The organization of nucleosomes across functional genomic elements represents a critical layer of control. Here, we present a strategy for high-resolution nucleosome profiling at selected genomic features, and use this to analyse dynamic nucleosome positioning at inducible and cell-type-specific mammalian promoters. We find that nucleosome patterning at inducible promoters frequently resembles that at active promoters, even before stimulus-driven activation. Accordingly, the nucleosome profile at many inactive inducible promoters is sufficient to predict cell-type-specific responsiveness. Induction of gene expression is generally not associated with major changes to nucleosome patterning, and a subset of inducible promoters can be activated without stable nucleosome depletion from their transcription start sites. These promoters are generally dependent on remodelling enzymes for their inducible activation, and exhibit transient nucleosome depletion only at alleles undergoing transcription initiation. Together, these data reveal how the responsiveness of inducible promoters to activating stimuli is linked to cell-type-specific nucleosome patterning. Nucleosome organisation plays important roles in regulating functional genomic elements. Here, the authors use high-resolution profiling to analyse dynamic nucleosome positioning at inducible and cell-type-specific promoters, providing a global view of chromatin architecture at inducible promoters. At gene promoters, the level of DNA accessibility for binding by transcription factors and by the transcriptional machinery represents a fundamental layer of control4. Accordingly, promoter nucleosome positioning is known to be highly regulated, and nucleosomes at promoters of actively transcribed genes often occupy precisely defined positions7.The packaging of eukaryotic genomes into chromatin influences every DNA-templated process. The basic unit of chromatin consists of genomic DNA wrapped around an octamer of histone proteins to form a nucleosome, which alters the conformation and accessibility of the nucleosome-wrapped DNA11, which are partially occluded from nucleosome-wrapped DNA and preferentially digest inter-nucleosomal regions12. A strength of this strategy is that the entire genome is amenable to analysis, and so it can reveal nucleosome positioning at all genomic regions18. However, this can also represent a drawback, since only a small proportion of analysed DNA corresponds to each genomic position, which limits the coverage and resolution at particular regions of interest. As a consequence, most studies performed in larger genomes have relied on high sequencing depths to provide a snapshot of global nucleosome occupancy, without following changes that occur during stimulation or experimental manipulation.Nucleosome positioning can be assayed by limited enzymatic digestion of chromatin using micrococcal nuclease (MNase) or other enzymes7. A similar behaviour has been found in all metazoan genomes that have been analysed, although in many cases this has been studied as an average of large numbers of genes, which could mask differences betweeen individual promoters21.Studies of nucleosome positioning at promoters in yeast have shown that most actively transcribing genes exhibit a characteristic pattern of nucleosome occupancy, consisting of a region with depleted measured occupancy surrounding the transciption start site (TSS), a consistently positioned nucleosome immediately downstream of the TSS (termed the +1 nucleosome), and regularly spaced—or phased—nucleosomes extending into the transcribed body of the gene22, and enzymatic nucleosome remodelling has been shown to play a role in regulating transcription at several well-studied model loci26. Moreover, it has been shown that many inducible genes that do not require remodelling for their activation are characterised by promoter CpG islands (CGIs), which correlate with higher levels of measured nucleosome depletion27, suggesting that this may affect their activation requirements27.At stimulus-inducible promoters in mammals, the magnitude of transcription after activation correlates with promoter nucleosome depletionNevertheless, dynamic changes in nucleosome positioning at mammalian inducible promoters have not been systematically defined at high resolution, and many key questions about its role in gene activation remain unanswered: (i) does the arrangement of nucleosomes at inducible promoters predict their responsiveness, and what changes—if any—occur upon transcriptional activation? (ii) to what extent is promoter nucleosome positioning driven by sequence-encoded features, and, conversely, to what level can it vary according to the cellular context? (iii) does the induction of gene expression establish a stable nucleosomal configuration that is permissive for multiple rounds of transcriptional initatiation, or are repeated nucleosome remodelling events required at each cycle of initiation? and (iv) can gene activation occur at all without nucleosome depletion from the transcriptional start site at promoters?Here, we establish a targeted strategy for mapping nucleosome occupancy at high resolution at defined genomic features, and apply this to investigate its role in gene activation. We find that nuclesome patterning at inducible promoters can define their cell-type-specific responsiveness, and that activation of non-patterned inducible promoters is associated with short-lived nucleosome remodelling events that accompany transcriptional initiation.To allow analysis of nucleosome positioning at genomic regions-of-interest associated with selected biochemical properties, we developed a protocol that we term ‘ChIP-MNase’, which couples chromatin immunoprecipitation (ChIP) with on-bead MNase-digestion of the recovered chromatin fragments , we selected the histone modification H3K4me1. H3K4me1 marks both promoters and enhancers that are active in a particular cell typeWe normalised the ChIP-MNase signal at the kilobase-scale, in order to counteract the uneven recovery of distinct genomic loci by ChIP and remove any contribution of the local profile of the chosen ChIP target across each locus from the MNase digestion profile Fig. , which a18. We performed ChIP-MNase using independent replicate samples of two cell-types—3T3 fibroblasts (hereafter fibroblasts) and in vitro-differentiated dendritic cells (DCs)—under distinct experimental conditions, to measure reproducibility and accuracy. To analyse reproducibility between samples, we used unsupervised hierarchical clustering of the normalised nucleosome occupancy signal surrounding all promoters. Replicate biological samples of each cell-type cluster together , or other particles that confer a reduced footprint of protection from MNase38. Indeed, using ChIP-MNase, we could readily detect sub-nucleosomal sized (50–100 bp) protected fragments within promoter NDRs . We used predicted nucleosome positions to realign occupancy profiles at individual promoters, either close to the TSS or at distal locations. We find that the phased profile of mean occupancy is largely undiminished both downstream and upstream of the TSS when nucleosomes are individually aligned  positioning46; Fig. We also analysed nucleosome positioning at non-promoter regulatory elements identified by DNase hypersensitivity. Mean nucleosome occupancy is depleted at DNase-hypersensitive sites , a potent pro-inflammatory stimulus that activates rapid expression of hundreds of target genes, and identified LPS-inducible genes by transcriptomic analysis of mRNA levels (note that genes assigned as non-inducible in this setup may nevertheless include some that might be induced by other stimuli). We then analysed nucleosome positioning in ChIP-MNase datasets from replicate samples before and after cellular stimulation.As described above, the mean nucleosomal occupancy across active promoters follows a characteristic pattern. Quantitative analysis of individual promoters indicates that this patterning is shared by the majority (>80%) of active promoters, and that it is absent from almost all inactive promoters Fig. . StrikinHowever, quantification of nucleosome patterning at individual promoters revealed that despite their bulk similarity to active promoters, inducible promoters are heterogeneous and can be divided into two groups: ‘patterned’ inducible promoters display features of active promoters even before stimulus-driven activation, including a pre-formed NDR at the future TSS; in contrast, ‘non-patterned’ inducible promoters lack a clearly measurable NDR and conform better to the arrangement of steady-state inactive pomoters Fig. . At someUnexpectedly, nucleosome patterning at both patterned and non-patterned inducible promoters was largely unchanged upon stimulus-driven activation. Patterned inducible promoters, which already resemble active promoters in the unstimulated state, mostly retained this nucleosomal patterning after stimulation, and the subset of inducible promoters that did not display this patterning before stimulation also generally did not acquire it afterwards Fig. . Thus, s53; however, an alternative possibility is that nucleosomal patterning is independently established in each cell type, and could thus identify the specific set of promoters that are stimulus responsive. To address this, we investigated the behaviour of promoters that are LPS-inducible in DCs, when they are instead exposed to a different stimulus in a different cell type. We stimulated fibroblasts with tumour necrosis factor alpha (TNF-α), an endogenous inflammatory cytokine that—like LPS—activates the NF kappa B pathway, and that induces an overlapping but distinct set of target genes.The presence of an NDR and pre-patterned nucleosomes at some stimulus-inducible promoters could arise from inherent, constitutive positioning signals encoded in their DNA sequencep ≤ 3.7 × 10−4). Thus, nucleosome patterning at inducible promoters can be cell-type-specific, and can reflect the stimulus-responsiveness of each promoter in a given cell type. This also indicates that the pattern of nucleosome positions at many inducible promoters—including the pre-stimulation presence of an NDR—is not universally specified by their DNA sequence alone .We first classified DC-inducible promoters into sets according to whether they were active, inducible or inactive in fibroblasts, and examined nucleosome patterning in each set separately Fig. . In DCs,p ≤ 1.6 × 10−3). Thus, nucleosome patterning is sufficient to predict the cell-type specific behaviour of some promoters. Conversely, promoters that are inducible in fibroblasts were not distinguishable from those that are active by this clustering, in agreement with the similarity of their patterning when they are analysed as separate groups . We began by using hierarchical clustering of individual DC-inducible promoters, based on their patterns of nucleosome occupancies in DCs and fibroblasts: this revealed three principal clusters, which were distinguishable by the presence or absence of an NDR in each cell type