACAGACCCTGCTC ACGCCTTCATAC-3′3′ prime 5′-GTATGAAGGCGTGAGCAGGGTCTGTCTGTT GCAAAAGAAGAG-3′The primers for point mutations at site SB4 were:5′ primer 5′-GAAAGCTAACCTCAGGGGAAAAAAGTAG GCGCCCCACTCTTC-3′3′ primer 5′-GAAGAGTGGGGCGCCTACTTTTTTCCCCT GAGGTTAGCTTTC-3′ASH2L cDNA were obtained from ATCC and subcloned into the pCDNA3.1 expression vector. The ANF luciferase reporter construct and HDAC expression vectors were generated as previously described (PCDNA3.1-Myc-Smyd1 expression vectors and fragment mutants were provided by Dr. Deepak Srivastava. Smyd1 HMTase-inactive mutant and PBS-Y234F were provided by Dr. Philip Tucker. Y234F was subcloned into the PCDNA3.1 vector at ClaI and XbaI sites. The primers for PCDNA3.1-Y234F cloning were:Mouse escribed . PCDNA3.5′ primer 5′-CCCGGGATCGATATGGAGAACGTGGAGG TCTT-3′3′ primer 5′-CCCGGGTCTAGAGGGTCACTGCTTCTT ATGGAACAG-3′TM 2000 (Invitrogen) in OPTImum medium (Gibco) 24 h after plating. At 16 h post-transfection, the medium was changed to the regular medium. Cells transfected with plasmids or the control vectors exhibited green fluorescence under fluorescence microscopy, and the transfection efficiency was about 80%. In HDAC inhibition experiments, 100 or 150 ng/ml trichostatin A (TSA) was added 24 h after transfection. Cells were harvested 40 h after transfection, and luciferase activity assays were performed with BD Monolight 3010. The data shown for transfections are the means from at least three independent experiments.C2C12 and CV1 cells were maintained in Dulbecco’s modified Eagle’s medium supplemented with 10% fetal bovine serum (Hyclone). Cotransfections were carried out using LipofectamineCo-IP assays were performed as previously described . Co-IP oV5-tagged ASH2L and MYC-tagged Smyd1 were cotransfected to HeLa cells. 40 h after transfection, whole-cell lysates were collected and stored at −80°C till used. The complex formation was determined by gel filtration performed on the FPLC equipped with a Superdex 200 10/300GL column. Briefly, the column was equilibrated with buffer A  and calibrated by standard molecular weight markers (Sigma MW-GF-200). The calibration curve was obtained with the method provided by Sigma Technical Bulletin (MW-GF-200). Cell lysates  were loaded into the column and run on buffer A at 0.75 ml/min. The fraction collector was set at 1 ml/fraction and turned on after 7.0 ml was eluted out. After the 10th fraction, the fraction collector was switched off. Each fraction was precipitated with 10% TCA and run in SDS-PAGE and analyzed by Western-blot.Isl1 promoter was labeled with [α-32P] dCTP. 1 μg of extracted proteins was incubated with 50,000 cpm of the 32P-labeled Isl1 probe at room temperature for 20 min in a buffer containing 1 μg poly dI-dC, 120 mM KCl, 25 mM MgCl2, 20 mM Tris–HCl [pH 7.9], 2 mM DTT, and 2 mM EDTA. In cold probe competition experiments, cold WT or MUT probes were added to the binding mixture at different ratios (from 5× to 200×). After a 15-min incubation on ice, labeled probes were added, and the samples were further incubated on ice for 20 min. In antibody competition experiments, 2 μg anti-MYC antibody was added to the reaction tubes for an additional 20-min incubation. A nondenaturing 5% polyacrylamide gel was used to separate the protein–DNA complexes. After electrophoresis 2.0 h at RT, the gels were dried at 80°C for 45 min and exposed to X-ray films.Nuclear proteins were extracted from CV1 cells which overexpressed MYC-tagged Smyd1 protein. Nuclear extract preparation and EMSA were carried out following the procedure described previously . Brieflyt test was used for statistical comparisons when appropriate. A p value < 0.05 was considered statistically significant.Data are presented as mean ± SD. Student’s in situ hybridization on embryos collected between E8.5 and E10.0 using a Smyd1 probe. At E8.5–9.5, high Smyd1 expression was observed in the outflow tract, ventricle, and atria . That fragment contained two candidate sequences, SB3 and SB4 (a “GGAGGG” site at −1119 to −1114 bp and a “CCCTCC” site at −1081 to −1075 bp). We also observed that the Smyd1 binding to other regions was much weaker than to P4 .Myc-Smyd1 expression plasmid, the probe which contained the SB4 site bound to Smyd1 avidly. This DNA–protein complex was reduced by an anti-MYC antibody and was abolished in the presence of an excess of the cold cognate DNA