The Long interspersed nuclear element 1 (LINE-1) is a primary source of genetic variation in humans and other mammals. Despite its importance, LINE-1 activity remains difficult to study because of its highly repetitive nature. Here, we developed and validated a method called TeXP to gauge LINE-1 activity accurately. TeXP builds mappability signatures from LINE-1 subfamilies to deconvolve the effect of pervasive transcription from autonomous LINE-1 activity. In particular, it apportions the multiple reads aligned to the many LINE-1 instances in the genome into these two categories. Using our method, we evaluated well-established cell lines, cell-line compartments and healthy tissues and found that the vast majority (91.7%) of transcriptome reads overlapping LINE-1 derive from pervasive transcription. We validated TeXP by independently estimating the levels of LINE-1 autonomous transcription using ddPCR, finding high concordance. Next, we applied our method to comprehensively measure LINE-1 activity across healthy somatic cells, while backing out the effect of pervasive transcription. Unexpectedly, we found that LINE-1 activity is present in many normal somatic cells. This finding contrasts with earlier studies showing that LINE-1 has limited activity in healthy somatic tissues, except for neuroprogenitor cells. Interestingly, we found that the amount of LINE-1 activity was associated with the with the amount of cell turnover, with tissues with low cell turnover rates  showing lower LINE-1 activity. Altogether, our results show how accounting for pervasive transcription is critical to accurately quantify the activity of highly repetitive regions of the human genome. Repetitive sequences, such as LINEs, comprise more than half of the human genome. Due to their repetitive nature, LINEs are hard to grasp. In particular, we find that pervasive transcription is a major confounding factor in transcriptome data. We observe that, on average, more than 90% of LINE signal derives from pervasive transcription. To investigate this issue, we developed and validated a new method called TeXP. TeXP accounts and removes the effects of pervasive transcription when quantifying LINE activity. Our method uses the broad distribution of LINEs to estimate the effects of pervasive transcription. Using TeXP, we processed thousands of transcriptome datasets to uniformly, and unbiasedly measure LINE-1 activity across healthy somatic cells. By removing the pervasive transcription component, we find that (1) LINE-1 is broadly expressed in healthy somatic tissues; (2) Adult brain show small levels of LINE transcription and; (3) LINE-1 transcription level is correlated with tissue cell turnover. Our method thus offers insights into how repetitive sequences and influenced by pervasive transcription. Moreover, we uncover the activity of LINE-1 in somatic tissues at an unmatched scale. Long interspersed nuclear element 1 (LINE-1) has attracted much attention in the last decade due to its capacity to promote genetic plasticity of the human genome. LINE-1 is a DNA sequence capable of duplicating itself and other DNA sequences by mobilizing messenger RNAs (mRNAs) to new genomic locations via retrotransposition . There aAs opposed to healthy tissues, tumor and tumor derived cell lines show higher levels of LINE-1 activity . LINE-1 How much pervasive transcription influences the human transcriptome is still unclear –29. SomeThis paper presents a new method to remove the effect of pervasive transcription on RNA sequencing datasets and reliably quantify LINE-1 subfamily transcriptional activity. We first show that the vast majority of reads overlapping LINE-1 elements are derived from pervasive transcription and propose a method to address this issue. We validated the LINE-1 transcription landscape in well-established human cell lines and their cell compartments. Finally, we surveyed LINE-1 activity in a variety of healthy somatic tissues. Although somatic retrotransposition has been mainly studied in the human brain, we found surprisingly little transcriptional activity in most brain regions from adults. Instead, we found LINE-1 transcriptional activity in other somatic tissues consistent with an overall trend of LINE-1 activity in cell with higher turnover.Recently amplified LINE-1 subfamilies, such as L1Hs, are frequently discarded from traditional transcript quantification assays due to the insufficient mapping specificity of LINE-1 