Fig. . Notablyps Figs. .p ≤ 2.1 × 10−5; Fig. The unsupervised nature of hierarchical clustering provides an unbiased indication of which promoters behave similarly, but it does not readily indicate the particular aspect(s) of the data that most-strongly drive the clustering. We therefore tested whether one could recapitulate the same level of prediction by simple grouping based on the quantified level of nucleosome patterning at each individual promoter as Fig. . Indeed,These analyses also revealed that promoters that are consistently patterned in both cell-types tested, which correspond to those that are always active or inducible, are strongly enriched for CGI-containing promoters Fig. . Thus, t12). This was proposed as a likely prerequisite for inducible activation of CGI promoters27—although until now it has not been assessed at the level of in vivo nucleosome positioning—and our data suggest that this may also be a requirement for patterned promoters that lack a CGI. To explore this further, we directly analysed RNA pol-II levels at inducible genes in non-stimulated and stimulated fibroblasts.The patterning observed at some inducible promoters before stimulation implies that their responsiveness may already be pre-configured into their nucleosomal arrangement. In particular, the presence of a pre-stimulation NDR could facilitate stimulus-inducible activity in a particular cell-type by rendering the TSS accessible for binding by the transcriptional machinery , are generally not BAF dependent27. Together, these data have led to a well-accepted model whereby the BAF complex is required to establish accessibility at many inducible promoters upon activation27.Our finding that most non-patterned promoters are inducibly activated without any measurable NDR was unexpected, and raises the question of how RNA pol-II or other components of the transcriptional machinery can gain access to the TSS at these promoters. Previous studies have shown that the BAF nucleosome-remodelling complex  is required for activation of many stimulus-inducible genesDRs Fig. 13, are gA simple expectation of this model would be that the BAF complex may remodel nucleosomes at inducible promoters from a non-permissive, inactive arrangement to a permissive, NDR-containing arrangement resembling the patterning of other active promoters. Thus, our finding that activation of non-patterned inducible promoters is not associated with establishment of a stable, patterned configuration was unanticipated Figs. . We ther24). We then measured gene expression levels and promoter nucleosome occupancies in Brm/Brg1-double-knockdown fibroblasts (hereafter BAF knockdown).We used shRNA to stably knock-down the mRNAs encoding the two catalytic remodelling subunits of the BAF complex, Brm and Brg1, in fibroblasts. A single shRNA targeting a shared sequence was able to reduce levels of both transcripts by around fivefold Fig. a24. We tExpression levels of most genes were undisturbed in BAF-knockdown cells, implying that the activity of the BAF complex is non-essential for the majority of steady-state transcription  of transcriptional activation.The finding that the BAF nucleosome remodelling complex is specifically required for activation of non-patterned inducible promoters provides direct in vivo support for a long-standing model for inducible gene activationThe measured nucleosome occupancy at each promoter represents the mean of both alleles, in samples prepared from millions of cells. Thus, we first considered whether the lack of a detectable NDR could be explained if only a minor fraction of promoters are activated under our stimulation conditions, while the majority of alleles remain inactive.63 and thus selectively marks the alleles of genes that are being actively transcribed . This modification is associated with the elongating form of RNA pol-II63). Using this approach to selectively obtain nucleosome profiles of the initiating alleles (pol-II S5P) of inducible non-patterned promoters, we could reveal the appearance of a detectable NDR at the TSS of at least 62% of these promoters  from most other active promoters  and to measured redundant nucleosome positioning in yeast (20–40 bp64).By enriching for genomic intervals bearing a selected biochemical property (not limited to antigens on nucleosomes), ChIP-MNase significantly increases the level of coverage across chosen features-of-interest (by 6- to 35-fold in our experiments), which is critical in order to define nucleosome positions with accuracy  at the TSS of many of these promoters when bound by initiating RNA pol-II  at non-patterned promoters, and it is plausible that this active process must occur repeatedly for successive transcription initiation events. How then is the BAF complex recruited to act at promoters? Using these publicly available data, we found that the BAF complex binds to most inducible promoters in non-stimulated cells, before promoter activation. Thus, it seems likely that the BAF complex associates with chromatin features or factors that are already present at inducible promoters in the inactive state, further supporting the notion that the responsiveness of these promoters is preconfigured before stimulation.81. Our finding that BAF-dependent genes are typically characterised by unpatterned promoters, which lack a stable NDR, suggests that aspects of promoters that favour efficient nucleosome assembly may be exploited as a strategy to achieve highly regulated expression. In line with this, sequences favouring high nucleosome occupancy are frequently selected at promoters and gene regulatory regions83.The set of genes that require the remodelling activity of the BAF complex for full activity in fibroblasts is rather limited, comprising only 8% of expressed genes in this cell type. However, it is enriched among genes with cell type-specific expression, and strongly enriched among stimulus-inducible genes. This concurs with its previously reported role in developmentally regulated and inflammatory gene expressionIn summary, in this report we have used the ChIP-MNase approach to characterise nucleosomal behaviour at high-resolution during activation of stimulus-inducible promoters. We find that the patterning of nucleosomes at individual promoters can vary substantially between cell-types, but that it is strikingly constant during stimulus-driven activation, and transcription initiation is linked to mainly transient remodelling events. Altogether, our results show that nucleosome patterning can define both the cofactor requirements and cell type-specific responsiveness of inducible promoters.−1 SCF, IL-3 (0.1% X63-IL3 supernatant) + IL-6 (2% X63-IL6 supernatant). DCs were differentiated from progenitor cells in granulocyte-macrophage colony-stimulating factor (GM-CSF) (4% X63-GMCSF supernatant) + 5 ng ml−1 SCF for 4–5 days, followed by GM-CSF alone for 4–5 days84. DCs were adherent and >90% MHC class II + CD11c+ by flow cytometric analysis. DCs were stimulated for 1 h with 100 ng ml−1 LPS. 3T3 fibroblasts were stimulated for 1 h with 5 ng ml−1 TNF-α.Progenitor cell lines derived from mouse bone marrow were cultured under non-differentiating conditions in 20 ng mlg at 4 °C. Cells were resuspended at 2 × 107 ml−1 and nuclei were released by incubation in ice-cold L1 buffer  for 5 min. Nuclei were collected by centrifugation for 5 min at 500g at 4 °C and washed twice in ice-cold buffer D . Washed nuclei were resuspended in MNase buffer  at 2 × 107 cells in 400 μl−1 and lysed by two freeze–thaw cycles and sonication . For each sample, six 20 μl aliquots (containing 106 lysed nuclei) were removed and digested with titrated amounts of MNase (5-fold dilutions from 4 U to 0.0013 U MNase per 106 lysed nuclei) at 25 °C for 30 min with shaking, reactions were stopped by addition of EDTA to a final concentration of 12.5 mM, fixation was reverted by addition of an an equal volume of 2% sodium dodecyl sulfate (SDS) solution and overnight incubation at 65 °C, samples were diluted to 500 μl with PBS and digested with 100 μg ml−1 proteinase K for 1 h at 55 °C, and DNA was purified using 300 μl of Miniprep Express matrix. Each digested aliquot was analysed on an agarose gel to determine the dose of MNase required to generate the desired fragment size for ChIP . This dose was used to digest the remainder of each sample in several 200 μl reactions (containing 107 lysed nuclei). After stopping reactions, debris was pelleted by centrifugation and supernatants containing chromatin were pooled and used for ChIP. Four parallel immunoprecipitations of chromatin from 2 × 107 nuclei were performed for each sample. 1 ml of buffer V  was added to each 400 μl of digested chromatin, and samples were pre-cleared using 40 μl of protein-G sepharose for 1 h at 4 °C with rotation. After centrifugation at 500g for 1 min, 4 μg of antibody was added to the supernatant containing the pre-cleared samples and incubated overnight at 4 °C with rotation. Antibody-bound chromatin was pulled-down by addition of 15 μl of protein-G sepharose for 30 min at 4 °C, and collected by centrifugation. Chromatin-bound beads were washed once quickly and 3 times for 5 min each with 1 ml of ice-cold buffer WBNS , followed by two washes for 5 min each with 1 ml of ice-cold MNase buffer. A 50 μl aliquot of each sample was collected at the penultimate wash step for verification of ChIP efficiency. Immunoprecipitated chromatin was resuspended in MNase buffer to a final volume of 30 μl (including 15 μl of beads), and digested using titrated amounts of MNase  at 25 °C for 30 min. Each reaction was stopped by addition of EDTA to a final concentration of 12.5 mM, fixation was reverted by addition of 70 μl of 2% SDS solution and overnight incubation at 65 °C, and DNA was directly purified using the MinElute PCR purification kit (Qiagen), and eluted in 30 μl elution buffer. DNA samples were quantified using picogreen reagent and a Qubit fluorometer (Invitrogen). ChIP efficiency was verified by quantitative polymerase chain reaction (qPCR) using pre-digestion samples, with primers specific for known positive and negative control regions according to the antibody target. MNase digestion efficiency was assessed by anaysis of 1 μl of each digestion using Agilent HS DNA chips, and for each initial sample, the digestion yielding approximately 80% mononucleosome-sized fragments was selected for sequencing. This was chosen to encourage complete digestion of inter-nuclesomal DNA, while limiting the scope for MNase nibbling into the edges of nucleosome-bound regions , and collected by scraping and centrifugation for 10 min at 50085. Total RNA was separated by addition of 0.2 volumes of chloroform, emulsification by shaking, and centrifugation at 13,000g for 10 min at 4 °C, followed by precipitation of RNA in the upper, aqueous phase by addition of 1 μl of glycogen azure (Sigma) plus 2.5 volumes of isopropanol, centrifugation at 13,000g for 20 min at 4 °C, and washing the RNA pellet in 70% ethanol. RNA samples were dissolved in 20 μl water, quantified using a Nanodrop spectrophotometer (Thermo), and subsequently processed for microarray analysis using Affymetrix GeneChip mouse gene 1.0 ST arrays. Three independent biological replicates were performed on different days and analysed for each experimental group.Cells were lysed directly into RNA lysis buffer  for 5 min. Nuclei were collected by centrifugation for 5 min at 500g at 4 °C and resuspended at 5 × 107 ml−1 in 900 μl of ice-cold L2 buffer . Chromatin was fragmented by sonication to an average size of 600–700 bp  and insoluble debris was pelleted by centrifugation. A 50 μl aliquot was removed from each sample and analysed by agarose gel electrophoresis after DNA extraction to verify fragmentation. Fragmented chromatin was diluted with 9 volumes of buffer DB  and pre-cleared using 40 μl of protein-G  or protein-A  sepharose for 1 h at 4 °C with rotation. After centrifugation at 500g for 1 min, 4 μg of antibody was added to each 1 ml of supernatant containing the pre-cleared samples and incubated overnight at 4 °C with rotation. Antibody-bound chromatin was pulled-down by addition of 15 μl of protein-A or -G sepharose for 30 min at 4 °C, and collected by centrifugation. Chromatin-bound beads were washed once quickly and 4 times for 5 min each with 1 ml of ice-cold buffer WB , followed by 3 washes for 5 min each with 1 ml of ice-cold TE. Immunoprecipitated chromatin was released by incubating beads in buffer EB (TE + 2% SDS) for 5 min at room temperature with periodic tickling, and the supernatant was collected. This was repeated two more times and the supernatantw were pooled. Fixation was reverted by overnight incubation at 65 °C, and DNA was directly purified using the MinElute PCR purification kit (Qiagen), and eluted in 30 μl elution buffer. DNA samples were quantified using picogreen reagent and a Qubit fluorometer (Invitrogen). ChIP efficiency was verified by qPCR with primers specific for known positive and negative control regions according to the antibody target. Sequencing libraries were prepared using the NebNext Ultra II DNA library kit (New England Biolabs), and sequenced using HiSeq2000 or NextSeq500 instruments (Illumina).Cells were washed twice with PBS at room temperature, and fixed by addition of 20 μl of 0.5 M DSG to cells in 5 ml PBS and incubation for 45 min at room temperature. Cells were washed again twice with PBS at room temperature, and double-fixed by addition of formaldehyde to a final concentration of 1% to cells in 5 ml PBS and incubation for 15 min at room temperature. Fixation was stopped by addition of glycine to a final concentration of 125 mM. Fixed cells were washed once quickly and twice for 10 min each with ice-cold PBS, and collected by scraping and centrifugation for 10 min at 500Brg1 and Brm mRNAs were stably knocked-down using an shRNA with sequence tggagaagcagcagaagattTCAAGAGaatcttctgctgcttctcca . Fibroblasts were retrovirally transduced with supernatants from transfected ecotropic Phoenix packaging cells, and sorted for high GFP expression. The level of residual Brg1 and Brm mRNA  and gccagtggatttcaaaaagataaa & ttgtgacagagaagcatgacg (to detect Brm).RNA Fig.  was meas86. Anti-pol-II S7P  was from Millipore, anti-pol-II S5P (cat ab5121) was from Abcam, anti-p65  was from Santa Cruz biotechnology.Monoclonal anti-HeK4me1 (clone CMA-302) was provided by H. Kimurap value of 0.05; the level of redundant reads that were filtered-out at this stage was typically around 10%. For paired-end sequencing, only mononucleosome-sized fragments in the range 140–180 bp were retained for subsequent analysis; for single-end sequencing, fragments were estimated by extending reads to the average fragment size derived from the distribution of distances between nearby reads that mapped to the genome in opposite orientations. Fragments mapping within 10 kb of refGene annotated promoters were selected, and only fragments that were uniquely mapped within this set were retained . The total number of fragments mapping within bins spanning 10 bp genomic intervals were counted, and these counts were normalised according to the total number of fragments mapping within the surrounding 1 kb genomic interval. Nucleosome occupancy was calculated as the mean number of normalised counts in overlapping 50 bp bins centred every 10 bp, and the levels were calculated for every position from −1 kb to +1 kb surrounding refGene annotated promoters.Demultiplexed sequence data was aligned to the mouse mm9 genome using bowtie ; alignment rates were typically around 80% for H3K4me1 ChIP-MNase, and around 90% for pol-II ChIP-MNase. Excess duplicate reads that mapped to the exact same genomic location (corresponding to likely PCR duplicates) were eliminated if there were significantly more than expected based on the local read density in a range of ±2 bp at a The mean number of fragments per bp in each dataset mapping between −10 kb and +10 kb of each annotated refGene promoter was counted, and the fold-enrichment at each promoter was calculated by dividing this number by the mean genome-wide density of mapped fragments in the dataset . High coverage promoters, which were used in subsequent analyses, were defined as those with a mean coverage of ≥0.05 fragments per bp in the range −1 kb to +1 kb in non-stimulated DC and fibroblast datasets , and amounted to 13,559 promoters, or 53% of the total of 25,881 refGene annotated promoters. To ensure that this criterion did not result in exclusion of promoters of non-expressed or low expressed genes, the fraction of promoters of genes with distinct expression levels was calculated among these high coverage promoters, or among the 10% of promoters with the highest fold-enrichment  were first identified throughout the 10 bp-resolution occupancy data. Next, to distinguish discrete peaks (likely reflecting true preferred nucleosome positions) from low-level measurement noise, we calculated the topographic prominence of each peak, defined as the height of each peak above the lowest valley separating it from an adjacent higher peak  and of the major peak of opposite-strand distances  gave comparable results . To exclude promoters, only DHS midpoints >1 kb from annotated TSSs were analysed. The midpoint of each DHS was defined as the median cut site position, that is, the position at which half of all sequence tags mapped within the DHS region are mapped on either side. Normalised nucleosome occupancies were aligned relative to this position. To identify predicted single-nucleosome-sized gaps within the ChIP-MNase data, we calculated the distances between all adjacent predicted nucleosome positions, and selected those in the range of 330–370 bp from each replicate fibroblast dataset. We considered only those gaps that were independently identified in both replicates, and that fell within 1 kb intervals with a minimum coverage of 0.025 fragments per bp (25 mapped fragments within the 1 kb range). DNase hypersensitivity data was aligned relative to the midpoint of each gap. Gaps were deemed to correspond to DHS regions if the mean density of DNase sequence tags across the 400 bp interval centred on the nucleosome gap exceeded 0.1 per bp . Note that although 39.5% of predicted gaps were classed as DHS using this criterion, this represents a lower bound for the true fraction of gaps that are DHS, since it is limited by any inaccuracies in the predition of nucleosome positions.DNase hypersensitive sites (DHS) analysed in fibroblasts correspond to ENCODE narrowPeak annotations (GEO accession number t test of replicate samples). CGI promoters were defined as those with a GC content of >50% and observed/expected ratio of CG-dinucleotides of >0.6 across the region spanning the TSS ± 200 bp87. BAF-dependent and -inhibited promoters were defined as those of genes that exhibited decreased or increased expression, respectively, in BAF-knockdown cells greater than 0.3 (measured by Affymetrix microarray RMA fluorescence differences in replicate samples).Active promoters were defined as those of genes with expression levels in non-stimulated cells (measured by RNA-seq) greater than 0.02× the mean expression level of all genes, and excluding inducible promoters. Inactive promoters were defined as those of genes with no detectable expression in non-stimulated cells  and excluding inducible promoters. Inducible promoters were defined as those of genes that exhibited a stimulus-driven increase in expression (measured by Affymetrix microarray RMA fluorescence differences) greater than 1 (for LPS-stimulated DCs) or greater than 0.35 (for TNF-α-stimulated fibroblasts), and for which the increase is statistically significant at an α-level 0.05 . Hierarchical clustering was performed using the Euclidian distances between levels, and the Ward’s minimum variance method to merge clusters.p value of 0.05; the level of redundant reads that were filtered out at this stage was around 6%. The total number of fragments mapping to each genomic interval were counted; fragments that mapped ambiguously to more than one genomic location were counted fractionally at all aligning locations.Demultiplexed sequence data was aligned to the mouse mm9 genome using bowtie (with -v 2 -a -m 5 --maxins 2000 --tryhard for paired-end sequencing); alignment rates were around 78%. Excess duplicate reads that mapped to the exact same genomic location (corresponding to likely PCR duplicates) were eliminated if there were significantly more than expected based on the local read density in a range of ±2 bp at a GSM1624320) at promoters and within gene bodies were calculated as the mean ChIP coverage levels across the TSS ± 500 bp  or across the region between +1 and +6 kb . The pause index for each promoter was defined as the ratio of pol-II levels at the promoter to the levels at the gene body.The levels of RNA pol-II S7P or of unmodified RNA pol-II (using dataset GSM624282), gene expression in fibroblasts (GSM970853), gene activation in LPS-stimulated DCs , gene activation in TNF-α-stimulated fibroblasts , DHSs in fibroblasts (GSM1014177), MNase-seq in fibroblasts , BAF-155 ChIP-seq (GSM1835955)93 and Brg1 ChIP-seq (GSM1835956 and GSM1132963) were retrieved from GEO.Gene and promoter annotations are from refGene (refGene table downloaded 08112013). Datasets used to analyse gene expression in DCs (Further information on research design is available in the Peer Review FileSupplementary InformationReporting Summary