sequence as a competitor, while a mutant probe failed to compete for Smyd1 binding  was transfected with the pLG3-Isl1-WT reporter as well. The mutant Smyd1 failed to activate the Isl1 promoter  and the pLG3-Isl1-WT reporter showed that the mutant Smyd1 failed to induce Isl1 expression  site located at −74 to −69 bp upstream to TSS. Primers were designed to cover that region . To observe the potential synergy between Smyd1 and HDAC, we wanted to find out an optimal dosage of Smyd1. However, even a tiny amount of Smyd1 plasmid (20 ng per well of 12 wells plate) induced a dramatic decrease in ANF (50%). This is not surprising because CV1 cells expressed endogenous HDACs that may already be saturated. Then, we had to test it in another way. TSA, the HDAC inhibitor, was added to the cotransfection system at a concentration of 100 ng/ml. The inhibition from Smyd1 decreased to 50% compared to the non-TSA control. When the concentration of TSA was increased to 150 ng/ml, the inhibition caused by Smyd1 was completely abolished , which is much stronger than the bindings on TSS and other regions. Furthermore, the H3K4me3 ChIP-qPCR analysis revealed that the H3K4me3 level in the P4 region was significantly reduced with no big effect on the TSS region. Those data suggested that Smyd1 was responsible for the H3K4me3 of the P4 site, but not that of the TSS. One question about those results is why no change of H3K4me3 was observed in the TSS region. It is possible that some other methyltransferases work on the TSS region of the Isl1 promoter. This may also explain why the expression of Isl1 was partially downregulated but not totally turned off in Smyd1-Cko mutant embryos.Global mapping of H3K4me3 sites shows that they are highly enriched in the proximal promoter of the expressed genes (within 1 kb of known or predicted transcript start sites) . MoreoveNkx2.8 and hTER genes to direct oncogenesis (Smyd1 and Smyd3 is different, they are highly homologous in structure, suggesting that Smyd1 has the same DNA-binding potential as Smyd3. Our EMSA assays demonstrated that Smyd1 bound to the SB4 site of the P4 region avidly. In addition, Smyd1 could activate pLG3-Isl1 Luc promoter activity alone. Site mutation on the SB4 site in the P4 region blocked the pLG3-Isl1 Luc reporter activity. Those data strongly support the premise that Smyd1 not only has the methyltransferase potential but also has the ability to bind to a specific binding motif on the promoters of target genes. This may be a common character of Smyd family members, which is different from another methyltransferase, such as MLL1. Based on the specific DNA-binding potential of Smyd1, one possible mechanism is the high-level H3K4me3 at the P4 region which facilitated the reorganization of the Isl1-targeted motif by another transcriptional factor. A reference example is transcription factor Myc (Isl1 promoter. Since the regulatory mechanisms underlying the Isl1 expression are largely unknown, we have few confirmed information about the transcriptional factors which regulate Isl1. In addition, our data demonstrated that Smyd1 interacted with ASH2L. Hence, it is possible that ASH2L may bridge Smyd1 to work with other factors to regulate gene activation. So far, the association between Smyd1 and H3K4me3 on the Isl1 promoter may function as a key event for cascade initiation of Isl1 promoter.The research on Smyd3, a family member of Smyd1, reveals that Smyd3 could specifically bind to 5′-CCCTCC-3′ or 5′-GGAGGG-3′ motifs on the promoter region of ogenesis . Althougctor Myc . ResearcANF. In Smyd1-Cko embryos, ANF was upregulated in the left ventricle and atrium (ANF promoter. Furthermore, the inhibition of ANF by Smyd1 was at least partially through the association with HDACs. Those data strongly suggest that Smyd1 directly controls the transcription of ANF. In addition, BNP and β-MHC were all upregulated in Smyd1-Cko embryos. β-MHC was also enhanced in the left ventricle and atrium region (Developmental processes are always dynamic , during d atrium . The Smym region . The potOur current study identified Smyd1 as a critical epigenetic factor in early heart morphogenesis through both positive and negative regulation. Smyd1 decreased the expression of key factors, which directs cardiac progenitor cell development, such as Isl1. To examine the exact function of Smyd1 in directing the cardiac