instances. Before addressing the LINE-1 multi-mappability issue, we quantified the number of reads overlapping LINE-1 subfamilies in thousands of RNA sequencing experiments from human cell lines and healthy primary tissues ,32. Fig We model pervasive transcription as sufficiently broad transcription component that creates a bias in RNA-seq reads overlapping LINE-1 subfamilies. This bias tends to be correlated to the number of instances (or bases) in the reference genome because pervasive transcription is predicted to create small quantities of reads across large portions of the genome.As opposed to the broad pervasive transcription model, under the narrow LINE-1 transcription model, we expect that a small number of LINE-1 instances to produce a much higher signal (number of reads) than the lowly expressed instances. Therefore, in order to investigate this issue, we first calculated the number of reads mapping to LINE-1 instances. We observed that, the vast majority of LINE-1 instances have a very small number of uniquely mapped reads  suggestiTo test the amount of signal generated by the top expressed instances and the lowly expressed instances we calculated the ratio of reads overlapping the most 10 expressed instances in each RNA-seq experiment and the sum of reads overlapping all instances with less than 10 reads. We find that the summation of the least expressed instances is, on average, one order of magnitude higher than the 10 mostly expressed instances. Together these results suggest that broad pervasive transcription is an important factor when quantifying LINE-1 subfamily transcription level.We then divided samples by their tissues of origin  and notiWe benchmarked TeXP by estimating the autonomous transcription of LINE-1 subfamilies in RNA sequencing experiments of well-established human cell lines . Fig 2A MCF-7, a cell line derived from breast cancer, was previously described as having remarkably high levels of L1Hs autonomous transcription ,24. The To validate the quantification of L1Hs autonomous transcription, we performed droplet digital PCR (ddPCR) to estimate autonomous and pervasive transcription levels on a reference panel of six cell lines: MCF-7, K-562, HeLa, HepG2, SK-MEL-5, and GM12878. For these experiments, we assumed that expression on the 5’ end of the L1Hs transcript can be used as an approximation to autonomous transcription due to the large imbalance of 5' truncated and full-length copies. The expression on the 3’ end, on the other hand, is an approximation to the combination of autonomous and pervasive transcription. We initially designed and tested multiple assays targeting different regions of the L1Hs locus and proceeded with the two best performing assays . The firHPRT1 5’ end as a reference. We estimate that the ddPCR analysis detected approximately 2,412.8 copies of autonomously transcribed transcripts/ng in MCF-7 cells. In agreement with our in-silico result, K562 and SK-MEL-5 had 990.0 and 1,075.6 copies of autonomously transcribed transcript/ng, respectively. For the GM12878 cell line, we expected to find no autonomous expression of L1Hs; however, our ddPCR assays detected low levels of autonomous transcription of L1Hs  immortalized cell lines and their corresponding primary tissue to understand the changes induced by cell line immortalization. EBV immortalization causes drastic changes in the expression of cell cycle, apoptosis, and alternative splicing pathways –36. OverHuman solid tumors display increased levels of LINE-1 activity –40. In oHuman tissues show remarkable variability of L1Hs autonomous transcription. We found that L1Hs autonomous transcription is inversely correlated to the time it takes cells to divide . No correlation was found between cell turnover and pervasive transcription . Tissues suggested to have low cell turnover, such as the human brain , are amoPrior to this study, the effects of pervasive transcription on the estimates of transposable elements activity were largely ignored. Here, we showed that most of the RNA-seq reads matching to LINE-1 instances derive from pervasive transcription, highlighting the importance of these effects. In order to account for the effect of pervasive transcription on the quantification of LINE-1 activity we developed TeXP, a method that uses the widespread nature and the mappability signatures of LINE-1 subfamilies to account for and remove the effects of pervasive transcription from L1Hs, L1P1, L1PA2, L1PA3, and