Salmonella enterica pathogenicity island 1 (SPI-1) encodes proteins required for invasion of gut epithelial cells. The timing of invasion is tightly controlled by a complex regulatory network. The transcription factor (TF) HilD is the master regulator of this process and senses environmental signals associated with invasion. HilD activates transcription of genes within and outside SPI-1, including six other TFs. Thus, the transcriptional program associated with host cell invasion is controlled by at least 7 TFs. However, very few of the regulatory targets are known for these TFs, and the extent of the regulatory network is unclear. In this study, we used complementary genomic approaches to map the direct regulatory targets of all 7 TFs. Our data reveal a highly complex and interconnected network that includes many previously undescribed regulatory targets. Moreover, the network extends well beyond the 7 TFs, due to the inclusion of many additional TFs and noncoding RNAs. By comparing gene expression profiles of regulatory targets for the 7 TFs, we identified many uncharacterized genes that are likely to play direct roles in invasion. We also uncovered cross talk between SPI-1 regulation and other regulatory pathways, which, in turn, identified gene clusters that likely share related functions. Our data are freely available through an intuitive online browser and represent a valuable resource for the bacterial research community. Salmonella enterica and requires secretion of specific proteins into host cells via a type III secretion system (T3SS). Most T3SS-associated proteins required for invasion are encoded in a horizontally acquired genomic locus known as Salmonella pathogenicity island 1 (SPI-1). Multiple regulators respond to environmental signals to ensure appropriate timing of SPI-1 gene expression. In particular, there are seven transcription regulators that are known to be involved in coordinating expression of SPI-1 genes. We have used complementary genome-scale approaches to map the gene targets of these seven regulators. Our data reveal a highly complex and interconnected regulatory network that includes many previously undescribed target genes. Moreover, our data functionally implicate many uncharacterized genes in the invasion process and reveal cross talk between SPI-1 regulation and other regulatory pathways. All datasets are freely available through an intuitive online browser.Invasion of epithelial cells is an early step during infection by  Salmonella enterica is the causative agent of typhoid fever and is also a major cause of foodborne illness  . This T3SS and most of the secreted effector proteins are encoded in Salmonella pathogenicity island 1 (SPI-1), a horizontally acquired chromosomal region of ~40 kbp , which are required for attachment of S. Typhimurium to epithelial cells  . There ahimurium . During  system TSS. This  system TSS. This  ~40 kbp 6. The ma ~40 kbp , 8. HilD ~40 kbp . HilD ac ~40 kbp –12, and ~40 kbp –, 10, 11.~40 kbp ––16. Appral cells . HilD almotility , 19. RegsprB is induced under conditions associated with invasion  (hilC (OmpR) (hilA (PhoP and FNR)  , 38, or g., Fur)  or activg., Fur) , 41. TraC (OmpR)  or hilA and FNR) , 43. By http://www.wadsworth.org/research/scientific-resources/interactive-genomics or http://salmonella.wadsworth.org). Our data reveal a complex web of regulation, with all SPI-1-associated TFs being regulated by at least one other. We also identified many novel regulatory targets for SPI-1-associated TFs. Expression of these genes is therefore tightly coordinated with the invasion process. Notably, many of the novel regulatory targets encode TFs or small RNAs (sRNAs), indicating that the regulatory network associated with invasion extends well beyond the known SPI-1-associated TFs. By comparing gene expression profiles for the regulatory targets of the seven analyzed TFs, we identified 17 genes whose expression closely mirrors that of known SPI-1 genes. These genes are therefore likely to play direct roles in invasion. We also identified clusters of genes with similar expression profiles, suggesting cross talk between invasion gene regulation and other regulatory pathways.HilD and HilA targets have been identified on a genomic scale using chromatin immunoprecipitation (ChIP) methods and transcription profiling , 13. HowS. Typhimurium invasion  , STM14_1613 (STM1329), STM14_1614 (STM1330), mcpC, STM14_5184 (STM4310), STM14_5185 (STM4312), STM14_5186 (STM4313), and STM14_5189 . Th. ThrtsB.pression . We deteenes see . Given tsprB see , we concAlthough a few regulatory targets have been described for SPI-1-associated TFs other than HilD, only the HilD and HilA regulons have been determined on a genomic scale , 13. As Tables S1 to hilA, hilD, and hilC but that HilC binding is less sensitive to mutations within the DNA site . HoweveS. Typhimurium . Thus, many of the target genes of HilD, HilC, and RtsA are likely regulated by a countersilencing mechanism. This is consistent with the fact that hilD, hilC, and rtsA were horizontally acquired by S. enterica , sicA, and sopE, have been described previously . sopB and sopE2 encode SPI-1-secreted proteins, and sicA encodes a chaperone for SPI-1 secreted proteins. sopB and sopE2 are located outside SPI-1. Our ChIP-seq and RNA-seq data for InvF confirm direct regulation of sopE2 , is shown in HilD and HilC positively regulate of SPI-4 . Our ChIrtsA and rtsB are strongly induced under conditions associated with activation of SPI-1 genes , the flagellar Sigma factor  as likely direct targets of HilA. Surprisingly, the 13 genes located outside SPI-1 that have been previously reported as direct HilA targets  that are typically noncoding . sRNAs ohimurium –59. Two imurium –, 60. We imurium –. This meimurium – from thedapZ  and in vivo (mouse models of infection) (–P = 4.7E−9). Specifically, mutants with mutations in nine of the novel invasion genes tested  were defective in establishing an infection in all three infection models  –68. We aflhC, flgM, flgN, and mcpC, are direct targets of either HilD or RtsB  T3SS that secretes proteins required for intracellular survival and persistence  were used for ChIP-seq. Cells were grown overnight in LB (0.17 M NaCl), subcultured 1 in 50 in high-salt LB (0.3 M NaCl), and shaken at 50 rpm at 37°C for 5.5 h. ChIP-seq was performed using M2 anti-FLAG antibody (Sigma), as described previously  to identify enriched sequence motifs. Only motifs with an hilD, hilC, rtsA, invF, sprB, rtsB, and hilA, respectively) were used for RNA-seq. These strains were transformed either with empty pBAD24 or with pBAD24 derivatives pBLP013, pBLP011, pBLP010, pCDS003, pCDS002, pCDS001, and pCDS004, which express hilD, hilC, rtsA, invF, sprB, rtsB, and hilA, respectively (the pBAD24-encoded TF matched the deleted TF gene for each strain). Cells were grown overnight in LB (0.17 M NaCl), subcultured 1 in 50 in high-salt LB (0.3 M NaCl), and shaken at 50 rpm at 37°C for 5.5 h, and arabinose was added  for 7 min before harvesting was performed. Note that 7 min was chosen as the induction time because a previous study had showed that expression of a regulator leads to direct regulatory effects that occur between 5 and 10 min after induction  by >2-fold in cells lacking the TF and cells in which the TF was transiently expressed. All RNA-seq analysis data generated using a reference genome are listed in The following procedure was applied for each of HilD, HilC, RtsA, InvF, SprB, and RtsB. RNA-seq data were analyzed using Rockhopper  , comparihilD, hilC, rtsA, invF, sprB, and rtsB were assigned as source nodes, and their target genes were assigned as target nodes. The regulatory relationships between the TFs and their target genes were represented by directed edges. Node identity is double-encoded as shape, indicating source or target nodes, and color, indicating the level of regulation. For target genes regulated by multiple TFs, the level of regulation shown is for the highest TF in the hierarchy HilD > HilC > RtsA > InvF > RtsB > SprB.The gene regulatory network graph was constructed and visualized using Cytoscape software (version 3.1.1) . TF geneq = <0.01) by >2-fold in cells lacking the TF and cells in which the TF was transiently expressed. We aligned these RNA sequences to the 14028s genome using CLC Genomics Workbench . We then compared the positions of the starts of these regulated transfrags to the positions of ChIP-seq peaks for the corresponding TF. Significantly regulated transfrags were classified as direct regulatory targets if the 5′ end of the transfrag was determined to be ≤200 bp downstream or ≤100 bp upstream of a ChIP-seq peak.The following procedure was applied for each of HilD, HilC, RtsA, InvF, SprB, and RtsB. We used Rockhopper to analyze RNA-seq data without a reference genome sequence or annotation  . We thenWe selected all HilD or RtsA ChIP-seq peaks that were located within 100 bp of a HilC ChIP-seq peak. If multiple HilC peaks were found within 100 bp of a HilD/RtsA peak, we selected the HilC peak with the highest Fold Above Threshold (FAT) score. For 400 bp surrounding each of the HilC peaks, we determined the sequence read coverage on each strand for each of the two replicate ChIP-seq datasets for HilC and for HilD/RtsA . We normalized the sequence read coverage in each case to the highest value in the 400-bp window. We then combined data for both strands and for both replicates for each TF. These data are represented by the heat maps in E value of 1E−10). We then extracted expression profiles for these genes for the 29 RNA-seq experiments described in reference For all direct regulatory targets of SPI-1-associated TFs see  , subcultured 1 in 20 in high-salt LB (0.3 mM NaCl), and shaken at 50 rpm at 37°C to achieve an optical density