A plethora of computational approaches have been proposed for reconstructing gene regulatory networks (GRNs) from gene expression data. However, gene regulatory processes are often too complex to predict from the transcriptome alone. Here, we present a computational method, Moni, that systematically integrates epigenetics, transcriptomics, and protein–protein interactions to reconstruct GRNs among core transcription factors and their co-factors governing cell identity. We applied Moni to 57 datasets of human cell types and lines and demonstrate that it can accurately infer GRNs, thereby outperforming state-of-the-art methods. In particular, the epigenetic landscape, defined by epigenetic modifications, chromatin accessibility, and chromatin conformation, determines phenotype-specific active regulatory regions of each core TF4. In particular, in contrast to active promoter regions shared by various cell and tissue types, enhancer regions modulate phenotype-specific expression profiles due to their phenotype-specific selection5.Cellular phenotypes are characterized by stable gene expression profiles maintained by a set of transcription factors (TFs) that jointly determine cell identity. Together with other co-factors, these identity TFs form a regulatory core network, which is shaped by the epigenetic landscape11. However, these methods require tremendous amounts of transcriptomics data and cannot provide information about the active regulatory regions, an issue that even new technologies such as single-cell profiling do not mitigate.In the past decades, a wealth of computational methods has been developed that aim at identifying regulatory interactions between genesMulti-omics network inference), a computational method that systematically integrates histone modification, chromatin accessibility, and transcriptomics data with a global atlas of TF-binding events, enhancer–promoter interactions, and protein–protein interactions across diverse cell types and lines, obtained from ENCODE12, the Roadmap Epigenomics Project13 and the Blueprint database14, in order to reconstruct phenotype-specific core regulatory networks. As a result, Moni provides a comprehensive map of the phenotype-specific core regulatory network including regulation at distal enhancer regions and the cooperativity of TFs in the regulation of target genes. With the steady increase in epigenetic profiling, we expect Moni to be of general utility for the molecular characterization of cellular phenotypes and to aid in the identification of key regulators.To address these shortcomings, we propose Moni (15). Based on a previous study demonstrating that the TFs with the highest phenotypic specificity are most likely to be essential determinants of cell identity16, the 10 TFs with the highest phenotypic specificity are selected as core TFs. In addition, potential co-factors are detected, i.e., TFs that are significantly more specific to the phenotype than their expected median specificity. Secondly, active promoters and enhancers of core TFs and co-factors are identified. Promoter regions are considered to be active if they overlap with at least one H3K4me3 peak while potential enhancers are associated to TFs on the basis of the GeneHancer database17 and deemed active if they overlap with at least one H3K27ac peak. Finally, directed interactions among core TFs and co-factors are inferred if they satisfy each of the following conditions: (1) the promoter of the target TF is active, (2) the interaction is supported by a (non-phenotype-specific) ChIP-seq peak in the promoter or any active enhancer region of the target TF, and (3) the supporting ChIP-seq peak falls within an accessible chromatin region. For interactions within the same genomic regions, i.e., enhancer or promoter, cooperative and competitive TF regulation is determined by the overlap of supporting ChIP-seq peaks and whether a protein–protein interaction among the TFs has been reported in databases  reconstruction by Moni involves three main steps Fig. . Firstly6), RTN10, ARACNE18, and Minet8. Cell-type-specific TF ChIP-seq data were collected from Cistrome19 and ENCODE12 and restricted to TFs in the networks reconstructed by each individual method. Comparison of predicted and validated interactions in the promoter regions resulted in an average F1 score of 0.84 of Moni , GM12878 (B-lymphocytes), and HepG2 (liver cancer), and compared the performance to six state-of-the-art methods including GENIE3 , in vitro-differentiated mesenchymal and neural stem cells, and in vitro-differentiated trophoblast, using promoter-capture Hi-C datasetslls Fig. . MoreoveIn addition to validating interactions and enhancer–promoter assignments, we assessed the selection and reproducibility of core TFs by employing the reconstructed networks for 54 cell lines and types. Projecting the incidence matrix of TFs and cell types into planar space reveals a homogeneous clustering by cell types, except for embryonic and cancer samples that are clustered together due to their share of co-factors Fig. . Of noteNext, we sought to establish the functional implication of core TFs and co-factors in maintaining important cell-type functions. Gene Ontology enrichment demonstrates that the selected TFs are not only consistently selected but are also implicated in important cellular functions , Cistrome19, String22, and Enhancer Atlas23. In addition, publicly available query datasets are available from the International Human Epigenome Consortium24 (IHEC), ENCODE12, and Gene Expression Omnibus25. Thus, we expect Moni to be a valuable tool for obtaining mechanistic insights into key transcriptional regulators in processes such as cell conversion and disease phenotypes.In summary, Moni integrates multiple-omics datasets to provide a more comprehensive characterization of the core GRN controlling cell identity. Although, we demonstrated the performance of Moni using human cell types and lines in this study, it is applicable to samples from other species. Especially for mouse cells, a multi-omics data atlas can be readily compiled from data in ArchS4 Given a query RNA-seq sample, the appropriate background distribution, i.e., polyA or total RNA, was selected based on the sequenced molecules. For each TF, the following three steps were performed. First, an idealized distribution was created for the query and background samples, having probability “1” in place of the query sample and “0” otherwise. Second, the background expression distribution was created for the combined query and background samples. Probabilities are defined as the scaled expression in each sample such that the sum of probabilities equals one. Finally, Jennsen–Shannon divergence (JSD) was computed between the idealized and background distributions. The 10 TFs having the lowest JSD value were considered identity TFs.z-score was computed for the rank its JSD value given the distribution of ranks in the background sample. Each TF having a z-score less than −1.5 were considered as co-factors.For co-factors, JSD values were computed for each sample in the background and ranked in ascending order, i.e the lowest JSD value has rank 1. Given a query sample, for each TF a 26 and truncated to 1500 bp up- and 500 bp downstream. If a promoter overlaps at least one phenotype-specific H3K4me3 peak, it is considered to be active. In addition, potential enhancer regions are linked to active promoters by the Genehancer database17. Enhancers are active if they overlap at least one phenotype-specific H3K27ac peak and truncated to the peak regions. Inactive enhancers are not considered. Third, phenotype-specific DNase-seq peaks in active promoter and enhancer regions define potentially active regulatory sites. Finally, (non-specific) TF ChIP-seq peaks from Cistrome19 overlapping with an active regulatory site establishes the GRN scaffold. The GRN scaffold is further filtered such that only interactions between identity TFs and co-factors are retained that satisfy the following condition. Each co-factor included in the network must regulate at least one identity TF and must be regulated by at least one identity TF.GRN reconstruction follows a four-step approach. First, identity TFs and co-factors are selected as described before. Second, active proximal and distal regulatory regions are identified for every identity TF and co-factor. Promoters are defined on the basis of the Ensembl promoter annotation from the Eukaryotic Promoter Database22 with a confidence score greater than 800. Second, the TF ChIP-seq peaks reciprocally overlap by at least 62%. The overlap threshold was defined on the basis of a positive and negative gold-standard dataset including 755 and 336 protein-protein interactions (PPIs), respectively28. The overlap of ChIP-seq peaks of interacting and non-interacting TFs was computed in all cell lines and types with available information in Cistrome19, resulting in a threshold of 62.43% above which TFs are more likely to interact than not , C3Net7, Minet8, ARACNE18, RTN10, and CorTo11. Of note, ARACNE was run using the implementation in the Minet R-package. For GENIE3 and Minet, different algorithms were employed for reconstructing networks. In particular, we employed GENIE3 using either the random forests or extra trees algorithm with all TFs (parameter: “ALL”) or a random selection of approximately 41 TFs (parameter: “SQRT”). For comparison, interactions within the top quartile were considered. Further, we employed Minet using the following configurations:Method parameter: mrnet and estimator parameter either “spearman” or “pearson”.Method parameter: aracne and estimator parameter either “mi.mm” or “mi.empirical” using “globalequalwidth” discretization.Method parameter: mrnetb and estimator parameter either “mi.mm” or “mi.empirical” using “globalequalwidth” discretization.We compared the performance of our method to six other methods, namely GENIE3 , in vitro-differentiated mesenchymal and neural stem cells and in vitro-differentiated trophoblast were obtained from a previous study17 and the other region is located within the promoter of a TF using bedtools version 1.2.4.2 (ref. 29) and a custom R script.For each sample, we obtained promoter–promoter and promoter–other interactions and identified all interactions where one genomic regions is located in an enhancer described in GeneHancerWe restricted the gold-standard datasets of every cell line/type to the TFs in the reconstructed networks and computed the percentage of correctly assigned enhancer regions.Further information on research design is available in the Reporting SummarySupplementary FiguresSupplementary Table legendsSupplementary Table 1Supplementary Table 2Supplementary Table 3