L1PA4 subfamilies. We compared TeXP estimates to other strategies such as naïve counts and others established methods such as SalmonTE  and TETrWe used TeXP to perform a comprehensive analysis of LINE-1 transcriptional activity across different cell types and somatic tissues. Previous studies suggest that LINE-1 is active in germline and tumor cells, but not in most normal somatic cells with the exception of hints of activity in neuro-precursor cells . SomaticWe validated many aspects of TeXP using ddPCR probes designed to quantify pervasive and autonomous transcription of L1Hs across human cell lines. These results show that our method lacks the sensitivity to measure autonomous transcription. In particular, TeXP underestimates L1Hs transcription levels when the signal-to-noise ratio is small. One could imagine using unique regions of LINE-1 instances after removing the pervasive transcription signal to improve the transcriptional quantification. However, removing pervasive transcription from individual instances is not trivial and should be carefully investigated.As a tool, TeXP could be useful in several scopes beyond this study. Pervasive transcription should also affect the quantification of other transposable elements and repetitive regions accounting for large portions of the human genome. Our method could be further used to estimate the autonomous transcription levels of pseudogenes, SINEs (ALUs) and HERVs. Furthermore, TeXP could be used in model organisms to distinguish the effects of pervasive transcription beyond humans. The mouse genome, for example, has evidence of higher rates of retrotransposition but little is known about the activity of LINE-1 in somatic cells. Moreover, some of the results we describe here could be extended and uncover important biological insights. For example, assays such as induced pluripotent stem cells clones , RC-seq TeXP models the number of reads overlapping L1 elements as the composition of signals deriving from pervasive transcription and L1 autonomous transcripts from distinct L1 subfamilies.Eq 1:Our model proposes that the number of reads overlapping L1Hs instances as described by the OL1Hs is the observed number of reads mapping to L1Hs, T is the total number of reads mapped to L1 instances, GL1Hs defines the proportion of L1 bases in the genome annotated as L1Hs, εpervasive is the percentage of reads emanating from pervasive transcription, M is the mappability fingerprint (defined bellow) that describes what is the proportion of reads emanating from the signal j ∈ {L1Hs, L1P1, L1PA2, L1PA3, L1PA4} that maps to L1 subfamily i ∈ {L1Hs, L1P1, L1PA2, L1PA3, L1PA4} and ε is the percentage of reads emanating from the autonomous transcription L1 Subfamily j. This model can be further generalized as the Eq 2:Where We selected these five LINE-1 subfamilies based on the rates of cross-mappability of simulated data . In partIn contrast to L1Hs which only a fourth of the reads map back to L1Hs, older elements such as L1PA5 and L1PA6 have a much higher self-mapping rates, 60% and 70% respectively. Therefore, these subfamilies should be less affected by confounding factors deriving from pervasive transcription.Oi is measured by analyzing paired-end or single-end RNA sequencing experiments independently. TeXP extracts basic information from fastq raw files such as read length and quality encoding. Fastq files are filtered to remove homopolymer reads and low quality reads using in-house scripts and FASTX suite (http://hannonlab.cshl.edu/fastx_toolkit/). Reads are mapped to the reference genome (hg38) using bowtie2 . Multiple mapping reads are assigned to one of the best alignments. Reads overlapping LINE-1 elements from Repeat Masker annotation of hg38 are extracted and counted per subfamily. The total number of reads T is defined as T = ∑iOi.The number of reads mapped to each subfamily i ∈ {L1Hs, L1PA2, L1PA3, L1PA4, L1P1}. We define Pi as the proportion of LINE-1 bases annotated as the subfamily i in the Eq 3:Pervasive transcription is defined as the transcription of regions well beyond the boundaries of known genes . We ratihttps://github.com/lh3/wgsim-parameters:-1 [RNA-seq mean read length]–N 100000 -d0 –r0.1 -e 0). One hundred simulations are performed and reads are aligned to the human reference genome (hg38) using the same parameters described in the model session. The three-dimensional count matrix C is defined as the number of reads mapped to