at 600 nm (OD600) of 0.8 to 0.9, and arabinose was added  for 7 min before cells were harvested. For analysis of invF 5′ UTR, RNA was taken from RNA-seq samples for CDS024 plus pBLP013. For analysis of STM14_5569 5′ UTR, RNA was taken from RNA-seq samples for CDS022 plus pBLP011. For analysis of ybdQ, osmB, and galF 5′ UTRs, RNA was taken from RNA-seq samples for CDS030 plus pCDS001. RNA was purified and treated with DNase I as previously described  are available at the EBI ArrayExpress database with accession numbers E-MTAB-3987 and E-MTAB-3765.Figure S1 sinR. (B) RNA-seq and ChIP-seq data for HilD, for the region encompassing hilC and sprB. (C) RNA-seq and ChIP-seq data for HilC, for the region encompassing cspE. (D and E) RNA-seq and ChIP-seq data for InvF, for the regions encompassing sopE2 (D) and STM14_1486 (STM1239) (E). (F) RNA-seq and ChIP-seq data for SprB, for the region encompassing STM14_2227 (STM1841). (G) RNA-seq and ChIP-seq data for SprB, for the region encompassing siiA (first gene in SPI-4). (H) RNA-seq and ChIP-seq data for RtsB and HilD, for the region encompassing flhDC. (I) RNA-seq and ChIP-seq data for RtsB and HilD, for the region encompassing stdA. (J and K) RNA-seq data for HilA, for the regions encompassing pspA to pspE (J) and yiaD (K). (L) RNA-seq and ChIP-seq data for HilD, HilC, and RtsA, for the region encompassing dapB. DapZ, the sRNA that initiates within dapB, is indicated by brackets. Download Genome coverage plots for SPI-1-associated TFs. Histograms show RNA or ChIP sequence read coverage across selected genomic regions. Genes shaded in red, blue, and black indicate positive regulation, negative regulation, and no regulation, respectively. Black arrowheads indicate the position of ChIP-seq peaks. (A) RNA-seq and ChIP-seq data for HilD, for the region encompassing Figure S1, PDF file, 0.4 MBFigure S2 hilC and sprB are cotranscribed. An agarose gel shows products from rtPCR performed using primers that span the region between hilC and sprB. L, 100-bp ladder; +, PCR was performed using a sample that was generated with reverse transcriptase and RNA purified from cells transiently overexpressing HilD; −, PCR was performed using a control sample that was generated without adding reverse transcriptase and RNA purified from cells transiently overexpressing HilD (negative control); C, PCR was performed using a colony of S. Typhimurium (positive control). Download Figure S2, PDF file, 0.1 MBFigure S3 sopB and sopE (in strain SL1344)  . Shaded Figure S3, PDF file, 0.1 MBFigure S4 S. Typhimurium (positive control). A black arrow to the right of each gel image indicates the expected PCR product size. Download Identification of transfrags as 5′ UTRs using rtPCR. Agarose gels show products from rtPCR performed using primers that span the indicated regions. L, 100-bp ladder; +, PCR was performed using a sample that was generated with reverse transcriptase and RNA purified from cells transiently overexpressing HilD; −, PCR was performed using a control sample that was generated without adding reverse transcriptase and RNA purified from cells transiently overexpressing HilD (negative control); C, PCR was performed using a colony of Figure S4, PDF file, 0.1 MBTable S1 Complete RNA-seq analysis for HilD, HilC, RtsA, InvF, SprB, RtsB, and HilA.Table S1, XLSX file, 2.8 MBTable S2 List of all ChIP-seq peaks for HilD, HilC, RtsA, InvF, SprB, and RtsB.Table S2, XLSX file, 0.02 MBTable S3 List of all direct regulatory targets for HilD, HilC, RtsA, InvF, SprB, and RtsB.Table S3, DOCX file, 0.03 MBTable S4 List of transcript variants (5′ UTRs) regulated by SPI-1-associated TFs.Table S4, DOCX file, 0.01 MBTable S5 Bacterial strains and plasmids used in this study.Table S5, DOCX file, 0.02 MBTable S6 Oligonucleotides used in this study.Table S6, DOCX file, 0.02 MB


ATAC-seq (Assays for Transposase-Accessible Chromatin using sequencing) is a recently developed technique for genome-wide analysis of chromatin accessibility. Compared to earlier methods for assaying chromatin accessibility, ATAC-seq is faster and easier to perform, does not require cross-linking, has higher signal to noise ratio, and can be performed on small cell numbers. However, to ensure a successful ATAC-seq experiment, step-by-step quality assurance processes, including both wet lab quality control and in silico quality assessment, are essential. While several tools have been developed or adopted for assessing read quality, identifying nucleosome occupancy and accessible regions from ATAC-seq data, none of the tools provide a comprehensive set of functionalities for preprocessing and quality assessment of aligned ATAC-seq datasets.ATACseqQC, for easily generating various diagnostic plots to help researchers quickly assess the quality of their ATAC-seq data. In addition, this package contains functions to preprocess aligned ATAC-seq data for subsequent peak calling. Here we demonstrate the utilities of our package using 25 publicly available ATAC-seq datasets from four studies. We also provide guidelines on what the diagnostic plots should look like for an ideal ATAC-seq dataset.We have developed a Bioconductor package, https://bioconductor.org/packages/release/bioc/html/ATACseqQC.html.This software package has been used successfully for preprocessing and assessing several in-house and public ATAC-seq datasets. Diagnostic plots generated by this package will facilitate the quality assessment of ATAC-seq data, and help researchers to evaluate their own ATAC-seq experiments as well as select high-quality ATAC-seq datasets from public repositories such as GEO to avoid generating hypotheses or drawing conclusions from low-quality ATAC-seq experiments. The software, source code, and documentation are freely available as a Bioconductor package at The online version of this article (10.1186/s12864-018-4559-3) contains supplementary material, which is available to authorized users. In eukaryotes, nuclear DNA is primarily found packaged in nucleosomes, each of which consists of ~ 147 bp of DNA coiled around a histone octamer core. Two adjacent nucleosomes are usually spaced by linker DNA of ~ 20–90 bp which can be bound by a linker histone H1 [In recent years, several high-throughput methods have been developed to assess chromatin accessibility, nucleosome positioning, and occupancy of DNA-associated proteins. These include three direct chromatin accessibility methods , PCR bottleneck coefficients 1 and 2 [To promote component reusability and compatibility among Bioconductor packages, several existing Bioconductor packages are leveraged. Alignment results in BAM files are first efficiently imported into R for quality assessment as samtools  and GenoicRanges  packages 1 and 2 ), and otfragSizeDist function in ATACseqQC can be used to plot the fragment size distributions of the filtered BAM files. Then, coordinates of read alignments are shifted using the shiftGAlignmentsList and shiftReads functions in ATACseqQC as described [splitGAlignmentsByCut and writeListOfGAlignements, are implemented for separating shifted reads into nucleosome-free and oligo-nucleosome-bound regions, which are used for the following analyses. To visualize aggregated signals around transcription start sites (TSSs) as heatmaps and histograms, the enrichedFragments, featureAlignedHeatmap and matplot functions from packages ATACseqQC, ChIPpeakAnno and graphics [IGVSnapshot is implemented to allow streamlined visualization of read distribution along any genomic regions of interest such as those containing housekeeping genes, leveraging the SRAdb package [Next, the escribed . Two fungraphics , 26 are  package  and the  package .pwmScore, plotFootprints and factorFootprints are implemented in ATACseqQC. It makes use of genomic sequences as BSgenome objects, available for various reference genomes, which can be efficiently accessed by methods in the BSgenome package [MotifDb package [matchPWM function in the BSgenome package [GenomicRanges objects, and plot the motif as a sequence logo using the motifStack package [factorFootprints function first uses the matchPWM function to predict the binding sites with an input position weight matrix (PWM) for a DNA-binding protein. Next, it calculates and plots the average cutting rates for those binding sites and 100-bp flanking sequences.For transcription factor footprint analysis,  package , and of  package . The foo package , 31 to s package . The facreadsDupFreq, estimateLibComplexity, and saturationPlot are implemented in ATACseqQC. The estimateLibComplexity function is built on the ds.mincount.bootstrap function implemented in preseqR [For the library complexity evaluation and sequence depth analysis,  preseqR .ATACseqQC are described in the vignette and manual provided with the package.An installation guide and additional generic use cases for Case studies.ATACseqQC [Twenty-five ATAC-seq datasets from four studies were downloaded from NCBI SRA Table  and analTACseqQC , 34, 35 TACseqQC . Reads wTACseqQC  to removATACseqQC package. First, we determined the fragment size distributions of the filtered BAM files. Given that size distributions of libraries from the same studies were more similar than those from different studies, further quality control steps were performed only for the representative sequencing libraries from each study using reads aligned to human chromosomes 1 and 2 unless otherwise stated. Coordinates of read alignments were shifted as described [Post-alignment quality of the ATAC-seq data was assessed by using our escribed . Based oescribed . For ploloess.smooth function from the stats package was used to generate a smoothed saturation curve.Sequencing depth analysis was performed for a high quality ATAC-seq dataset (SRR891270). MACS2 was used to call broad peaks for a series