progenitor cell population, the overexpression experiments in ES cells are being undertaken. Meanwhile, evidence showed that Smyd1 carried out diverse functions on different gene promoters through association with cofactors, including ASH2L, HSP90α, HDAC, and skNAC. In the future work, it is worthy to find out more cofactors of Smyd1 and how they cooperate with Smyd1 to control gene expression.Isl1 and inhibited ANF transcription. The interaction between Smyd1 and other cofactors may help it to perform those different functions under various conditions, such as ASH2L and HDAC. More factors involved in this machinery are waiting to be elucidated.In summary, our studies provided new insights about the functional mechanisms of Smyd1 in heart development. Smyd1 activated the expression of The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.The animal study was reviewed and approved by the Ethical Committee of The First Affiliated Hospital of Hainan Medical University.RS and YM designed the research. YM, ZW, QL, and JL performed the research work. YM, ZW, and FS analyzed the results. YM, ZW, and QL wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Catalyzed by zDHHC-PAT enzymes and reversed by thioesterases, protein palmitoylation is the only post-translational modification recognized to regulate the sodium/calcium exchanger NCX1. NCX1 palmitoylation occurs at a single site at position 739 in its large regulatory intracellular loop. An amphipathic ɑ-helix between residues 740–756 is a critical for NCX1 palmitoylation. Given the rich background of the structural elements involving in NCX1 palmitoylation, the molecular basis of NCX1 palmitoylation is still relatively poorly understood. Here we found that (1) the identity of palmitoylation machinery of NCX1 controls its spatial organization within the cell, (2) the NCX1 amphipathic ɑ-helix directly interacts with zDHHC-PATs, (3) NCX1 is still palmitoylated when it is arrested in either Golgi or ER, indicating that NCX1 is a substrate for multiple zDHHC-PATs, (4) the thioesterase APT1 but not APT2 as a part of NCX1-depalmitoylation machinery governs subcellular organization of NCX1, (5) APT1 catalyzes NCX1 depalmitoylation in the Golgi but not in the ER. We also report that NCX2 and NCX3 are dually palmitoylated, with important implications for substrate recognition and enzyme catalysis by zDHHC-PATs. Our results could support new molecular or pharmacological strategies targeting the NCX1 palmitoylation and depalmitoylation machinery. In healthy myocardium, NCX1 mainly generates an inward Na+ current and extrudes Ca2 (1 Ca2+: 3 Na+) [The cardiac Naanger NCX regulate 1 Ca2+:  Na+ [3],: 3 Na+)  and arrh: 3 Na+) ,6.Multiple regulatory mechanisms control NCX1. Two calcium binding domains (CBDs) in the large regulatory loop A facilitPalmitoylation relies on the direct conjugation of a fatty acid  onto cysteine residue(s) of a target protein via thioester bond . A singlGiven the structural insights into the underlying mechanism of NCX1 palmitoylation reported so far, our knowledge on enzymatic regulation of NCX1 palmitoylation is limited to the recent discovery of zDHHC5 and APT1 mediated palmitoylation and depalmitoylation of the exchanger . zDHHC5 Despite the considerable importance of palmitoylation in NCX1 physiology, there are still missing pieces regarding molecular specificity of NCX1 palmitoylation. In this study, we set out to investigate the spatial control of NCX1 palmitoylation, and how in turn this controls NCX1 localisation. We report that by interacting with the NCX1 palmitoylating enzymes, the amphipathic α-helix responsible for NCX1 palmitoylation directs the subcellular distribution of NCX1. By arresting NCX1 in intracellular compartments we determine that it is a substrate for both ER and Golgi-resident zDHHC-PATs. We also find that the same the amphipathic α-helix responsible for NCX1 palmitoylation directs dual palmitoylation of NCX2 and NCX3. Finally we report that ATP1 but not APT2 controls the spatial organisation of NCX1 in the cell, but that it is unable to depalmitoylate NCX1 in the ER. Our findings defining the molecular nature of NCX1 palmitoylation have important implications in the design of pharmacological tools to target NCX1 palmitoylation to cope with cardiac pathologies associated with abnormal exchanger activity.22.1266Y, [266–765) covering