the subfamily i ∈ {L1Hs, L1PA2, L1PA3, L1PA4, L1P1} emanating from the set of full-length transcripts j ∈ {L1Hs, L1PA2, L1PA3, L1PA4, L1P1} in the simulation k. The matrix M is defined as the median percentage of counts across all simulations as in Eq 4:On the other had mappability fingerprints, which represents how reads deriving from LINE-1 transcripts would be mapped to the genome, are created by aligning simulated reads deriving from putative L1 transcripts from each L1 subfamily. For each L1 subfamily, we extract the sequences of instances based on RepeatMasker annotation and the reference genome (hg38). Read from putative transcripts are generated using wgsim  to maintain sparsity. We used the R package penalized  accession number phs000424.v6.p1.Raw RNA sequencing datasets from healthy tissues were obtained from Database of Genotypes and Phenotypes (DB-Gap - https://www.encodeproject.org/search). We selected RNA-seq experiments from immortalized cell lines with multiple cellular fractions and transcripts selection experiments. Accessions and cell lines are available in Raw RNA sequencing data from cell lines were obtained from the ENCODE data portal  . MCF-7 cells were cultured in Dulbecco’s Modified Eagle Medium: Nutrient Mixture F-12 . HeLa, SK-MEL-5, and HepG2 cells were cultured in Dulbecco’s Modified Eagle Medium . K562 and GM12878 cells were cultured in RPMI 1640 (Gibco). All cell culture media were supplemented with 10% fetal bovine serum (FBS) (Atlanta Biologics) and 1% penicillin/streptomycin (Fisher Scientific). All cells were cultured and expanded using the standard methods.RNA was extracted using the RNeasy PLUS Mini Kit and the QIAshredders (Qiagen) following the manufacturer’s protocol. All samples were treated with DNase I (New England BioLabs Inc.) to remove any remaining genomic DNA. RNA concentration was determined by Qubit 2.0 Fluorometer (Invitrogen). RNA quality was determined by Nanodrop (Thermo Scientific) and 2100 BioAnalyzer with the Agilent RNA 6000 Nano kit (Agilent Technologies). Approximately 5 μg of RNA was used for synthesis of the cDNA using the iScript Advanced cDNA Synthesis Kit (Bio-Rad). The final cDNA product was quantified and a working solution of 10 ng/μL was prepared for the subsequent studies.HPRT1, which targeted the 5’ and 3’ ends of the transcript, respectively  System (Bio-Rad Laboratories) was utilized to quantify the L1Hs transcript expression in the cell lines described above. Since L1Hs is a highly repetitive and heterogeneous target, we had initially designed and tested a panel of primers and probes that targeted the 5’ untranslated region (5’UTR), the open reading frame 1 (ORF1), the open reading frame 2 (ORF2), and the 3’ untranslated region (3’UTR) of the L1Hs locus, respectively. After a pilot screening study, we selected the two assays covering ORF1 and ORF2, which not only exhibited overall better performance, but also could help us to distinguish autonomous and pervasive L1Hs transcriptions. We also designed two reference assays on the housekeeping gene ectively . All theThe ddPCR reactions were performed according to the protocol provided by the manufacturer. Briefly, 10ng DNA template was mixed with the PCR Mastermix, primers, and probes to a final volume of 20 μL, followed by mixing with 60 μL of droplet generation oil to generate the droplet by the Bio-Rad QX200 Droplet Generator. After the droplets were generated, they were transferred into a 96-well PCR plate and then heat-sealed with a foil seal. PCR amplification was performed using a C1000 Touch thermal cycler and once completed, the 96-well PCR plate was loaded on the QX200 Droplet Reader. All ddPCR assays performed in this study included two normal human controls (NA12878 and NA10851) and two mouse controls (NSG and XFED/X3T3) as well as a no-template control . All samples and controls were run in duplicates. Data was analyzed utilizing the QuantaSoft analysis software provided by the manufacturer (Bio-Rad). Data were presented in copies of transcript/μL format which was mathematically normalized to copies of transcript/ng to allow for comparison between cell lines.In order to estimate the levels of pervasive and autonomous transcription using ddPCR we use the following formulation:X3′ and X5′ are the transcript/μL measurements of the 3’ and 5’ L1Hs primers; a and p are autonomous and pervasive transcription estimates, respectively; and f is the fraction of L1Hs instances