Arabidopsis thaliana by transcription isoform sequencing (TIF-seq) and report four transcript isoforms per expressed gene. While intragenic initiation represents a large source of regulated isoform diversity, we observe that ~14% of expressed genes generate relatively unstable short promoter-proximal RNAs (sppRNAs) from nascent transcript cleavage and polyadenylation shortly after initiation. The location of sppRNAs correlates with the position of promoter-proximal RNAPII stalling, indicating that large pools of promoter-stalled RNAPII may engage in transcriptional termination. We propose that promoter-proximal RNAPII stalling-linked to premature transcriptional termination may represent a checkpoint that governs plant gene expression.RNA polymerase II (RNAPII) transcription converts the DNA sequence of a single gene into multiple transcript isoforms that may carry alternative functions. Gene isoforms result from variable transcription start sites (TSSs) at the beginning and polyadenylation sites (PASs) at the end of transcripts. How alternative TSSs relate to variable PASs is poorly understood. Here, we identify both ends of RNA molecules in  Arabidopsis, suggesting this may represent a checkpoint that regulates plant gene expression.Gene isoforms result from variable transcription start sites (TSSs) and polyadenylation sites (PASs) at the end of transcripts. Here, the authors perform transcript isoform sequencing and find widespread promoter- proximal transcriptional termination in  However, RNAPII transcription generates distinct mRNA and non-coding RNA isoforms derived from a single gene2. As alternative gene isoforms may have opposing functions3, the identification of transcript isoform diversity can be key to resolve molecular mechanisms underpinning causative genetic variation. The selection of transcription start sites (TSSs) represents a key source of transcript heterogeneity. The majority of TSSs map to gene promoter regions, where DNA sequence elements recruit RNAPII to initiate transcription in nucleosome-depleted regions (NDRs). The precise positions of TSSs may form a focused pattern with one predominant TSS position, or a dispersed pattern, where TSSs can be detected within a broader sequence window that is characteristic of housekeeping genes4. A dispersed pattern of initiation maintains steady levels of transcription, yet results in RNA molecules that differ in their 5′ sequences, which may affect transcript stability5 or alternative translation6. Additional transcript isoforms result from transcription on the antisense strand of genes to yield long non-coding RNA (antisense lncRNA)7. Antisense lncRNA transcription may regulate expression of the corresponding mRNA transcript8. Transcriptional initiation within genes (intragenic initiation) may result in mRNA isoforms encoding protein variants lacking N-terminal protein domains9. Chromatin-based mechanisms counter-acting intragenic TSSs during RNAPII elongation could affect the diversity of proteins derived from a gene10. However, it remains an open question whether intragenic TSSs usually connect to the canonical gene 3′-end, or if short transcript isoforms are generated that may not be translated.Organisms respond to changing environments by regulating the transcriptional activity of RNA polymerase II (RNAPII) to adjust gene expression12. Transcriptional termination at intragenic polyadenylation sites (PASs) results in mRNA isoforms lacking C-terminal protein domains and represents a mechanism regulating a wide range of biological processes12. In addition, the process of transcriptional termination is tightly connected to RNA surveillance pathways that determine RNA stability and nuclear export13. The nuclear exosome represents a 3′-to-5′ exonuclease that associates with RNAPII to degrade nascent RNAs7. Transcriptome analyses in nuclear exosome mutants thus reveal RNA molecules that are transcribed yet rapidly targeted for RNA degradation (i.e. cryptic transcripts), for example widespread divergent lncRNA transcription from gene promoter NDRs in the opposite direction respective to the mRNA7. Transcription in each direction of gene promoter NDRs is also selectively controlled at the level of initiation by chromatin-based mechanisms15 and sequence-specific transcription factors16. Binding sites for the spliceosomal small nuclear RNA (snRNA) U1 selectively promote elongation of mRNA transcription over divergent lncRNA transcription17. The suppression of premature transcriptional termination (i.e. telescripting) by U1 is distinct from the splicing function of U1 (ref. 18). pre-mRNA splicing represents an additional mechanism to amplify gene isoform diversity through alternative combinations of exons19. In summary, alternative pre-mRNA processing activities during transcriptional elongation contribute to the transcript isoforms generated from a single gene.Alternative transcriptional termination shapes isoform expression through differential selection of the 3′-end of transcripts20. Peaks near TSSs represent RNAPII promoter-proximal pausing, where transcriptionally active RNAPII accumulates21. In metazoans, DRB sensitivity-inducing factor (DSIF) and negative elongation factor (NELF) stabilise RNAPII pausing22. RNAPII transcription elongation factors such as positive transcription elongation factor b (p-TEFb) and polymerase associate factor I (PAF-I) trigger disassociation of NELF and DSIF and promote transcriptional elongation24. The stability of paused RNAPII complexes in metazoans varies greatly between genes27, supporting the idea that RNAPII turnover affects the accumulation of RNAPII complexes near TSSs28. RNAPII turnover at PASs as part of transcriptional termination coincides with peaks of RNAPII density11. As there is known RNAPII accumulation at PASs at the ends of genes, it is possible that the accumulation of RNAPII at the pausing sites is a result of the same process, but coupled to promoter-proximal polyadenylation. Indeed, short transcripts at genes with unstable promoter–promoter proximal RNAPII pausing are reported in Drosophila29 that rely on transcriptional termination by the Integrator complex31, that also functions in 3′-end processing of relatively short snRNA32. The prevalence of promoter-proximal transcriptional termination in additional organisms and potential roles of this phenomenon in gene expression remain an active research area.RNAPII density profiles across genes typically show two peaks: a first peak near the TSSs, and a second peak near PASs. The peak at PASs reflects a reduction of RNAPII movement that aids nascent transcript cleavage and polyadenylation by eukaryotic cleavage and polyadenylation specificity factors (CPSF) and cleavage stimulation factors (CstF)33. Although antisense lncRNA represent common gene isoforms, other gene isoforms such as divergent lncRNA are less frequent compared to metazoans34. Chromatin-based repression of intragenic RNAPII initiation relies on histone chaperone activity of the FACT complex. However, repression of intragenic TSSs during RNAPII elongation in plants is associated with histone 3 lysine 4 mono-methylation (H3K4me1) rather than histone 3 lysine 36 tri-methylation (H3K36me3), highlighting intriguing diversity in the use of chromatin-based signalling between organisms35. Plants and metazoans form equivalent CPSF/CstF protein complexes to mediate polyadenylation of mRNA37 and integrator to mediate snRNA termination38. Similar to metazoans, nuclear exosome variants perform transcript surveillance40. An expansion of the RRM-domain RNA-binding gene family in plants specifies alternative polyadenylation sites41 and regulates key aspects of plant development42. Interestingly, plant genomes lack NELF homologs yet display an accumulation of RNAPII near gene promoters43. A strong correlation between the position of the +1 nucleosome indicates a nucleosome-defined mechanism for promoter-proximal stalling in plants34. The functional significance of promoter-proximal stalling for gene isoform expression in plants is unclear.Regulation of gene expression underpins key adjustments of sessile organisms (e.g. plants) to changing environments. In plants, a variable pattern of TSSs at gene promoters also contributes to heterogeneity of gene expression44. TIF-seq resolves transcript heterogeneity in budding yeast, representing a small eukaryotic genome with low average transcript length45.Eukaryotic primary transcripts may extend up to a couple of megabases and provide extensive opportunities for co-transcriptional regulation of gene isoform diversity. Commonly used transcriptomics methods based on short-read sequencing detect either the TSS or the PAS of a given transcript. The connections between alternative TSSs to variable PASs that are key for gene isoform diversity thus remain poorly understood. Transcript Isoform sequencing (TIF-seq) brings TSS/PAS pairs of individual transcript molecules into close proximity during next-generation sequencing library construction to enable the identification of both RNA endsArabidopsis thaliana using an improved TIF-seq protocol suitable for larger genomes. TIF-seq data suggest on average over four isoforms corresponding to variable TSS/PAS pairs per expressed gene across different environmental conditions. TIF-seq in Arabidopsis nuclear exosome mutants reveals the cryptic transcriptome, in particular short promoter-proximal RNAs (sppRNAs) from nascent transcript cleavage and polyadenylation shortly after initiation. The location of sppRNAs coincides with promoter-proximal RNAPII stalling. These data connect promoter-proximal RNAPII stalling and transcriptional termination in plants, with important implications for the regulation of gene expression in biotechnology.Here, we define TSS/PAS pairs for individual transcripts in the model plant Arabidopsis thaliana seedlings using an improved TIF-seq protocol45 46. Overall, TIF-seq data correlated well to data mapping each transcript boundary separately  gene . However, it remained unclear whether intragenic initiation repressed by FACT generates mRNA isoforms, or perhaps shorter variants. We performed TIF-seq in mutants of both Arabidopsis FACT subunits to resolve transcripts from intragenic initiation more clearly. Biological repeats of TIF-seq libraries for spt16-1 (r = 0.99) and ssrp1-2 (r = 0.97) showed high correlation  display defective exosome activity40. We performed TIF-seq in the hen2-2 mutant to map cryptic transcript isoforms in Arabidopsis. Biological repeats of hen2-2 TIF-seq libraries correlated well  locus  followed by northern blotting  and hen2-2 (r = 0.99) were correlated, indicated reproducibility between repeats  analyses revealed that a significant number of genes with sppRNAs in ambient temperature are cold-regulated  fluorescence following transient expression in N. benthamiana leaves in constructs with wild-type 5′-UTR sequence and mutant versions carrying deletions of the sppRNA sequences. Constructs carrying deletions of sppRNA sequences (ΔsppRNA) visually reduced YFP fluorescence compared to the wild-type 5′-UTRs  map within peaks of RNAPII occupancy  data for genes with detectable sppRNAsncy Fig. . We alsoncy Fig. 43. Theseity Fig. . Overallity Fig. .Fig. 5RN48. Our analyses of promoter elements revealed an enrichment of two motifs for sppRNA genes : cdkc2-2 (ref. 53). RT-qPCR analyses of sppRNA generated from five loci revealed that sppRNA levels increased in cdkc2-2 compared to wild type  gene54, which encodes a conserved subunit of the PAF-I complex. These data are consistent with RNAPII elongation repressing sppRNA formation. Thus, EF activity may help to bypass a gene expression checkpoint associated with sppRNA formation.Premature transcriptional termination of RNAPII transcription shortly after transcriptional initiation would reconcile the position of sppRNA PASs. The coincidence of sppRNA formation and promoter-proximal RNAPII stalling suggested co-transcriptional regulation by transcription elongation factors (EFs). p-TEFb phosphorylates RNAPII and associated factors to drive elongationype Fig. . Notablyip5 Fig. , a mutanArabidopsis