the CBDs and palmitoylation site  with only the palmitoylation site  or ER (DsER-red) markers . In separate experiments we obtained similar results when we examined colocalization of NCX1266–765 with HA-tagged Golgin-84 (Golgi marker) or HA-tagged Ii (ER marker) .NCX1 architecture consists of 10 T M domains and a large intracellular loop positioned between TM5 and 6, which harbours 3 functionally key components: 20aa long XIP peptide, two CBDs and the palmitoylation site A. Here,  middle, B and rese right, B, were ee right, B which i markers C, then a markers D. Both f266–765 localization is altered when its palmitoylation is drastically abolished by either (1) breaking the amphipathic ɑ-helix that is required for NCX1 palmitoylation (M744 P/H745 P/F746 P) or (2) introducing negatively charged aa; Glu- (E), to position 746 and 750 in hydrophobic part of the ɑ-helix (F746E/F750E)  E, or (3)E/F750E) E. All exE/F750E) F.2.2740–756) peptide from cell lysates. Strikingly, we pulled down all zDHHC isoforms with different propensities to bind to the ɑ-helix peptide  was previously identified as the “control unit” of NCX1 palmitoylation ,19. We a peptide A. It is  peptide B. This s2.3Given our finding that the palmitoylation-directing NCX1 helix interacts with the zDHHC-PAT(s), we turned our attention to the same region of NCX2 and NCX3. These transporters both possess a cysteine in the equivalent position to C739, followed by the identical amphipathic α helix with a second cysteine at its C terminal end   System . RUSH sy266Y) B, and (2266Y) B. HA-tag266Y) C. When S266Y) D. These 266Y) D. We fou2.5266–765, was over-expressed in HEK293 cells together with either APT1- or APT2- tagged with CFP, or alone as a control. In order to detect the activity of depalmitoylation machinery of NCX1, we first calculated the size of the compartmentalized NCX1 population in the cell in proportion to the total cell size for each cell  covering the palmitoylation site are compartmentalized in both Golgi and ER. Our previous investigations identified the Golgi as the main cellular location in which NCX1266–765 became localised [What do these observations tell us? The important take-home message here is that substrate recognition by zDHHC-PATs occurs at the ɑ-helix structure within the NCX1 intracellular loop, and NCX1 can be palmitoylated locally in either Golgi or ER which is determined by the nature of palmitoylation complex. The notion of local palmitoylation has been previously hypothesised for several proteins [We first noted that NCX1 fragments . Again, this contrasts with the behaviour of protein kinases, which is generally much more strict. Evidently the crystal structure of the zDHHC-PATs has greatly increased our understanding of this class of enzymes , but a cOur finding that NCX1 is palmitoylated immediately after translation implies that this regulatory mechanism controls NCX1 activity from very early in the lifetime of the protein. Since palmitoylation controls XIP sensitivity of NCX1, our results suggest that NCX1 is sensitive to XIP throughout its lifetime in the cell. Ultimately the toxicity of NCX1 mediated cellular Ca overload means understanding the NCX1 regulatory mechanisms is a high priority. The complex interplay between NCX1 palmitoylation and the ionic  and phospholipid (PIP2) regulatory mechanisms evidently merits further investigation. For example, Ca occupancy of CBD2 opposes NCX1 inactivation . The fac3.3Data presented in this investigation shows that APT1 but not APT2 controls subcellular distribution of NCX1. Curiously, although APT1 is clearly capable of depalmitoylating NCX1, ER-localized palmitoylated NCX1 is unavailable to APT1. We suggest that APT1 is excluded from the ER, and that its ability to depalmitoylate NCX1 (and likely other substrates) hinges on their proximity in the same membrane compartment, rather than simple diffusion. This contrasts with the suggestion that the cytosolic pool of APT1 is responsible for substrate depalmitoylation in all membrane compartments . Since A3.4Although experimental conditions were optimised to minimise non-specific binding, we acknowledge that overexpressed integral membrane proteins such as zDHHC-PATs can be susceptible to non-specific affinity purification. As such, we cannot rule out the possibility that some zDHHC-PATs co-purify non-specifically with