that could potentially generate full-length transcripts. We estimate that f is approximately 12%. Using this model, the estimated level of L1Hs autonomous transcription using two primers and ddPCR is:Where HPRT1 transcript, respectively, and used as the reference controls in this study  instances have small amount of uniquely mapped reads (1–5) suggesting low levels of transcription throughout the genome. B. Distribution of the read count ratio. Ratio between the Top10 mostly expressed instances and all instances having 10 reads or less across all GTEx RNA-seq assays–On average only 10% of the reads derive from top expressed L1Hs instances.Broad model of pervasive transcription is evidenced by the number of uniquely mapped reads overlapping LINE-1 (L1Hs) instances. (PDF)Click here for additional data file.S2 FigFraction of reads deriving from 4 subfamilies and pervasive transcription from four methods.(PDF)Click here for additional data file.S3 FigSimulated transcripts from putative L1Hs, L1P1, L1PA2, L1PA3, L1PA4, L1PA5 and L1PA6 were aligned to the reference genome using the same parameters as the TeXP pipeline. The proportion of reads mapped to each subfamily was calculated.(PDF)Click here for additional data file.S4 FigMost samples have zero RPKM (denser bar at the bottom). L1Hs has more samples with higher RPKM than any other subfamily followed by L1PA2 the second most recent L1 Subfamily.(PDF)Click here for additional data file.S5 FigEvery four bars are experiments respectively from whole cell polyA-; whole cell polyA+; cytoplasm polyA+ and nuclear polyA+.(PDF)Click here for additional data file.S6 FigEvery four bars are experiments respectively from whole cell polyA-; whole cell polyA+; cytoplasm polyA+ and nuclear polyA+. Gray and Dark blue bars refer to pervasive transcription and L1Hs autonomous transcription signal respectively.(PDF)Click here for additional data file.S7 FigEstimates of L1Hs autonomous transcription from ddPCR (Y-axis) and TeXP (X-axis). (PDF)Click here for additional data file.S8 FigInput to TeXP were simulated as a combination of known signals (i.e. X pervasive transcription + (1-X) L1Hs autonomous transcription). Y-axis represents the root means square error for TeXP estimations at 3 different scenarios . Ribbons represent 25%-75% of TeXP rmse.(PDF)Click here for additional data file.S9 FigMost samples have zero RPKM (more dense bar at the bottom). L1Hs has more samples with higher RPKM than any other subfamily followed by L1PA2 the second most recent L1 Subfamily.(PDF)Click here for additional data file.S10 FigMost signal mapping to LINE-1 samples is derived from pervasive transcription.(PDF)Click here for additional data file.S11 FigMost of EBV transformed cell lines have no autonomous transcription of L1Hs (bottom box). Transformed fibroblasts, derived from skin, have intermediate autonomous transcription of L1Hs (at lower levels than Skin samples). And K-562, derived from Leukemia tumor, has consistently high autonomous transcription of L1Hs across distinct batches.(PDF)Click here for additional data file.S12 FigWhen comparing primary tissue and EBV transformed cell-lines from the same individuals we noticed a consistent decrease in the autonomous transcription of (A) Skin samples and EBV-Transformed fibroblasts  and (B) Whole-blood and EBV-Transformed lymphocytes . (PDF)Click here for additional data file.S13 FigDistribution of normalized number of reads (RPM) derived from pervasive transcription and autonomous transcription of LINE-1 from tumor and healthy samples.(PDF)Click here for additional data file.S14 FigPervasive transcription index was estimated for each GTEx sample and ordered by pervasive transcription median.(PDF)Click here for additional data file.S15 FigDifferent aligners were used to assess the construction of mappability fingerprints in the human reference genome. One hundred independent simulations of L1Hs transcript reads were independently mapped to the reference genome using bowtie2 (red), bwa (green) and star (blue). The box plot represents the distribution of the number of reads mapped to each L1 subfamily in the reference genome.(PDF)Click here for additional data file.S16 FigSimulating L1Hs reads with different length yield distinct proportions of reads mapped to each subfamily. As expected, the longer the read, the higher the proportion of reads correctly mapped originating subfamily.(PDF)Click here for additional data file.S17 FigCorrelation between the number of LINE-1 elements in intergenic (x-axis) and intronic (y-axis) regions .(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 TableComparison of the absolute expression of copies of full-length transcript/ng of L1Hs autonomous transcript (ORF1) and L1Hs pervasive transcript (ORF2) when run with both references.(PDF)Click here for additional data file.S4 TableBladder, Kidney Cortex and minor salivary gland were eliminated from further analysis.(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.