of subsamples  of the filtered BAM file and the full dataset. The number of significant peaks as well as the total width of significant peaks (FDR ≤ 0.05) from each subsample and the full dataset was plotted against the size of the corresponding subsample or the full dataset. The Library complexities were evaluated for five ATAC-seq datasets (three 50 K–cell replicates: SRR891269-SRR891271 and two 500-cell replicates: SRR891272 and SRR891274), using the BAM files with mitochondria-derived reads removed.ATACseqQC.To assess the effect of sequencing depth on diagnostic plots, the BAM file from a high-quality dataset (SRA run accession SRR891270)  was subsScripts used for the case studies are available in Additional file ATACseqQC. Detailed functional comparisons between ATACseqQC and existing tools are listed in Additional file To help researchers quickly assess the quality of their ATAC-seq datasets, we have implemented a comprehensive set of functionalities in bamQC function in the ATACseqQC package or external tools, such as SAMtools [For bioinformatics analysis of ATAC-seq data, quality of reads per library is assessed using FASTQC. If raw reads pass FASTQC quality control, then they can be aligned to a reference genome of choice using BWA-mem , Bowtie SAMtools , to remoPer base quality assessment results from FASTQC, and summary statistics of read mapping and filtering using BWA-mem  and SAMtATAC-seq leverages the hyperactive Tn5 transposase, preloaded with sequencing adaptors, to simultaneously fragment transposase-accessible DNA and tag the fragmented DNA with the sequencing adaptors, a process called tagmentation. Tn5 transposase preferentially inserts sequencing adaptors into chromatin regions of higher accessibility. Notably, besides in vivo chromatin states, frequency of Tn5 transposition also depends on DNA sequence  and tranfragSizeDist , which represent nucleosome-free regions. In addition, the Tn5 transposase inserts sequencing adaptors into the linker DNA between neighboring nucleosomes. The remaining reads from larger fragments therefore come from nucleosome-bound but open chromatin regions. The insert size distribution of all the fragments should show an obvious downward laddering pattern reflecting the amount and length of DNA fragments from nucleosome-free regions, and those associated with one to several nucleosomes. The size distributions of filtered sequence fragments for all 25 samples from the four studies are plotted using 00 bp, whATACseqQC package, the function shiftGAlignmentsList can be used to shift the chromosomal location of the aligned reads. By default, 5′ of the reads aligned to the positive and negative strands are offset by + 4 bp and − 5 bp, respectively. Additionally, prior to drawing several other diagnostic plots, reads need to be separated into different bins based on their inferred in vivo chromatin origins, i.e., nucleosome-free and oligo-nucleosome-bound such as mono-, di-, and tri-nucleosome regions, using the function splitGAlignmentsByCut.The Tn5 transposase has been shown to function as a dimer and inserts the two sequencing adaptors into target regions separated by 9 bp . For dowPromoter regions of active genes are in an open chromatin state. Ideally, ATAC-seq fragments shorter than 100 bp  should cluster immediately upstream of TSSs. By contrast, fragments corresponding to mono-, di- or tri-nucleosomes should be depleted from TSSs of active promoters throughout the genome, but display periodic peaks of read density immediately upstream and downstream of those TSSs. Signals around TSSs from nucleosome-free fragments and from oligo-nucleosome-occupied fragments are shown as side-by-side heatmaps and the average read coverage plots for four representative ATAC-seq libraries from 4 different studies Fig.  and l. TIGVSnapshot to allow streamlined visualization of ATAC-seq results at genomic regions of interest. Housekeeping genes are known to be expressed across many tissue types [.While the heatmaps and histogram plots provide a genome-wide overview of aggregated signals around TSSs, read distribution along specific genomic regions, such as those containing actively transcribed genes and their flanking regions, can give an more intuitive and detailed view of the quality of ATAC-seq data. Therefore, we have developed the function ue types . TherefofactorFootprints function can be used to generate footprint plots. It first uses the matchPWM function in the Bsgenome package to predict the binding sites with an input position frequency matrix (PFM) for a DNA-binding protein. Next, it calculates and plots the average cutting rates for those binding sites and 100-bp flanking sequences. Footprints of a DNA-binding protein, CTCF, for the four representative ATAC-seq libraries are shown in Fig. ....In open chromatin regions, DNA stably bound by DNA-binding proteins, such as transcription factors (TFs), can be protected from Tn5-mediated insertion of sequencing adaptors, while the flanking open regions are not. As a result, these protein-bound regions will be depleted of signal from adaptor insertions and are referred to as “footprints.” Thus, the existence of a “footprint” suggests the presence of a DNA-binding protein at that site. By plotting the aggregated signals from short-reads (< 100 bp) along predicted binding sites for DNA-binding proteins, we expect to observe “footprints” at known binding motifs. The . estimateLibComplexity, along with two additional biological replicates containing 50 K cells and two libraries from 500 cells. As shown in Fig.The results from both peak number- and peak width-based saturation analysis suggest that ATAC-seq library SRR89127 was not sequenced to a saturated depth, although the rate of increase in the total number or width of peaks decreases slightly after 10 million fragments passing filtering criteria Fig. . To dete..To determine whether sequencing depth affects the patterns in various diagnostic plots, we randomly subsampled the BAM files from a successful experiment (SRR891270). The results show that the fragment size distribution and the aggregated signals around TSSs from the subsamples with as low as 2.6 million uniquely mapped reads Additional file 2:Table S1. Functional comparison between ATACseqQC and existing tools for ATAC-seq data QC analyses. (XLSX 14 kb)Additional file 3:Table S2. Summary statistics of base quality of raw reads, mapping and filtering of read alignments. The four datasets chosen for detailed quality control are highlighted in bold. (XLSX 21 kb)Additional file 4:Figure S1. Size distributions of sequenced fragments passing filtering criteria. NCBI SRA accession numbers for each ATACT-seq dataset are listed above each sub-fig. (A-I) are based on a study by Buenrostro et al. 2013; (J-O) are based on a study by Wijetunga et al. 2017; (P-S) are based on a study by Conrad et al. 2017; (T-Y) are based on an unpublished study by Vallés AJ and Izquierdo-Bouldstridge A. (PDF 572 kb)Additional file 5:Figure S2. Read distribution along genomic regions containing housekeeping genes. (A) ACTB; (B) VCP; (C) REEP5; (D) RAB7A; and (E) VPS29. (PDF 289 kb)Additional file 6:Figure S3. Diagnostic plots for subsampled datasets. Figs. A-D, E-H, I-L and M-P are based on 10%, 25%, 50% and 75% of randomly sampled reads from the post-filtered BAM file for dataset SRR891270.  fragment size distributions;  Heatmaps showing signals around TSSs;  distributions of averaged coverage;  aggregated CTCF footprints. (PDF 1197 kb)


PGF in human trophoblast cell lines. While both factors independently have a positive effect on PGF gene expression, when combined, DLX3 acts as an antagonist to GCM. Despite this understanding, potential mechanisms accounting for this regulatory interaction remain unexplored. We identify physical and functional interactions between specific domains of DLX3 and GCM1 in human trophoblast-derived cells by performing immunoprecipitation and mammalian one hybrid assays. Studies revealed that DLX3 binding reduced the transcriptional activity of GCM1, providing a mechanistic explanation of their functional antagonism in regulating PGF promoter activity. The DLX3 homeodomain (HD) was essential for DLX3-GCM1 interaction, and that the HD together with the DLX3 amino- or carboxyl-terminal domains was required for maximal inhibition of GCM1. Interestingly, a naturally occurring DLX3 mutant that disrupts the carboxyl-terminal domain leading to tricho-dento-osseous syndrome in humans displayed activities indistinguishable from wild type DLX3 in this system. Collectively, our studies demonstrate that DLX3 physically interacts with GCM1 and inhibits its transactivation activity, suggesting that DLX3 and GCM1 may form a complex to functionally regulate placental cell function through modulation of target gene expression.The placental transcription factors Distal-less 3 (DLX3) and Glial cell missing-1 (GCM1) have been shown to coordinate the specific regulation of  Coordinated with the maturation of placental vasculature, placental trophoblast lineages progressively expand, differentiate, migrate toward and invade the maternal uterus, and thus drive placental morphogenesis4. Aberrant placental vasculature has frequently been found to occur coordinately with trophoblast defects in placentae of an array of pregnancy disorders, including the most common and clinically significant complications of preeclampsia (PE) and intrauterine growth restriction (IUGR)8. A wealth of data supports observations that placental cytokines and transcription factors play important roles in stimulating and modulating trophoblast development, placental angiogenesis, and vascular remodeling across placentation12; many of which have been found to be dysregulated in PE- and IUGR-complicated pregnancies and have been