our NCX1 peptide. Notwithstanding this point, our functional and imaging experiments clearly support the concept that NCX1 is palmitoylated by multiple zDHHC-PATs in multiple cellular compartments.In this investigation we focussed entirely on NCX1 palmitoylation and depalmitoylation mechanisms in HEK cells. The overlap between the repertoire of zDHHC-PATs expressed in cardiac muscle is substantial ,42. We h3.5Palmitoylation is a critical process for physiological regulation of NCX1. Here we bring an important aspect into NCX1 palmitoylation by defining its molecular identity. Results of this study could possibly pave the way for new molecular/pharmacological strategies targeting the NCX1 palmitoylation/depalmitoylation machinery. As we now know that zDHHC-PATs and APT1 physically interact with NCX1 at amphipathic ɑ-helix, manipulating the availability of this region for binding of palmitoylating and depalmitoylating enzyme would be helpful to tune NCX1 physiology. More broadly, this investigation specifically supports the concept of ‘local palmitoylation’. Several zDHHC-PATs localised in different subcellular compartments are clearly capable of palmitoylating NCX1. We suggest that a similar principal will apply to numerous other palmitoylated integral membrane proteins as they move through the secretory pathway.44.1HEK293 cells and HEK293 derived FT293 cells stably expressing Golgi- (Golgin84), ER- (Ii) hook were generated using Flp-In T-Rex System (Invitrogen). Plasmids expressing YFP tagged full length NCX1 , YFP tag4.2Resin-assisted capture of acylated proteins (Acyl-RAC) was performed to purify palmitoylated proteins as described in detail elsewhere ,18,19. FTo measure palmitoylation site occupancy we used a PEGylation assay that exchanges palmitates for a 5 kDa PEG molecule . Briefly4.3740–756), as described elsewhere [HEK293 cells were transfected with HA tagged zDHHC isoforms. Following 24 h expression of zDHHC-PATs, cells were lysed and solubilised in lysis buffer; 2 mg/mL C12E10 supplemented with protease inhibitors for 30 min at 4 °C. Lysates were first precleared by incubating with equilibrated streptavidin-beads, then interacting zDHHC-PATs with NCX1 were pulled down using biotinylated NCX1 peptide (Alta Biosciences); corresponds to residues between 740 and 756  fused to full length NCX1 tagged with YFP at 266 position). Golgin-84 or Ii were fused to streptavidin and tagged with the hemagglutinin (HA) tag, and stably expressed in HEK293 derived FT293 cells using Flp-In T-Rex System (Invitrogen). SBP was fused to NCX1266Y using In-Fusion cloning (TakaraBio Inc.) and expressed in either Golgi or ER hook stably expressing cells.Retention using selective hook (RUSH) system described previously  was adap4.5Confocal imaging was performed on HEK293 cells, Golgi- and ER-hook stable cells. 24−48 h after transfection with the plasmids, cells were fixed in 4% PFA for 10 min at room temperature. DNA of the cells were counter-stained with Hoechst to locate the nucleus, where it is indicated. Confocal images were captured with Zeiss LSM880 with Airyscan confocal microscopy. Diode (405−430 nm), Argon  and Hene594 (594 nm) lasers were used for detecting blue (Hoechst or CFP), green (YFP) and red (DsRed or mcherry). Colocalization of NCX1 with Golgi- and ER- markers were analysed using Coloc2 macro in Fiji . To quan4.6t-test using GraphPad Prism. Information about sample size, means and SEM and p-values can be found in Figure legends.Data are presented as mean ± standard error of the mean (SEM). Quantitative differences between groups were calculated by students’ 10.13039/501100000274British Heart Foundation: FS/14/68/30988 to WF and NF, SP/16/3/32317 and PG/18/60/33957 to WF and a Centre of Research Excellence award RE/18/6/34217.We acknowledge financial support from the Further information and requests for resources and reagents should be directed to and will be fulfilled by the corresponding author, William Fuller.CG: Conceptualization, Investigation, Formal Analysis, Writing – original draft.AM: Investigation.XG: Methodology, Resources.ZK: Methodology, Investigation.FP: Investigation.CWK: Methodology, Resources.ADR: Methodology, Resources.NJF: Funding Acquisition.WF: Conceptualization, Project Administration, Funding Acquisition, Supervision, Writing – review & editing.The authors report no declarations of interest.