Through the application of mammary organoids to bypass the severe phenotype associated with Suz12 loss in vivo, we have explored gene expression and chromatin structure in wild-type and Suz12-deleted basal-derived organoids. Analysis of organoids led to the identification of lineage-specific changes in gene expression and chromatin structure, inferring cell type–specific PRC2-mediated gene silencing of the chromatin state. These expression changes were accompanied by cell cycle arrest but not lineage infidelity. Together, these data indicate that canonical PRC2 function is essential for development of the mammary gland through the repression of alternate transcription programs and maintenance of chromatin states.Distinct transcriptional states are maintained through organization of chromatin, resulting from the sum of numerous repressive and active histone modifications, into tightly packaged heterochromatin versus more accessible euchromatin. Polycomb repressive complex 2 (PRC2) is the main mammalian complex responsible for histone 3 lysine 27 trimethylation (H3K27me3) and is integral to chromatin organization. Using  Suz12 resulted in nonfunctional PRC2 and led to growth defects in mammary epithelial cells in vivo and in vitro. Here, we have used a 3D mammary organoid system to circumvent the lethality associated with Suz12 loss and studied chromatin dynamics and gene expression of PRC2 complexes. Our results suggest that loss of all canonical PRC2 complexes results in failure to repress transcriptional programs associated with early commitment and differentiation of mammary epithelial cells.The formation of mammary glands requires the tight regulation of many genes that govern cell fate decisions in the cells that form them. However, most of these genes remain undefined. The Polycomb repressive complex 2 (PRC2) has a role in gene silencing, and it is comprised of several subunits, which include either Enhancer of Zeste homolog 2 (Ezh2) or Ezh1 in combination with Suppressor of Zeste 12 protein homolog (Suz12) and embryonic ectoderm development (EED). Dysregulation of these subunits can lead to breast cancer. Although previous studies have analyzed the contribution of these complexes in mammary epithelium, failure to inactivate all canonical PRC2 complexes has made this task difficult. Deletion of  Suz12 or Eed resulted in elevated expression of Hox genes in Drosophila and mammalian cells and marked increases in gene networks that control developmental lineages [Suz12 or Eed, Ezh2-deficient mice are not viable and die during early implantation stages [A central question in biology is how different cell types maintain distinct cell fates despite containing the same genetic material. Organization of DNA into open or closed chromatin states by posttranslational modifications (PTMs) of histones has emerged as a critical mechanism underpinning cell diversity and reflecting lineage-specific gene expression, developmental programs, or disease processes . The higlineages ,8 becauslineages . In contlineages  or compllineages , by Ezh1n stages .Members of PRC2—in particular, Ezh2—are often found dysregulated in human cancers. In breast cancer, Ezh2 overexpression is associated with aggressive breast cancers and poor prognosis and inversely correlated with H3K27me3 expression . It remaEzh2 in the mammary gland resulted in a developmental delay [The mammary gland in both humans and mice is a bilayered structure composed of two cellular lineages: an inner luminal layer and an outer myoepithelial layer that contacts the basement membrane . There ial delay ,17 but dSuz12 in vivo and in mammary organoids [Suz12 in mammary progenitor cells due to loss of PRC2 function. Similar findings were made upon deletion of Eed. Through the application of assay for transposase-accessible chromatin using sequencing (ATAC-seq) to probe chromatin accessibility in Suz12-deficient organoid cultures, we