implicated in disease pathogenesis15.Establishment of the maternal-fetal interface during human pregnancy is characterized by extensive placental angiogenesis and uterine vascular remodeling to increase placental perfusion to meet the growing metabolic demands of the conceptus12. Distal-less 3 (DLX3) is among these factors. DLX3 is a homeodomain-containing protein16 that was initially described as a transcription factor involved in epidermal, osteogenic, and hair follicle differentiation during embryonic development20. Consistent with these findings, naturally occurring mutations of the DLX3 gene in humans have been associated with ectodermal dysplasia featured by birth defects in hair, teeth and craniofacial bones, as seen in the tricho-dento-osseous syndrome (TDO) and other related developmental diseases24. Additionally, the potentially causal link between epidermal loss of DLX3 expression and human skin cancer25, and the finding of DLX3 downregulation in B cells with the MLL-AF4 translocation in acute lymphoblastic leukemia in children26 suggest normal postnatal expression of DLX3 in certain tissue and cell types is also required.A number of placental-specific transcription factors crucial for normal placental development have been described based upon genetically modified mouse models, which in many instances share similar molecular mechanisms with the human placentaDlx3-null mice are embryonic lethal due to placental failure featured by malformation of the labyrinth compartment and an abnormally formed fetal and maternal vascular network27. Further, Dlx3 haploinsufficient mice display disrupted placental vasculature development and altered fetal growth trajectory28. Our previous work revealed the Dlx3-dependent transcriptome in the mouse placenta and identified multiple pathways in the DLX3-dependent gene regulatory network important for placental development and functional maintenance in human trophoblast-derived cells, including transcriptional regulation of the glycoprotein hormone alpha subunit (CGA) a subunit of human chorionic gonadotropin, matrix metalloproteinase 9 (MMP9) and PGF32. CGA is one of two glycoprotein hormone subunits making up human chorionic gonadotropin, a crucial luteotropic signal produced by trophoblasts in the developing conceptus early in pregnancy to signal maternal recognition of pregnancy. Identifying the specific cis-regulatory element within the human CGA gene promoter was the initial study linking Dlx3 to the endocrine function of placental trophoblasts. PGF, a proangiogenic cytokine primarily produced by trophoblast cells, plays dual roles in stimulating placental angiogenesis and trophoblast differentiation36. Moreover, PGF has been implicated to support maternal endothelial system integrity as a free peptide in the maternal circulation39. Downregulation of placental-derived PGF has been shown to be a predictive biomarker and a potential therapeutic target of PE39. We and others have previously demonstrated that PGF expression in human trophoblast-derived cells requires both transcription factors of DLX3 and Glial cell missing (GCM)140. Correlated with this finding, expression patterns of DLX3, GCM1 and PGF overlap remarkably within human placental tissues examined at term; most abundantly in villous cytotrophoblasts (CTB), syncytiotrophoblasts (STB) and extravillous trophoblasts (EVT) in the proximal regions of CTB columns44. Expression of DLX3 and PGF was additionally observed in endothelial cells surrounding fetal capillaries, highlighting the potential involvement of DLX3 in fetoplacental angiogenesis44. Importantly, decreased levels of DLX3 and GCM1 have been reported in placentae complicated with PE, in which PGF is dysregulated46. Interestingly, our earlier study in human trophoblast-derived cell lines revealed that while DLX3 or GCM1 in isolation were positive regulators of PGF gene regulation, their combined impact resulted in a regulatory antagonism in PGF expression32.The involvement of DLX3 in murine placental development is well described. 47. GCM1 expression is restricted in the kidney and the thymus in adult mammals and in a subset of trophoblast lineages within the placenta49. GCM1 plays a pivotal role in placental development by regulating the expression of several essential genes in trophoblasts cells and is necessary for proper differentiation and functions of trophoblast lineages50. For example, GCM1 controls the expression of mouse syncytins and the human homolog ERVFRD-1, which encode fusogenic proteins which induce CTBs to fuse into multinucleated STBs, and hence the formation of the most essential functional structures of murine and human placentae—the murine labyrinth and human syncytial trophoblast layer52. GCM1 also promotes the differentiation and invasiveness of EVTs by upregulating the High-Temperature Requirement Protein A4 to facilitate extracellular matrix remodeling53. Notably, decreased GCM1 expression was correlated with defects in both extravillous and villous trophoblast lineages observed in PE-complicated placentae54. In mice, Gcm1 ablation leads to a phenotype similar to that of Dlx3 null mice, with mid-gestational embryonic death characterized by reduced branching of the chorioallantoic interface and the absence of STB cell lineage leading to placental malformation56. Heterozygous Gcm1 mutants are fertile, but exhibit deficient trophoblast differentiation, abnormal fetoplacental vascularity, and increased syncytial necrosis57. Interestingly, wild-type dams carrying Gcm1 heterozygous fetuses developed late gestational hypertension—one of the hallmarks of PE in humans57.GCM1 is a zinc finger transcription factor with a conserved DNA-binding motifPGF promoter32. Intriguingly, the combination of DLX3 and GCM1 formed a functional antagonism in regulating PGF expression32. Based on these observations, we speculated that a physical interaction between DLX3 and GCM1 is likely to functionally coordinate PGF regulation. If true, this observation would further our understanding of complex molecular and genetic networks controlling human placental cell function, which may have enormous implications in human pregnancy-associated conditions and numerous adult diseases with origins in early prenatal environment59. The goal of the current study was to identify the physical and functional interactions between DLX3 and GCM1 in human trophoblast-derived cells. Our studies provide important mechanistic underpinnings of how a DLX3-GCM1 antagonism influences PGF regulation32.DLX3 and GCM1 share expression domains and functional importance within murine and human placentae. This was highlighted by our previous study where we identified both factors co-localized on the PGF promoter in human trophoblast-derived cells32; in those studies, both transcription factors were localized by chromatin-immunoprecipitation assay to a novel cis-acting regulatory element on the PGF promoter. In the current studies, we hypothesized that DLX3 and GCM1 might physically associate to form a complex to regulate PGF expression. Initial pilot studies were carried out to detect such an interaction between endogenous DLX3 and GCM1 in the JEG-3 human choriocarcinoma cell line which constitutively expresses both transcription factors. However, these initial studies were hampered by high relative rates of proteosomal degradation (data not shown). As an alternative, JEG-3 cells were transfected with a GCM1 expression plasmid (pHA-GCM1) expressing the hemagglutinin (HA) epitope-tagged GCM1, and treated with the proteasome inhibitor MG-132 before collection. Cell lysates were subjected to IP using the anti-DLX3 antibody and the IPs were analyzed by immunoblotting with the anti-HA antibody. With this approach, HA-GCM1 was detected in the DLX3 IP, indicating physical interactions between overexpressed HA-GCM1 and endogenous DLX3  binding and a minimal adenoviral promoter upstream of the firefly luciferase gene was used to assess the transcriptional activity of GAL4-DBD GCM1 in JEG-3 and HTR-8/SVneo cells overexpressing DLX3. As is shown in Fig. 47. Consistent with our previous studies32, DLX3 overexpression significantly reduced the luciferase activity induced by GAL4-DBD GCM1 in a dose-dependent manner in both JEG-3 and HTR-8/SVneo cells, indicating that the binding of DLX3 with GCM1 in this context inhibited the transactivation-stimulating activity of GCM1. Importantly, overexpression of DLX3 in the absence of Gal4-GCM1 was without effect on this system. Western blot analyses using the HA epitope characterized DLX3 expression levels in this system for JEG3  of the DLX3-GCM1 interaction on the isolated transcriptional activity of GCM1 in JEG3 and HTR-8/SVneo cell lines Fig. . HTR-8/SEG3 Fig.  with simEG3 Fig. . Thus, D16 which formed the framework of our five truncation mutants of DLX3  of DLX3 mediating the DLX3-GCM1 physical and functional interactions. The DNA-binding HD and transcriptional activation domains of DLX3 have been describedLX3 Fig. . As a prLX3 Fig. . SimilarLX3 Fig. .Figure 2WT) , (ii) the amino domain-truncated DLX3 mutant (DLX3∆ND) and (iii) the DLX3 HD domain mutant (DLX3HD), all of which contain the DLX3 HD domain  which lack the HD domain were unable to interact with GCM1 in isolation  Fig. . Using tain Fig. . In contion Fig. . These b∆ND and DLX3∆CD led to significant inhibition in GAL4-DBD GCM1-induced luciferase activity in a manner consistent with DLX3WT; whereas DLX3ND and DLX3CD overexpression had no effect on the transactivation activity of GAL4-DBD GCM1 in both JEG-3 and HTR-8/SVneo cells  both the DLX3 ND and CD contain transactivation activities of DLX3; (ii) unlike the Xenopus and murine Dlx3 where both ND and CD are simultaneously required60, for human DLX3, either the ND or CD in conjunction with the HD is sufficient