Cleavage Stimulation Factor 64 (CstF64) on sppRNA 3′-end formation we assayed sppRNA and mRNA in the cstf64-2 mutant42. Even though sppRNA are polyadenylated, we could not resolve decreased sppRNA in ctsf64-2 compared to wild type  site usage consistent with usage of distal sites  and CPSF100 (ref. 56). We detected higher mRNA expression at sppRNA genes compared to the control set specific to the two cstf/cpsf mutants  genes, respectively38. We detected decreased sppRNA formation, suggesting that the Arabidopsis Integrator complex promotes sppRNA 3′-end formation  tails that are associated with the activities of eukaryotic CPSF/CstF 3′-end formation complexes. To test the contribution of ype Fig. . Howevertes Fig. . Perhapsnts Fig. . These dion Fig. . At four45 revealed transcript isoform diversity of four genotypes at ambient temperature and two genotypes during cold treatment, totalling 12 TIF-seq libraries. Remarkably, we identified four transcript isoforms per expressed gene on average, insensitive to temperature variations, defective chromatin assembly and defective nuclear RNA degradation. A strength of TIF-seq is the ability to capture transcript isoform diversity arising through heterogeneous transcript ends. We note that TIF-seq would miss isoform diversity that arises in the middle of transcripts, for example through alternative pre-mRNA splicing19. Moreover, transcripts carrying epitranscriptome variations such as alternative 5′-cap structures57 may be incompatible with our sequencing library strategy. The combination of TIF-seq complemented by long read RNA sequencing methods appears suitable to gain powerful insight into transcript isoform diversity58. The synergies offered by TIF-seq and long read sequencing would enhance transcriptome annotations in non-model species beyond RNA-seq, that currently represents the starting point for investigation, for example in many plant species59.Our improved TIF-seq protocol for genomes larger than budding yeast2. The largest fraction of transcript variants detected by TIF-seq contained the full ORF, yet displayed variable 5′-UTR or 3′-UTR lengths. In metazoans, 3′-UTRs regulate protein function by several mechanisms60. The differences in 3′-UTRs we detected by TIF-seq could thus reflect a largely unexplored layer of regulation. Likewise, variability in 5′-UTRs can affect RNA structure, which may regulate gene activity through effects on translation6. We note that expression strategies for heterologous expression in biotechnology often lack endogenous UTR variations, potentially limiting beneficial properties for expression61. Our transcriptome annotation by TIF-seq provides the platform for future studies characterising the functional significance of variations in UTRs that distinguish plant transcript isoforms.Our data inform the debate of what we should consider a gene3. TIF-seq clarified that transcripts initiating within gene bodies usually extend to the predicted gene PAS. However, the contributions of intragenic initiation to plant proteome diversity remain unresolved, and these transcript isoforms may be targets for cellular RNA degradation pathways62.We detected intragenic transcription initiation as second largest source of isoform diversity. This type of isoform variation would encode a protein lacking N-terminal domains that often determine intracellular localisationArabidopsis FACT complex represses intragenic TSSs35. TIF-seq data showed that FACT-repressed intragenic TSSs often extend to the canonical PASs. Repression of intragenic initiation of RNAPII transcription by FACT thus results in transcript isoforms that potentially encode proteins lacking N-terminal domains. These data support chromatin-based regulation of the transcript isoform diversity derived from a single gene. FACT represses intragenic TSSs also in yeast and metazoans64, supporting the idea that our results may help to understand gene isoform regulation by FACT in additional organisms. FACT mutants affect plant growth and development66, perhaps reflecting that regulation of intragenic initiation by FACT assists cellular decisions needed to execute plant developmental programmes.TSS-seq data previously suggested that the hen2-2). However, for some genes we detected sppRNA in TIF-seq libraries generated from wild type. sppRNAs co-localise with promoter-proximal stalled RNAPII complexes. The function of stalled RNAPII complexes is unclear; they may prevent new acts of initiation67 and limit full-length isoform production by regulated pause-release mechanisms25. In mammals, RNAPII pauses shortly after the TSSs, and then stalls again further downstream near the position of the +1 nucleosome68. Notably, the extent to which RNAPII accumulation near metazoan TSSs represents transient transcriptional pausing or dynamic RNAPII turnover, perhaps through transcriptional termination, is actively debated69.TIF-seq data revealed alternative 3′-end formation sites close to TSSs that identified sppRNA. We note that most sppRNA represent cryptic transcripts, since sppRNA accumulate in nuclear exosome mutants  tails, our data thus support a biogenesis mechanism involving CstF/CPSF complexes. However, the Integrator complex assists sppRNA formation. The Integrator complex terminates transcription at unstable promoter-proximal pausing sites in metazoans30. Interestingly, our cis-element analyses identified the GAGA motif at sppRNA genes in plants. This motif affects metazoan RNAPII promoter-proximal pausing; albeit at a position further upstream48. In conclusion, our data identifies promoter-proximal termination of RNAPII transcription in plants that bears some tantalising parallels to a perhaps equivalent process in metazoans.Nucleosome-defined RNAPII stalling is dominant in sppRNA detection supports transcriptional termination as the outcome of RNAPII stalling near the +1 nucleosome at many plant promoters. We tested for gene regulation by selective sppRNA formation at the expense of mRNA formation upon cold perception. Although these analyses were consistent with gene regulation at some selected loci, sppRNA formation largely correlated positively with RNAPII transcription and full-length gene isoform formation. Consistently, targeted deletion of sppRNA sequences in the 5′-UTR reduced reporter gene activity. On the other hand, our analyses of sppRNA/mRNA ratios in mutants defective in 3′-end formation pathways argue that sppRNA formation may limit full-length mRNA expression. Promoter-proximal RNAPII transcriptional termination resulting in sppRNA and associated RNAPII turnover may positively contribute to plant gene expression, perhaps by maintaining NDRs near TSSs to facilitate new rounds of transcription initiation. On the other hand, sppRNA formation reduces mRNA formation by limiting progression of a fraction of initiated RNAPII complexes. Although this reflects negative regulation of full-length mRNA isoforms, perhaps RNAPII complexes that fail quality control checkpoints trigger sppRNA formation to promote the quality of full-length mRNA expression. Elucidating the potential roles of sppRNA in gene regulation will remain a topic for future studies.Our data are consistent with the idea that sppRNA result from stalled RNAPII complexes failing to enter productive elongation, perhaps representing a checkpoint for plant RNAPII transcription during early transcriptional elongation. In summary, our analyses of isoform diversity in plants inform on the debate about peaks of RNAPII occupancy shortly after TSSs, suggesting they often represent RNAPII complexes engaged in premature transcriptional termination.A. thaliana lines used in this study are listed in Supplementary Table Arabidopsis seeds were grown on plates containing 1/2 Murashige and Skoog (MS) medium containing 1% sucrose and supplemented with 1% Microagar. Seeds were stratified in the dark at 4 °C for 48 h. Plates were then transferred to climate chambers with a long day photoperiod (16 h light/8 h dark cycle) at 22 °C/18 °C and grown for 2 weeks. Light intensity during control growth conditions was ~100 μE m−2 s−1. The Columbia accession Col-0 was used as wild-type background for all experiments. For cold treatment, seedlings were grown in control conditions for two weeks and subsequently transferred to a cold room (4 °C) with light for 3 h before harvesting.All A. thaliana were mapped by an optimised version of TIF-seq44. The TIF-seq approach was modified in order to better detect corresponding TSS/PAS pairs in organism more complex than that of budding yeast Saccharomyces cerevisiae. Most notably, the make-up and orientation of sequencing adapters was improved to effectively double the return of map-able reads. The detailed protocol will be published elsewhere70. Here we describe its application to A. thaliana. Briefly, total Arabidopsis RNA was isolated using the RNeasy Plant Mini Kit (Qiagen) and treated with Turbo DNase (ThermoFisher Scientific) according to manufacturers’ instructions. DNase-treated RNA was recovered by acid–phenol extraction and ethanol precipitation. RNA integrity was assessed using the 2100 Bioanalyzer RNA 6000 Nano assay (Agilent). 10 micrograms of DNase-treated total RNA was treated with CIP (NEB) according to manufacturer’s instructions in order to remove all non-capped RNA species in the sample. RNA was recovered by acid–phenol extraction and ethanol precipitation. Next, 5′ caps were removed using Cap-Clip (CellScript). RNA was quickly recovered by acid–phenol extraction and ethanol precipitation. The single-stranded rP5_RND adapter (see Supplementary Data NotI (NEB) for 30 min at 37 °C. The enzyme was inactivated at 65 °C. Digested cDNA was recovered using AMPure XP beads (Beckman Coulter). Digested linear cDNA pools were diluted to <1 ng/μl and circularised using T4 DNA ligase (NEB) at 16 °C overnight. PlasmidSafe™ DNase (Cambio) was added for 30 min at 37 °C to remove linear DNA molecules from the sample and then inactivated at 70 °C for 30 min. Circular cDNA molecules were recovered by phenol–chloroform extraction followed by isopropanol precipitation. Circular cDNA was sheared by sonication at 4 °C in a Bioruptor (Diagenode) . Sheared DNA fragments were purified with AMPure XP beads (Beckmann Coulter). Biotinylated fragments containing flanking TSS/PAS pairs were captured with M-280 Streptavidin Dynabeads (ThermoFisher Scientific), end repaired with End Repair Enzyme mix (NEB), A-tailed with Klenow fragment exo- (NEB), and ligated to a common Illumina-compatible forked adapter using T4 DNA ligase (NEB) according to manufacturers’ instructions. Libraries were amplified using the Phusion High-Fidelity PCR mix (NEB) with the following PCR steps: 98 °C for 1 min, 15 cycles , 5 min at 72 °C. Libraries were size selected using AMPure XP beads (Beckman Coulter) and sequenced with the following flowcell: FC-404-2002 NextSeq 500/550 High Output v2 kit (150 cycles) (Illumina). The sequencing run was carried out using a mix 1:1 of custom oligos SeqR1 + 15T and SeqR2 for sequence reads 1 and 2, and of custom oligos SeqINDX and TIF3-RvRT for