to confer the transactivation activity of DLX3 in the CGA reporter system.Previous studies have described the DNA binding domain, transactivation domains and nuclear localization signal sequence of lx3 Fig. . CGA is tor Fig. . HoweverCGA gene promoter, transactivation activities of DLX3 structural mutants were also assessed in the PGF luciferase reporter system. Consistent with the CGA luciferase reporter system, overexpression of DLX3∆CD and DLX3∆ND significantly upregulated the PGF promoter activity at a level higher than that induced by DLX3WT, whereas DLX3CD, DLX3ND and DLX3HD didn’t possess any transactivation activities in JEG-3 and HTR-8/SVneo cells . This mutation is predicted to result in a frameshift in the coding region and thereby a truncated translation product with altered carboxyl terminus21. Little is known regarding the activities of DLX3TDO compared to DLX3WT at the molecular level. We speculated that DLX3TDO could potentially behave similar to DLX3WT in our specific promoter analyses since the HD and ND remain intact in the truncated protein. Alternatively, the truncated DLX3TDO protein could be characterized as misfolded due to the truncation leading to an inactive protein. To parse out these alternatives, we assessed the transcriptional activity of DLX3TDO comparing it directly to DLX3WT in regulating placental specific genes in human trophoblast-derived cells by performing the luciferase reporter assays using the CGA  and further suggest that the mechanisms leading to the TDO phenotype in humans are clearly separable from the impact of DLX3 transactivation potential of genes like CGA and PGF and the functional antagonism between DLX3 and GCM1.Naturally occurring mutations in DLX3 have been identified in human populations with TDO syndrome and other ectodermal diseasesCGA Fig.  and PGF PGF Fig.  promoterope Fig.  revealinays Fig. . These s61. Importantly, increasing evidence supports the observation that an adverse prenatal environment in early life linked to a dysfunctional placenta could program the conceptus toward increased risk of a number of chronic diseases developed in its adult life59. Thus it is highly desirable to obtain a deeper understanding of the cellular and molecular mechanism underlying placental cell function and maintenance, which would provide invaluable insights into novel preventative and therapeutic strategies with potential for lifelong impact61. Here we identified the physical and functional interactions between two important placental transcription factors—DLX3 and GCM1 in human trophoblast-derived cells. Our studies revealed that the DLX3 HD together with either the ND or CD is required for the DLX3-GCM1 interactions. Focusing on DLX3, we further defined the link between structural domains and transcriptional activity of this protein, and demonstrated that both ND and CD retain significant and independent transactivation activities. Our studies implicate the convergence of a DLX3- and GCM1-dependent gene regulatory network at the transcriptional level in human trophoblast-derived cells, which is likely to extend our understanding of molecular mechanisms controlling placental development and functional activities under normal and pathophysiological conditions. The caveat to the present studies is the limitation of trophoblast cell lines and future efforts should include analyses in primary human trophoblast cells and human placentas.Placental dysfunction leads to pregnancy disorders including gestational diabetes, maternal gestational hypertension, PE and eclampsia, IUGR, abnormal neurodevelopment, preterm birth and stillbirth64. We and others have proposed a role of HD proteins in placental vascular development, represented by the HD family member DLX3, as targeted deletion of Dlx3 results in mid-gestation fetal death due to placental vascularization abnormalities in mice27. In our earlier effort to define gene targets and molecular mechanism underlying the regulatory activities of DLX3, we identified PGF as one of the direct gene targets of DLX3 in the mouse placenta and human trophoblast-derived cells, and therefore proposed a mechanism by which DLX3 engages in placental angiogenesis and trophoblast differentiation and survival, where PGF plays a pivotal role32. In the present study, we provide further evidence for a novel role of DLX3 as the inhibitory modulator of transcriptional activity of an important determinant of placental function, GCM1. While our studies using the in vitro human trophoblast cell model have clear limitations, findings reported here may have important implications in the in vivo physiological conditions within the human placenta. DLX3 has been previously showed to be a modest regulator of PGF compared with GCM1, which strongly induces PGF gene transcription32. While speculative, by binding with GCM1 and inhibiting the transactivation-stimulating activity in a dose-dependent manner, DLX3 may serve as a “molecular brake” to fine-tune GCM1-dominated PGF expression, and therefore potentially control placental vascular expansion and trophoblast overgrowth.Homeodomain-containing proteins fulfill a wide spectrum of biological functions as regulators of gene expression throughout embryonic development and postnatally in axon guidance and tissue patterning, migration, regulation of plasticity and differentiation65. However, unlike in the latter cases where the DLX3-MsxI and DLX3-Smad6 associations interfere with the binding of the DLX3 HD to DNA and thus lead to loss of DLX3’s transcriptional activity65, the DLX3-GCM1 interactions appear to have no effect on DLX3’s transcriptional activity32. Conversely, we speculate that the interaction with GCM1 might facilitate the association and binding specificity of DLX3 on the PGF promoter. The HD domain is known for promiscuous DNA binding with a general preference for motifs centered with the TAAT sequence66. Many HD proteins act in conjunctions with their binding partners to impart their DNA binding affinity and specificity67. It is likely that interactions between DLX3 and its binding partners including GCM1 serve as a means to distinguish specific gene targets from other TAAT-rich sequences in the genome, depending on cellular contexts and physiological states. This is supported by the lack of DLX3 consensus binding sites on the PGF promoter region where both DLX3 and GCM1 localize in the proximal portion of the PGF promoter32. Notably, different binding partners would lead to variable regulatory outcomes in combination with DLX3. For example, DLX3 and ETS2 have been reported to synergistically transactivate the bovine Interferon-Tau and the CGA promoters in human trophoblast-derived cell lines in vitro69; and here we demonstrate that DLX3 and GCM1 antagonistically regulate PGF expression in human trophoblast-derived cells. Thus, we postulate that the DLX3-GCM1 interactions might affect specific functional activities of both factors from different aspects: while DLX3 inhibits GCM1’s transcriptional activity, GCM1 might facilitate the specificity of DLX3 binding on the PGF promoter.Our studies revealed the central role of the DLX3 HD domain in mediating the DLX3-GCM1 physical and functional interactions. This is not surprising considering the HD domain also mediates the DLX3-MsxI and DLX3-Smad6 interactions70. In addition, our study here indicated that the transactivation domains of human DLX3 reside in both ND and CD domains, consistent with the findings in the murine and Xenopus. However, unlike the murine and Xenopus Dlx3 which require the presence of both the ND and CD to induce Dlx3-dependent transactivation, we showed that either one of the human DLX3 ND or CD in conjunction with the HD DNA binding activity was sufficient to confer DLX3’s transactivation activity in upregulating promoter activities of CGA and PGF. Intriguingly, deletion of either the ND or CD resulted in increased transcriptional activities of DLX3 mutants, possibly due to potential conformation changes in the folding of the truncated proteins, or loss of post-translational modification sites conferring transactivation inhibition of DLX3. The DLX3TDO mutant included in our study displayed similar transcriptional activity and functional interactions with GCM1 compared with wild-type DLX3, suggesting the DLX3 CD domain is dispensable for DLX3-dependent upregulation of CGA and PGF, and further is potentially dispensable for DLX3’s functional activities in the placenta. It is presently unclear if pregnancy disorders associated with malformation of the placenta occurs in the TDO-affected population. Our study suggests that the DLX3 CD is likely to be involved in tissue-specific signal interpretation that contribute to highly precise biological functions of DLX3 in different tissues and cellular contexts.The DLX3 ND and CD domains appeared to be important for full inhibition of DLX3 on GCM1’s activity, suggesting that both domains might be involved in the DLX3-GCM1 interactions, and possibly mediate interactions between DLX3 and other binding partners. This is consistent with previous findings in Homeobox (HOX) genes—a subfamily of HD proteins, where regions outside of the HD domain could play multiple roles including regulating interactions between HOX and specific binding partners, providing additional specificity to gene targets of HOX, and helping to interpret input through signaling cascadesPGF regulation by the combination of DLX3 and GCM1. The DLX3 HD domain mediates the physical and functional interactions between DLX3 and GCM1, whereas the ND and CD domains appeared to further facilitate the interactions. We characterized that both the DLX3 ND and CD conferred the transactivation activity of DLX3, correlated with their potential involvement in cofactor binding and signaling interpretation. Our study helps to define the inter-play between two important placental transcription factors and characterizes a link between structural domains and transcriptional activity of human DLX3 in human trophoblast-derived cells, therefore providing potentially important insights into DLX3- and GCM1-related gene regulatory network underlying placental cell function. Based on previously reported involvement of DLX3 and GCM1 in the pathophysiology of PE-complicated placentae in human patients54, our in vitro study may also implicate novel perspectives in targeting molecular pathways contributing to maternal disease associated with PE.In conclusion, we have uncovered novel roles of DLX3 as a binding partner of GCM1 and a negative regulator of GCM1’s transactivation-stimulating activity in human trophoblast-derived cells, thus providing a mechanistic explanation of the antagonistic regulatory effect on CGA promoter luciferase reporter and the PGF promoter luciferase reporter plasmids are described in previous studies32. HA-tagged GCM1 was generously provided by Dr. Hungwen Chen . Series of structural domains of DLX3 were constructed by polymerase chain reaction (PCR) using pKH3-DLX3 as the template and the following primer sets: DLX3ND (DLX31-128), forward 5′-CGGGAATTCAAATGAGCGG-3′ and reverse 5′-ATCGATTCACTTCTTGGGCTTCCCATTCAC-3′; DLX3∆CD (DLX31-192), forward 5′-CCGGGAATTCAAATGAGCGG and reverse 5′-ATCGATTCACACCTCCCCGTTCTTGTAGAG-3′; DLX3∆ND (DLX3125-287), forward 5′-GAATTCAAAAGCCCAAGAAGGTCCGAAAG-3′ and reverse 5′-GGCCATCGATTCAGTACACA-3′; DLX3CD (DLX3194-287), forward 5′-GAATTCAACCGCTGGAGCACAGTCCCAATA-3′ and reverse 5′-GGCCATCGATTCAGTACACA-3′; DLX3HD (DLX3125-194), forward 5′-GAATTCAAAAGCCCAAGAAGGTCCGAAAG-3′ and reverse 5′-ATCGATTCACACCTCCCCGTTCTTGTAGAG-3′. To construct the TDO mutant coding sequence, the pKH3-DLX3 was used as the template and three rounds of PCR were carried out with the following primer sets: first round, forward 5′-CCGGGAATTCAAATGAGCGG-3′ and reverse 5′-AGCGGCACCTGTTCTTGTAGAGTTTCTTGA-3′; second round, forward 5′-CTACAAGAACAGGTGCCGCTGGAGCACAGT-3′ and reverse 5′-GGCCATCGATTCAGTACACA-3′; third round, forward 5′-CCGGGAATTCAAATGAGCGG-3′ and reverse 5′-GGCCATCGATTCAGTACACA-3′. EcoRI and ClaI restriction sites were incorporated into 5′ and 3′ primers respectively to facilitate cloning. All sequences were verified by nucleotide sequence analyses and incorporated into the pHK3 vector to generate HA-tagged DLX3 domain expressing constructs. Antibodies used in this study included: rabbit anti-DLX3 , goat anti-GCM1 , mouse anti-HA , nonspecific rabbit IgG isotype control (ChIP Grade) , nonspecific goat IgG isotype control , HRP-conjugated VeriBlot for IP secondary antibody . The proteasome inhibitor MG-132  was used with a final concentration of 10 μM in culture media.PKH3-DLX3, the 71 was purchased from American Type Culture Collection (30-2003). HTR-8/SVneo is the immortalized extravillous trophoblast cell line derived from human first trimester placenta72 and was generously provided by Dr. Charles Graham . JEG-3 and HTR-8/SVneo were cultured in Dulbeco’s Modified Eagle Medium  supplemented with 10% (v/v) fetal bovine serum (FBS) (Sigma-Aldrich) and RPMI 1640 Medium  supplemented with 5% (v/v) FBS respectively. Media were also supplemented with 100 units/mL penicillin G, and 100 μg/ml streptomycin (Invitrogen). Cells were incubated in a humidified 5% CO2 atmosphere at 37 °C.The human choriocarcinoma cell line JEG-3Transient transfection was performed in dishes of cells with 60~80% confluency using the polyethylenimine reagent  per manufacturer’s protocol. Corresponding empty vectors of expression plasmids were used as substitutions to make equal amounts of total DNA transfected across dishes. Eight hours after transfection, cells were washed with phosphate-buffered saline (PBS), and supplemented with fresh serum-containing media. Cells incubated for 48 h were collected and lysed in luciferase buffer . Luciferase activity was quantified using the LB9501 luminometer  and standardized by total cell protein content as determined by Bradford assay.JEG-3 cells were transfected with pHA-GCM1 for 48 h followed by MG-132 treatment for 6 h. Cells were then collected in lysis buffer  freshly supplemented with complete protease inhibitor cocktail  by rotation at 4 °C for 1 hr. For each IP, 500 μg whole cell lysate was incubated with 5 µg primary antibody at 4 °C for 1 h with rotation. Protein A Dynabeads  were then added into samples and incubated at 4 °C with rotation for an additional hour. IP- antibody-beads were collected using a magnet, washed twice with lysis buffer and once with TPBS , and eluted in 50 μl 2 × Laemmli buffer at 95 °C for 5 min. IPs were subsequently resolved by SDS-PAGE and subjected to Western blot analyses.Cell lysates or IPs were resolved on SDS-polyacrylamide gels and transferred to polyvinylidene difluoride (PVDF) membrane . Membrane was blocked with 5% milk-TBST and probed with the primary antibody , HRP-conjugated secondary antibody , and ECL reagents (Bio-Rad).EcoRI and Xba were added to the 5′ and 3′ end of GCM1 cDNA sequence respectively to facilitate cloning. The sequence was verified by nucleotide-sequencing analysis. To construct the GAL4 DBD-GCM1 fusion protein, GCM1 cDNA was incorporated in frame and downstream of the GAL4 DBD (residues 1-147) of the pSG424 plasmid73. JEG-3 or HTR-8/SVneo cells were seeded in 35 mm wells at a 60~70% confluency before transfection. Cells were transfected with 0.5 μg pSG424-GCM1 , 0.5 μg pGL4.31 luciferase reporter  containing five-repeats of the GAL4 binding site (5 × GAL4 binding sites) adjacent to the minimal TATA box of major late adenovirus promoter, and 0.5 μg or 1 μg pKH3-DLX3 or DLX3 domain expression plasmids for 48 h followed by MG-132 treatment for 6 h. Cells were then collected for luciferase assays.GCM1 cDNA was obtained by PCR using the pHA-GCM1 plasmid as the template and the following primers: forward 5′-AATCCATGGAACCTGACGACTTTGA-3′ and reverse 5′-TCTAGAGTCATCTCAAAGGACACAGGTT-3′. t tests were used to compare the means between DLX3WT and DLX3 functional domains, and DLX3WT and DLX3TDO. Two-way ANOVA followed by the post-hoc Bonferroni tests were used multiple comparisons were performed between GCM1 and DLX3WT, GCM1 and DLX3 functional domains and GCM1 and DLX3TDO in luciferase reporter and mammalian one hybrid assays. A P value of less than 0.05 was considered to be statistically significant.Data are presented as mean ± standard error (SEM) from an individual representative of three independent experiments. One-way analysis of variance (ANOVA) followed by Student’s unpaired Supplemental Data


Chromatin remodeling and histone modifying enzymes play a critical role in shaping the regulatory output of a cell. Although much is known about these classes of proteins, identifying the mechanisms by which they coordinate gene expression programs remains an exciting topic of investigation. One factor that may contribute to the targeting and activity of chromatin regulators is local chromatin landscape. We leveraged genomic approaches and publically-available datasets to characterize the chromatin landscape at targets of the human INO80 chromatin remodeling complex (INO80-C). Our data revealed two classes of INO80-C targets with distinct chromatin signatures. The predominant INO80-C class was enriched for open chromatin, H3K27ac, and representative subunits from each of the three INO80-C modules . We named this class Canonical INO80. Notably, we identified an unexpected class of INO80-C targets that contained only the INO80 ATPase and harbored a repressive chromatin signature characterized by inaccessible chromatin, H3K27me3, and the methyltransferase EZH2. We named this class Non-Canonical INO80 (NC-INO80). Biochemical approaches indicated that INO80-C and the H3K27 acetyltransferase P300 physically interact, suggesting INO80-C and P300 may jointly coordinate chromatin accessibility at Canonical INO80 sites. No interaction was detected between INO80-C and EZH2, indicating INO80-C and EZH2 may engage in a separate form of regulatory crosstalk at NC-INO80 targets. Our data indicate that INO80-C is more compositionally heterogenous at its genomic targets than anticipated. Moreover, our data suggest there is an important link between INO80-C and histone modifying enzymes that may have consequences in developmental and pathological contexts. ATP-dependent chromatin remodeling complexes (remodelers) comprise a class of highly conserved protein complexes that mediate nucleosome position throughout the genome . Each rein vitro are well understood. Originally identified in a genetic screen for S. cerevisiae mutants sensitive to inositol . Details regarding the structural and biochemical assembly of INO80-C in vitro  supplemented with 10% Fetal Bovine Serum and penicillin/streptomycin (100 units/mL).BAF53A: LifeSpan Biosciences LS-C196606 (Western). EZH2: Millipore 07-400 . INO80: Abcam ab105451 . MCRS1: Proteintech 11362-1-AP (ChIP). RUVBL1: Bethyl A304-716A (ChIP) and Abcam ab51500 (Western). RUVBL2: Bethyl A302-537A (ChIP) and Abcam ab89942 (Western). SUZ12: Cell Signaling 3737S (Western). YY1: Santa Cruz sc-1703 (Western).HepG2 cells were fixed using 0.3% formaldehyde in 1X PBS for 30 min at 4°. Fixative was quenched with 1/10 volume of 2M glycine for 5 min at room temperature. Cells were pelleted at 2000 rpm for 10 min at 4°, then washed 3 times by resuspension in 50mLs of ice cold 1X PBS and centrifugation at 2000 rpm for 10 min at 4°. Cells were aliquoted at 20 million cells and snap frozen using liquid nitrogen.ChIP was performed as previously described  plus protease inhibitors and placed on ice for 10 min to swell. Cells were then centrifuged at 1300 rpm for 10 min at 4°. Cells were dounced with 2 packed cell