the index reads 1 and 2 . Here, we extend our previous TSS-seq analyses to 2-week-old hen2-2 seedlings before and after cold treatment (3 h at 4 °C) in order to correct for TIF-TSS bias . All TSS-seq experiments were performed as independent duplicates.TSSs were mapped genome-wide in Arabidopsis seedlings using Plant RNeasy Mini-Kits (Qiagen) and treated with Turbo DNase-treated (Ambion) as per manufacturers’ instructions. For selective quantitative analysis of mRNAs and sppRNAs, first-strand complementary DNA (cDNA) synthesis was performed on RNA using an oligo(dT)-containing primer (see Supplementary Data ACT2). Relative expression to wild type of mRNAs and sppRNAs were calculated with normalisation to ACT2 mRNA. Error bars reflect SEM resulting from at least three independent replicates. See source data file for values used to construct plots.RNA was isolated from two-week old 35 with minor modifications. Briefly, 10 μg of total RNA was separated by electrophoresis on agarose–formaldehyde–MOPS gels and transferred to a nylon transfer membrane by capillary blotting in 6× SSC overnight. RNA was crosslinked to the nylon membrane by UV irradiation using a Stratalinker UV Crosslinker (Stratagene). Membranes were prehybridised in Church Buffer  for 1 h at 68 °C. Next, membranes were probed overnight at 68 °C with single-stranded DNA probes generated by PCR-based amplification and incorporation of radioactive α-32P-dTTP (PerkinElmer). Membranes were washed twice in pre-warmed buffer containing 2× SSC and 0.1% SDS for 10 min (68 °C) before exposure to Storage Phosphor Screens (Kodak). A Typhoon phosphor-imager  was used for analysis. For loading controls, membranes were stripped three times with a near boiling buffer containing 0.1% SDS before prehybridization in Church Buffer as above and northern blotting against the mRNA of a reference gene lacking detectable sppRNAs (UBI). For northern analysis in Fig. Northern analysis in Fig. 72. The promoter region (~1 kb) and the first exon until the ATG start codon of MPK20 (pMPK20) and ACT8 (pACT8) were amplified from Arabidopsis wild-type genomic DNA by PCR. The PCR products were ligated to the NotI-HF and AscI digested pENTR-D-Topo vector through isothermal assembly to yield entry clones. Short promoter-proximal RNA (sppRNAs) deletion constructs were generated from entry clones. The 58 bp sppRNA region in the first exon of the MPK20 5′-UTR was deleted (pMPK20-ΔsppRNA), and the 64 bp sppRNA region was deleted in the ACT8 5′-UTRs (pACT8-ΔsppRNA). Entry vectors were used in Invitrogen, Gateway LR Clonase II reaction according to manufacturer conditions with pGWB540 (enhanced YFP) to generate expression vectors: pMPK20-eYFP, pΔMPK20-eYFP, pACT8-eYFP and pΔACT8-eYFP. The expression vectors were transformed into Agrobacterium tumefaciens strain GV3850 by electroporation under 2.5 kV, 400 Ω and 25 μF. Agrobacteria containing expression vectors were respectively co-infiltrated with the p19 suppressor of silencing into Nicotiana benthamiana leaves. The eYFP signal was assayed on the third day after infiltration.Transient translations of eYFP reporter constructs were generated based on pGWB540 vectors35. For TIF-seq, paired-end sequencing reads were demultiplexed and concatenated according to the indexes adjacent to 5′- or 3′-ends of transcript molecules. Reads were then subjected to adapter- and UMI-trimming with respectively cutadapt v1.18  and UMI-Tools v0.5.5. Extra stretches of As near the polyadenylation sites were removed from reads using cutadapt v1.18. Next, reads containing 10-nt-long stretches of As and Ts with one mismatch were removed using AfterQC v0.9.7. The adapter-, UMI- and polyX-trimmed reads were aligned to TAIR10 genome assembly using STAR v2.6.1c . The output sorted BAM files were filtered for uniquely aligned reads using SAMtools v1.7. Finally, the filtered BAM files were deduplicated using UMI-Tools dedup.py. The two replicate alignments of each data set were compared using deepTools multiBamsummary, calculating a coverage matrix between replicates (--binSize 10). The Pearson correlation for each matrix was plotted with the deepTools plotCorrelation (--corMethod pearson --log1p and –removeOutliers) in Supplementary Figs. Computational analysis of TSS-seq data analysis was performed as previously described44, the 5′ reads of TIF-seq were used to call TIF-TSSs for each data set using the CAGEfightR package v1.0.0 package available from Bioconductor [https://bioconductor.org/packages/release/bioc/html/CAGEfightR.html] and filtered out with no overlap to a previously defined TSS called from published TSS-seq data35. The TSS peak distance to the annotated TSSs called with CAGEfightR for TSS-seq and TIF-seq in wild type, representing the TIF-TSSs bias towards small clusters overlapping the end of genes, later removed, is shown in Supplementary Fig. Each pair of reads aligned to the genome was clustered with the custom script “TIF-Seq_isoform_clustering_v6.py” as follows: “Pre-clusters” were called and read pairs overlapping exactly at both ends were merged. Next, Pre-clusters were merged if both start and end positions overlapped another existing “Pre-cluster” by <20 nucleotides. Subsisting merged pre-clusters were kept as final clusters only if their pairs of reads count was equal or higher than 3. The remainder were discarded. To further remove clusters produced by accidental 3′-end mispriming, clusters with extended end positions overlapping genomic polyA regions (from 6 to 9 As) called with bowtie aligner were discarded. Due to artefactual bias of TIF-seq called TSSs towards genic 3′-ends, as previously describedhen2-2 but only on clusters with widths <250 nucleotides . To analyse FACT TIF-clusters, we combined the two FACT mutants ssrp1-2 and spt16-1 TIF-seq data sets and extracted the fact-specific TIF-Seq clusters overlapping published fact-specific and upregulated TSSs—based on TSS-seq35. In Fig. hen2-2 and in cold-treated wild type and cold hen2-2 in Figs. hen2-2 was calculated in Fig. hen2-2 in Supplementary Fig. For all further genome-wide clusters analysis, measurements considered only non-overlapping genes to avoid false-positive calculations while accepting to lose some information. In Figs. To extract small promoter-proximal clusters, all clusters <350 nucleotides were mapped to annotated TSSs extended by 40 nucleotides upstream and downstream and to overlap no termination sites to account for TSS variation. The corresponding set of overlapping clusters were then called short promoter-proximal RNAs  and 200 nucleotides upstream of the PAS (−200 from TSS) to reduce biases from recently reported peaks in nascent transcription signals near TSS-proximal or PAS-proximal positions43. The percentage of cold-induced genes  that only have sppRNAs at 22 °C was compared against a control set of genes without detectable sppRNAs in Supplementary Fig. hen2-2 data sets before and after cold treatment were plotted as a Venn Diagram (cf script “ViennDiagram.R”).All further analysis requiring expression controls were calculated using transcription profiles of published unphosphorylated RNAPII (8WG16) pNET-seq wild-type data setscis-elements in promoter DNA sequences and gene body sequences. Promoter DNA sequences were defined as 300 nt upstream and 50 nt downstream of the measured TSS, and downstream sequences were defined as 20 nt upstream of the gene TSS and 250 nt downstream. Enriched cis-elements between the two control sets were identified with MEME suite DREME and RSAT web tools by differential comparison of sequences regions for sppRNA genes and control genes of similar nascent transcription distribution enrichment and p-values in source data file. The main TSSs were used as anchor points in the metagene plots in Fig. cis-elements heatmaps in Fig. 35. In Fig. 43 by dividing the promoter TPM coverage from pNET-seq (150 nt upstream and downstream around the TSS) to the gene body (shrunk gene region by 300 nt downstream of the TSS and 300 nt upstream of the PAS). Full-length mRNA expression was compared between mutants by measuring TPM at genes annotated PAS site from PAT-seq experiments. A specific control set of genes with equal distribution to sppRNA genes was calculated with the corresponding experiment wild-type samples, then full-length expressions in TPM were compared by Wilcoxon test between the control set of genes and sppRNA genes in the mutant sample and in wild type in Fig. The distribution of nascent transcriptional activity over sppRNA genes was compared to all other genes with no sppRNAs in wild type in Fig. A. thaliana (TAIR10) annotation. In particular, TIF-seq reads  were visualised with the following settings: collapsed, view as pairs, colour alignments by read strand.Finally, all genome browser screenshots were made using Integrative Genomics Viewer (IGV_2.3.93) with the Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting summary


Transcription of protein-encoding genes starts with forming a pre-initiation complex comprised of RNA polymerase II and several general transcription factors. To activate gene expression, transcription factors must overcome repressive chromatin structure, which is accomplished with multiprotein complexes. One such complex, SAGA, modifies the nucleosomal histones through acetylation and other histone modifications. A prototypical histone acetyltransferase (HAT) known as general control non-repressed protein 5 (GCN5), was defined biochemically as the first transcription-linked HAT with specificity for histone H3 lysine 14. In this review, we analyze the components of the putative plant SAGA complex during plant evolution, and current knowledge on the biological role of the key components of the HAT module, GCN5 and ADA2b in plants, will be summarized. Saccharomyces cerevisiae .,23.Arabi mutants . Moreovec stress ,26,27. Tmutation . Mutatioto ADA2b ,31, suggArabidopsis are not crucial for proper plant development . Alternatively, a new mechanism has been recently proposed . F. FSPT mugcn5 mutants have reduced expression of light-responsive genes like CAB2 and RBCS1A, and H3 acetylation, especially for H3K14, is reduced in the promoter regions of those genes. GCN5 was found to be enriched on those genes suggesting that they are direct targets . In light-responsive gene expression, GCN5 acts as a positive regulator;  targets . Upon ir of FRD3 . Similar of FRD3 . The fir of FRD3 ,78,79. A of FRD3  independ of FRD3 . Further of GCN5 . ADA2b i of GCN5 . Moreove of GCN5 . Finallyolerance . GCN5 isn levels . Interesficiency , salt [8ficiency , heat [8ficiency , and phoficiency , suggestMYC2, DND2, and WRKY33, and promotes their expression. In GCN5 mutants, the reduced acetylation of MYC2, DND2, and WRKY33 results in reduced expression of the genes and increased biosynthesis of SA [Phytophthora sojae, PsAvh23, suppresses H3K9ac mediated by ADA2 and GCN5 and increases plant susceptibility [GCN5-mediated histone acetylation is required in the integration of hormone signals during stress responses. In poplar trees, GCN5 and ADA2b are recruited by the transcription factor AREB1 to the promoter of drought and ABA-responsive genes, resulting in increased H3K9 acetylation levels . GCN5 anis of SA . In soybtibility . These r