volumes of hypotonic cell lysis buffer. Nuclei were pelleted at 1300 rpm for 10min at 4°. Nuclei were washed with 10 packed cell volumes with hypotonic cell lysis buffer and centrifuged at 5000 rpm for 10 min. Extractions were performed with 60μL of nuclear lysis buffer  per 100μL cell pellet. Lysates were clarified at 14,000 rpm for 10 min at 4° between extractions. Lysates were diluted with storage buffer  to bring final KCl concentration to 150mM.Prior to beginning the IP, we washed Dynal Protein A beads 3 times with PBS + 0.5% BSA at 4°. We resuspended beads in 400μL of 1X + 0.5% BSA, then added 5ug of antibody and incubated overnight at 4°. The following day, we thawed the nuclear lysates on ice. Lysates were added to antibody-conjugated beads and incubated overnight. Beads were washed 7 times for 5 min each, in this order: 2X with IP Buffer , 3X with Wash Buffer , and 2X Final Buffer . Proteins were eluted from beads using 2X Laemmli buffer with 1:10 DTT for 10 min at 95C. Beads were magnetized and supernatants stored at -20°.Density sedimentation was performed as previously described . BrieflyReads were aligned to hg19 using bowtie2  using thPeaks were called using Macs2 . Given the role of INO80 as a scaffold for its biochemical partners, we hypothesized that the NC-INO80 sites represented a meaningful unidentified class of INO80 targeting.We were surprised to find that 22% of INO80 ATPase peaks did not overlap RUVBL1, RUVBL2, MCRS1, or YY1 . We sepaBiochemical evidence suggests the remodeling activity of INO80-C is most efficient when its subunit modules are intact. Therefore we measured chromatin accessibility at Canonical INO80 and NC-INO80 sites using ENCODE DNase-seq data in HepG2. Canonical INO80 sites displayed high signal by DNase-seq . BecauseTo identify distinguishing chromatin features that influence INO80 targeting, we intersected our ChIP-seq datasets with chromatin state predictions from ENCODE in HepG2 (ChromHMM . More thAdditionally, we used our previous RNA-seq data in HepG2 cells . These dWe next performed a series of biochemical experiments to determine if INO80-C interacted with histone modifiers of the chromatin landscape from Canonical INO80 and NC-INO80 classes. First, we performed low-stringency co-immunoprecipitations to determine if INO80-C members interacted with PRC2. Although we detected robust interactions between members of the same complex, we did not detect any interaction between the INO80 ATPase and EZH2, or other members from opposite complexes .Surprisingly, we observed interactions between members of INO80-C and P300 by co-immunoprecipitation under the same conditions . P300 isOur data suggest that two remarkably distinct classes of INO80-C targeting exist in human cells . A classOur discovery that NC-INO80 correlates with inaccessible chromatin suggests that the INO80 ATPase binds widely across the genome, and that the appropriate chromatin context may determine the ability of INO80-C to assemble and remodel chromatin. This is consistent with a scaffolding function for the INO80 protein and its requirement for interaction with its accessory subunits to mobilize nucleosomes (et al. recently proposed that in vivo assembly of INO80-C occurs in a stepwise fashion, which may explain the differences in subunit localization and chromatin features at Canonical INO80 and NC-INO80 targets (Zhou  targets . The autin vivo, one alternative explanation could be that INO80-C forms an array of complexes and subcomplexes throughout the genome, with each comprised of distinct sets of proteins that facilitate specific functions. Similar remodeling complexes to INO80-C, such as SWI/SNF, have been shown to exhibit striking compositional plasticity (Aside from a model for stepwise assembly of INO80-C asticity . For SWIasticity . For INOet al. recently demonstrated that the INO80 ATPase forms a complex with transcriptional modifiers MOT1 and NC2, termed MINC (For instance, Xue Interestingly, MINC also suppressed transcription at a set of targets that harbored active chromatin features (www.g3journal.org/lookup/suppl/doi:10.1534/g3.117.300504/-/DC1.Supplemental Material is available online at Click here for additional data file.Click here for additional data file.


Saccharomyces cerevisiae is widely studied for production of biofuels and biochemicals. To improve production efficiency under industrially relevant conditions, coordinated expression of multiple genes by manipulating promoter strengths is an efficient approach. It is known that gene expression is highly dependent on the practically used environmental conditions and is subject to dynamic changes. Therefore, investigating promoter activities of S. cerevisiae under different culture conditions in different time points, especially under stressful conditions is of great importance.S. cerevisiae under stressful conditions and in the presence of xylose were characterized using yeast enhanced green fluorescent protein (yEGFP) as a reporter. The stresses include toxic levels of acetic acid and furfural, and high temperature, which are related to fermentation of lignocellulosic hydrolysates. In addition to investigating eight native promoters, the synthetic hybrid promoter P3xC-TEF1 was also evaluated. The results revealed that PTDH3 and the synthetic promoter P3xC-TEF1 showed the highest strengths under almost all the conditions. Importantly, these two promoters also exhibited high stabilities throughout the cultivation. However, the strengths of PADH1 and PPGK1, which are generally regarded as ‘constitutive’ promoters, decreased significantly under certain conditions, suggesting that cautions should be taken to use such constitutive promoters to drive gene expression under stressful conditions. Interestingly, PHSP12 and PHSP26 were able to response to both high temperature and acetic acid stress. Moreover, PHSP12 also led to moderate yEGFP expression when xylose was used as the sole carbon source, indicating that this promoter could be used for inducing proper gene expression for xylose utilization.In this study, the activities of various promoters in S. cerevisiae throughout batch fermentation in the presence of inhibitors as well as using xylose. These results provide insights in selection of promoters to construct S. cerevisiae strains for efficient bioproduction under practical conditions. Our results also encouraged applications of synthetic promoters with high stability for yeast strain development.The results here revealed dynamic changes of promoter activities in The online version of this article (10.1186/s12934-018-0899-6) contains supplementary material, which is available to authorized users. Saccharomyces cerevisiae is commonly used for development of microbial cell factories to produce biofuels and biochemicals. Despite great progress in metabolic engineering and synthetic biology of S. cerevisiae, the bioconversion efficiency of the developed strains still requires further optimization, especially under practical application conditions [nditions . To imprnditions –3.S. cerevisiae have been characterized [Promoter is one of the most important genetic elements involved in the rational control and optimization of gene expression levels . Activitcterized –6. In adcterized –12. Withcterized , 13, 14.PTEF1) and glycolytic genes, such as 3-phosphoglycerate kinase (PPGK1), glyceraldehyde-3-phosphate dehydrogenase (PTDH3), and alcohol dehydrogenase (PADH1) are commonly utilized [TPS1, which encodes trehalose-6-phosphate synthase 1, to induce ethanol-responsive expression of FLO1 [PPGK1. Recently, HXT1 promoter, which provides both high-glucose induction and low-glucose repression, was also employed to control the expression of key genes for terpenoid synthesis [The commonly-used promoters can be divided into two main classes, namely, ‘constitutive’ and ‘inducible’ promoters , 15. ‘Coutilized . On the  of FLO1 . The resynthesis , 17, 18.S. cerevisiae have been characterized during the past decades [Abundant native promoters in  decades , 15, 19. decades , 20, 21. decades . Regulat decades –24. Nove decades . The synBesides generating promoter diversities as described above, it is also important to understand the responses of promoter strengths under different conditions. However, related study is still very limited. During the formation of specific products, yeast cells are often subjected to nasty conditions . For exaDue to the gradual depletion of fossil fuels and concerns on environmental protection, production of biofuels and biochemicals derived from renewable lignocellulosic biomass has aroused great research interests. Xylose is the second most abundant sugar in lignocellulosic hydrolysates , therefoDespite extensive studies in development of yeast strains for bioconversion of lignocellulosic biomass, effects of fermentation conditions on promoter activities remain unclear. In this study, the responses of nine promoters, including eight native constitutive promoters and one synthetic hybrid promoter, were investigated using yeast enhanced green fluorescent protein (yEGFP) as a reporter. Special concerns were focused on the responses of the strengths of these promoters under inhibitory conditions and in the presence of xylose. The data presented here provides basis for rational control of gene expression levels under practical fermentation conditions.S. cerevisiae strains, resulting in various reporter strains , or in the presence of acetic acid (3.6 g/L) or furfural (1.0 g/L), respectively. As shown in Fig. To investigate the impact of stressful conditions on the responses of promoters, P3xC-TEF1 and PTDH3 were always the two strongest promoters under the stressful conditions tested, albeit the strength of PTDH3 was slightly higher than P3xC-TEF1 , all these ‘inducible’ promoters showed similar dynamic pattern as that of the control (YPD100 without supplementation)  Fig. a, d. HowS. cerevisiae BY4741 was detected using xylose as the sole carbon source  [The responses of all the ‘constitutive’ promoters in the presence of xylose were subsequently compared in X03 Fig. . The prorce Fig. a. When cble Fig. a. When xEF1 Fig. b. Moderarce Fig. b. The reose Fig. b. Similaose Fig. c, d. Wheose Fig. . The stro − 627) , 34.Fig.PHSP26 and PADH2 exhibited similar strength profiles, but the strengths of PHSP26 showed higher variety than that of PADH2  [Generally, ‘constitutive promoters’ showed relatively higher promoter strengths toward various conditions compared to ‘inducible’ promoters. However, significant lower activities of d YPGal) . In combPHSP12 and PHSP26 exhibited higher strengths in the presence of xylose compared to those with glucose as the sole carbon source. It was reported that xylose concentration higher than 20 g/L may cause stress to the S. cerevisiae strains [PHSP12 and PHSP26 exhibited high transcription level as well as high ribosome occupancy at glucose starvation condition [PHSP12 and PHSP26 in the presence of xylose may be a combined consequence of glucose depletion and xylose stress, since these two promoters response to both glucose starvation and stressful conditions. Previously, two-stage transcriptional control of xylose assimilating pathway was achieved by employing PHSP26 to fine-tune the expression of TAL1 and XKS1, which resulted in elevated xylose consumption rate and ethanol yield from xylose compared to traditional design with constitutive promoter PPGK1 [PHSP12 to construct xylose-utilizing yeast. Our results revealed that PHSP12 can be used to develop xylose utilizing yeast strains. strains . In addiondition . Therefoer PPGK1 . HoweverP3xC-TEF1) based on PTEF1 showed the highest strength among all the promoters under almost all conditions and exhibited high stability  or in the presence of xylose in this study. We assumed that the conditions employed in this study exert important influences on the promoter activities, and that these promoter activities are led by the combination of both environmental conditions and growth rate. The mechanisms for dynamic response of individual promoter need to be further investigated in future studies.We also compared our results with the transcriptomic data in the literature. Significant different expression levels of  in PGK1 . In anotperature , which i mixture . However mixture , 40, 41, mixture . Accordiwth rate , neverthPCCW1 and PYGP1 toward low pH could be successfully changed by engineering the TFBSs in the promoter sequences [CLB2 transcription under normal and oxidative stress conditions [PTEF1, 12 more TFBSs for Fkh1p/Fkh2p were found in the synthetic promoter P3xC-TEF1  and stress response elements (STREs) are responsible for response to multiple stresses, and STREs are responsible for the high ribosome occupancy of mRNAs in cytoplasm in response to glucose starvation [PHSP12, PHSP26, and PTPS1, which was consistent with the increased strengths of these promoters in response stressful conditions as well as glucose depletion. Altogether, the predicted correlations of TFBSs and the response of promoter strength might shed lights on further optimization of synthetic promoters in future work.Previous study showed that the responses of the equences , suggestnditions . Comparerevisiae , 45, werarvation . High nuPTDH3 and the synthetic hybrid promoter (P3xC-TEF1) showed the highest strength and stability in almost all conditions tested in this study, suggesting that synthetic promoter has the potential to achieve not only stronger activity, but also more stable expression under various time points and various conditions. The constitutive promoters exhibit dynamic changes in their strengths under various stress conditions. Among the inducible promoters, PHSP12 was superior in higher temperature and acetic acid stress, and showed the highest expression levels when xylose was used as the carbon source. Our results provide novel insights in promoter activities for further optimization of gene expression in practical applications.S. cerevisiae strains were cultured in YP medium (containing 10 g/L yeast extract and 20 g/L peptone) supplemented with different carbon sources. Genomic DNA extracted from the model S. cerevisiae strain S288c  was used for the amplification of native promoters. Laboratory strain S. cerevisiae BY4741 was used as the host for the determination of promoter strengths. To investigate the response of promoters in presence of xylose, a xylose assimilating strain S. cerevisiae LX03 containing xylose reductase-xylitol dehydrogenase (XR-XDH) pathway was used.The strains and plasmids used in this study were listed in Tables Escherichia coli DH5alpha was used for the propagation of plasmid and cultivated in Luria–Bertani medium , 100 mg/L ampicillin was added for the selection of transformants.PADH1, PPGK1, PTEF1, PTDH3, PTPS1, PHSP12, PHSP26 and PADH2, were included  terminator was amplified from pKT127 by polymerase chain reaction (PCR) with primers yEGFP-F/R, the PCR products were cloned into the multi-cloning sites (MCSs) of pRS41H plasmid between SmaI and KpnI. The newly constructed plasmid was named as pRS41H-yEGFP. Then the sequences of eight native promoters were amplified by PCR with the genomic DNA of S288c as template and cloned into the MCSs of pRS41H-yEGFP within BamHI and HindIII restriction site. The hybrid promoter -PTEF) containing PTEF1 core with three tandem UASs of CLB2 promoter  was amplified by PCR from the plasmid p416-UASCLB(3X)-PTEF-lacZ [S. cerevisiae BY4741 or LX03 via LiAc/ssDNA/PEG method [A set of reporter plasmids were constructed with yEGFP as the reporter . Firstly, yeast cells stored in 15% (v/v) glycerol at − 80 °C were inoculated in 100 mL shake flask containing 20 mL YPD medium with 200 mg/L hygromycin B and cultivated at 30 °C, 150 rpm for 16 h. Then 200 μL of the culture was transferred into the 24 deep-well baffled plates with 3 mL growth or fermentation medium with 200 mg/L hygromycin B. The cells were cultivated at 200 rpm, and 39 °C was used to examine the response of promoters toward high temperature, otherwise the cells were cultivated at 30 °C. To test the response of promoters toward different conditions, yeast cells were cultured in different media. Cell growth and promoter strength were compared in BY4741 background under different carbon sources, including 20 g/L glucose (G20), 20 g/L xylose (X20), 20 g/L galactose , 20 g/L glycerol (Gly20) and a mixture of 20 g/L glucose + 10 g/L xylose (G20X10). Besides, 40 g/L ethanol (Eth40) and 5.0 g/L (AA5.0) acetic acid were supplemented into YPD to investigate the response under the stress of ethanol and acetic acid, respectively.S. cerevisiae BY4741 derivative strains were used to determine the promoter strength throughout the ethanol fermentation process, with or without inhibition factors. Cells were cultured in YPD100  medium under the following conditions: (1) 30 °C and 200 rpm (control); (2) 39 °C and 200 rpm (39 °C); (3) 30 °C, 200 rpm and in the presence of 3.6 g/L acetic acid (AA3.6); (4) 30 °C, 200 rpm and in the presence of 1.0 g/L furfural (Fur1.0).S. cerevisiae LX03. Cells were cultured at 30 °C and 200 rpm in YP medium supplemented with the following carbon sources: 20 g/L glucose (G20); 20 g/L xylose (X20); 20 g/L glucose and 20 g/L xylose (G20X20); and 20 g/L glucose and 40 g/L xylose (G20X40). The cell growth and promoter strengths throughout the co-fermentation of glucose and xylose were determined.The responses of promoter strengths in presence of xylose were compared in the xylose assimilating 600) via spectrophotometer  with the 96-well plate. Samples were withdrawn from the cell culture, diluted with ddH2O to an OD600 within 0.2–0.8, 200 μL of the diluted suspension was added into the plates for analysis.The cell density in the culture plates was determined by measuring optical density at 600 nm . Total RNA were extracted with plant total RNA extraction kit . After reverse transcription with the reverse transcription kit , the mRNA level was determined by real-time RT-PCR kit (Bio-Rad). The mRNA levels of rol gene , and theCLB2 was predicted on YEASTACT database (http://www.yeastract.com/) following the previously described method [The TFBSs in the sequences individual promoter as well as in UASd method .2SO4 was used as the mobile phase. All the samples withdrew from the fermentation broth were centrifuged at 10,000×g at room temperature for 2 min, the supernatants were then diluted and filtrated with 0.22 μm filter before sampling. Twenty microliter of diluted samples were injected into the HPLC system for analysis.The concentration of glucose, xylose, ethanol, acetate, glycerol and other components in fermentation broth were analyzed by high performance liquid chromatography system  equipped with a Refractive Index Detector  and Aminex HPX-87H column . The operating temperature of the column and RI were 65 and 50 °C, respectively, and 4 mmol/L HAll the experiments were duplicated and the results were expressed as mean value and standard derivations.Additional file 1: Table S1. Primers used in this study. Table S2. Ranking of promoter strengths at log phase under various conditions. Table S3. Sequence analysis of the promoters. Table S4. Predicted transcription factor (TF) binding sites involved in the promoter sequences. Figure S1. The GFP fluorescence of yeast cells under the control of various promoters and in the presence or absence of hygromycin B. Figure S2. Correlation of yEGFP fluorescence and mRNA levels. Figure S3. Cell growth of S. cerevisiae BY4741 under various conditions. Figure S4. The promoter strengths in log-phase cells under different conditions.