PPARγ2) is a nuclear hormone receptor of ligand-dependent transcription factor with a key role in adipogenesis and insulin sensitization in diabetes mellitus. In this study, we investigated genetic variants in PPARγ2 promoter, its association with gestational diabetes mellitus (GDM), and its molecular regulation. PPARγ2 promoter and start codon  from 400 pregnancies with GDM and 400 gestational-age matched control pregnancies were sequenced. Association and linkage disequilibrium of the identified polymorphisms with GDM was determined. ChIP-seq, gene silencing, and dual-luciferase reporter assays were performed to confirm transcription factor binding sites and promoter activity of the variants. Transfection experiments were carried out to determine the effects of variants on gene expression and adipogenesis. Among 15 variants identified, 7 known variants were not significantly associated with the risk of GDM  while linkage disequilibrium was significant . However, T-A-A-T-G haplotype was not significantly associated with GDM . Five rare variants and 3 novel variants  were only found in GDM. Transcription factor glucocorticoid receptor β (GRβ) bound to -807A/C (rs948820149) and knockdown of GRβ suppressed PPARγ2 promoter activity. This mutation significantly down-regulated PPARγ2 expression and alleviated adipogenesis. In conclusion, a novel -807A/C in PPARγ2 promoter was identified in Chinese women with GDM and the mutation affected GRβ binding and transcription of PPARγ2 for adipogenesis.Peroxisome proliferator-activated receptor γ2 ( TCF7L2) (γ (PPARγ) (IRS1), Melatonin Receptor 1B (MTNR1B) (GCK), Insulin Like Growth Factor 2 mRNA Binding Protein 2 (IGF2BP2), Potassium Voltage-Gated Channel Subfamily J Member 11 (KCNJ11), Potassium Voltage-Gated Channel Subfamily Q Member 1 (KCNQ1) (CDKAL1) are involved in both GDM and T2DM risk, their effect sizes for the risk vary in different genes.Gestational diabetes mellitus (GDM) is defined as the first recognition of glucose intolerance during pregnancy. Interaction between genetic and environmental factors plays an important role in the development of GDM –3. GenomTCF7L2) , 10, Per (PPARγ) , Insulin(MTNR1B) , 13, Glu (KCNQ1) , 15, andPPARγ is a nuclear hormone receptor which regulates cell growth, differentiation and metabolism in different organs and tissues  which has extra 28 amino acids at NH2-terminal, leading to 5- to 10-fold ligand-independent activation. PPARγ2 expression is mainly identified in adipocytes under physiological conditions , insulin sensitivity and low risk of T2DM  of  of T2DM –25. In aulations , 27. HowPPARγ2, variants in promoter region may also affect the gene expression and its function. However, there is no data available in GDM. In this study, we investigated polymorphisms of PPARγ2 promoter and exon B regions in Chinese pregnant women complicated with GDM. Novel genetic variants were identified only in GDM and its molecular regulation in gene expression and adipogenesis was studied.Exon B is near the start codon of 2 and polycystic ovary syndrome) were excluded. The whole blood samples collected during OGTT were stored in -80°C prior to analysis.Pregnant women at second trimester attended antenatal clinic of the Third Affiliated Hospital of Sun Yat-Sen University in Guangzhou between September 2013 and September 2015 for oral glucose tolerance test (OGTT) were included. The prevalence of GDM during the study period was 16.8%. The present study was approved by Ethics Committee of the Third Affiliated Hospital of Sun Yat-Sen University. All experimental procedures were performed in accordance with the relevant guidelines and regulations. Written informed consent was obtained from each patient to collect extra blood samples for genetic test. Diagnostic criteria of GDM was based on 75 g OGTT according to the International Association of the Diabetes and Pregnancy Study Groups . GDM werPPARγ2 were sequenced by Sanger sequencing. Genomic DNA was extracted from the whole blood sample by Magnetic Beads Automatic DNA Extraction Apparatus . DNA quality was checked by NanoDrop 1000 Spectrophotometer . DNA with A260/A280 ratio within 1.6–1.8 was regarded as high quality for sequencing. Sequencing primers were designed by Primer3 version 4.0.0. Four pairs of primers covering the whole promoter and exon B regions were used (PPARγ2 in Ensemble Human Genome Assembly (GRCh38.p12). Minor allele frequency (MAF) was calculated as the frequency of less frequent allele in a given locus and a given population. Known variant was defined as variant that has been previously identified and reported in public database, such as dbSNP. Novel variant was defined as a newly discovered variant and has never been reported anywhere. Common variant was defined as the variant with minor allele frequency (MAF) >5%; rare variant as the known variant with minor allele frequency (MAF) <0.5% .Promoter (chromosome 3:12351302∼12351501) and exon B regions (chromosome 3:12351502∼12351637) of F) <0.5% . Pooled PPARγ2 was detected in both cultivated HEK293 cells and differentiated HPA-s cells. HEK293 cells were used for transcription factor siRNA silencing and luciferase reporter assays, and HPA-s cells were used for chromatin immunoprecipitation (ChIP)-PCR and transfection and functional studies.Cell lines were used for molecular and functional studies of the identified variants. Human Embryonic Kidney cells (HEK293) were purchased from American Type Culture Collection  and cultured in DMEM (Thermo Fisher Scientific) supplemented with 10% fetal bovine serum  and 1% penicillin and streptomycin (Sigma-Aldrich). Human pre-adipocytes-subcutaneous (HPA-s) cells were purchased from ScienCell , cultured in Pre-adipocyte Medium supplemented with 10% FBS and 1% penicillin and streptomycin, and differentiated into mature adipocytes in Pre-adipocyte Differentiation Medium supplemented with 10% FBS and 1% PS according to previous report . ExpressTo study transcription factors of the variants, transcription factor binding sites were predicted by PROMO 3.0.2 . To confst and 2nd Strand cDNA Kit . PPARγ2 expression were quantified using Taqman Primers (Thermo Fisher Scientific) by a PCR system  using SYBR Green Real-Time PCR Master Mixes (Takara). The relative mRNA expression of PPARγ2 was normalized by GAPDH and calculated using the 2-ΔΔCt method.To silence the specific transcription factors of the variants, small interfering RNAs (siRNAs) targeting the transcription factors were purchased from RiboBio . The reference sequences were obtained from genOFF™ siRNA library, and sequences of the siRNAs are shown in PPARγ. GRβ vector NC and PGL3 with PPARγ-Promoter of the wild type or mutant allele were co-transfected into HEK293 cells at 80% confluent by Lipofectamine 2000 reagent (Invitrogen). At 72 h after transfection, dual-luciferase reporter activity was determined by adding Luciferase Assay Buffer II and Stop & Glo® Reagent using Dual Luciferase Reporter Assay kits  according to manufacturer’s instruction. The firefly luciferase activity and Renilla luciferase activity were measured in EnSpire™ ultra-sensitive luminescence reader .Firefly luciferase vector PGL3  was used to examine promoter activity of the variants. The putative promoter region with DNA fragment of wild type or mutant allele of ~982 bp length  was inserted to upstream of luciferin gene. Human NR3C1 CDS with 2,369 bp was inserted in pcDNA3.1 [EcoRI/XhoI] to over-express PPARγ2 were inserted to the upstream of PGK promoter. HPA-s cells at 70–80% confluence in 96-well plate were transfected with the pLVX-PGK-Puro vector at 1 to 100 multiplicity of infection , and puromycin was used for transductant selection . The putative promoter region with DNA fragment of wild type allele or risk allele and the whole CCDS of election .After 3 days of puromycin selection, HPA-s cells were evaluated by Oil red staining. Briefly, the cells were washed with ice-cold phosphate buffered saline (PBS) for 3 times and fixed with 10% formalin at room temperature for 30 min. After that, the cells were rinsed with 60% isopropanol and were then stained with Oil Red O solution (Sigma-Aldrich) for 30 min followed by washing with 60% isopropanol. The stained cells were then imaged under a light microscope.P <0.05. Linkage disequilibrium was carried out by Haploview software, D’ > 0.7 and R2 > 0.33 were referred to strong linkage disequilibrium. Genetic analysis was conducted by Sequence Scanner Software v1.0 (Applera Corp.).Data analysis was performed using the SPSS 27.0  and GraphPad Prism Software Version 5.0 . The continuous data were presented as mean ± standard deviation, and significant differences between different groups were analyzed by unpaired Student’s t-test or one-way ANOVA followed by the Bonferroni’s post-hoc test, as appropriate. For the categorical data, the significant difference was analyzed using Chi-square test. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Statistical significant level is set at In control group, 5 cases were excluded due to twin pregnancy, and duplicated and invalid samples. In GDM group, 2 cases were excluded due to the diagnosis of overt T1DM after delivery. Maternal age, pre-pregnancy BMI, glucose levels, fasting cholesterol and triglyceride levels were significantly higher and high-density lipoprotein level was significantly lower in GDM group than that in the control group  showed significant linkage disequilibrium , GRβ, TATA binding protein-1 (TFIID-1), TATA binding protein-2 (TFIID-2), CCAAT enhancer binding protein alpha , CCAAT enhancer binding protein beta (C/EBP beta), general transcription factor II I (TFII-1), homeobox D10 (HOXD10), signal transducer and activator of transcription 4 (STAT4) and forkhead box A1 , were predicted to bind to the mutant allele , MOI = 50 (P = 0.0178) and MOI = 100 (P = 0.0007)  have significant linkage disequilibrium but not significantly associated with the risk of GDM. Importantly, 3 novel SNPs  were detected only in GDM group. While -807A/C variant (rs948820149) bound to the transcription factor GRβ and regulated the transcription of PPARγ2 in adipogenesis. Identification of genetic variants may allow early intervention in pre-conception or early pregnancy period in order to prevent maternal and pre-/post-natal complications.In conclusion, the common genetic variants in promoter and start codon region of All datasets generated/analyzed for this study are included in the article/The studies involving human participants were reviewed and approved by The Third Affiliated Hospital of Sun Yat-Sen University. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.LW, CCW, and TT designed the research studies. YZ, YCY, and HYH provided blood samples. LW, YS, BL, and YD conducted experiments. LW and YS acquired data and analyzed data. LW and CCW wrote the manuscript. CCW revised 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.