have identified regions of PRC2-dependent chromatin compaction and consequent changes in gene expression. Interestingly, the chromatin state in basal-derived organoids was reliant on PRC2 function, and Suz12 deletion led to gene de-repression, thus highlighting a crucial role for PRC2 in the maintenance of chromatin states within the mammary epithelial hierarchy.To reexamine the contribution of the canonical PRC2 complex to mammary gland development, we deleted rganoids . Here, wSuz12 alleles with Cre transgenic mice that express Cre under control of the mouse mammary tumor virus promoter (MMTVcre). Genotyping of offspring revealed that two-thirds of MMTVcreT/+Suz12fl/f mice did not survive to weaning  mice with respect to bodyweight   . These smparable , demonstithelium . Cell faithelium .Eed produced a similar phenotype to deletion of Suz12. As seen with Suz12 deletion, conditional deletion of Eed with MMTVcre resulted in perinatal lethality of mice  mouse model. R26creERT2KI/+Suz12f/f mice injected with tamoxifen die within 2 weeks because of hematopoietic failure , thus precluding the use of this model for in vivo studies. Basal/MaSC-enriched , committed luminal progenitor (CD29loCD24+CD14+), and mature luminal (CD29loCD24+CD14−) cell subsets were sorted from R26creERT2KI/+Suz12f/f and control mice and subjected to in vitro colony-forming assays to determine the effect of Suz12 deletion on the activity of mammary progenitor cells following induction by 4-hydroxytamoxifen (4OHT). Consistent with expression of Suz12 in all mammary epithelial subsets , consistent with a known role for Ezh2/PRC2 in regulating cdkn2a gene expression  cells was evident  enrichment analysis found that common DE down-regulated genes were significantly enriched for metabolic processes and H3K27 methylation, while common DE up-regulated genes were enriched for development and morphogenesis GO terms  analysis of ulations , and delulations . In both of PRC2 . A large of PRC2 , as well of PRC2  in Suz12GO terms .Suz12 reflect alterations in chromatin compaction associated with loss of repressive H3K27me2/me3 marks, we performed global mapping of chromatin accessibility using ATAC-seq. Differential accessibility analysis identified 2,767 windows, of which 2,415 (87.3%) were more accessible upon Suz12 deletion . Nevertheless, the frequency of reads mapping to nucleosome-free regions versus mononucleosomes was not significantly altered. We observed only minor changes to insert length, suggesting no gross changes in nucleosome occupancy  of the canonical PRC2 complexes leads to a complete block in mammary gland development in vivo and markedly curtails progenitor cell activity in vitro. Strikingly, function . Assays  studies  have und studies . By explSuz12 deletion, we observed comparable changes in gene expression (7%–11%) in both basal- and luminal-derived organoids. There was significant overlap between the up-regulated genes in basal- and luminal-derived organoids, and the magnitude of change was highly correlated. Assessment of chromatin accessibility with ATAC-seq indicated that the chromatin of basal-derived organoids is more open upon Suz12 loss. Recent findings in human breast epithelial subsets [Suz12-deleted basal-derived organoids. Since we did not observe a significant increase in GC content of DA reads identified by ATAC-seq, the mechanism is most likely to involve enhancers. Coupling RNA-seq with ATAC-seq analysis in basal-derived organoids revealed that deletion of Suz12 led to more than one quarter of genes becoming significantly transcriptionally active coincident with more accessible chromatin. We have previously shown that the MaSC/basal subset demonstrates the lowest levels of H3K27me3 across transcriptional start sites (TSSs) [PRC2 complexes are reported to colocalize with H3K27me3 on the promoters of around 10% of all genes . Upon Su subsets  predict s (TSSs)  while als (TSSs) . This obs (TSSs) , while ts (TSSs) . In lumis (TSSs) .Suz12-deleted cells. Notably, we show here that the absence of these repressive marks through deletion of a single protein results in large changes in gene expression, consistent with the notion that chromatin states are the sum of a limited repertoire of PTM combinations on histone tails. While these data do not exclude noncanonical functions in the presence of a functional PRC2 complex [Our studies indicate that PRC2 is responsible for both H3K27me3 and H3K27me2 marks in MECs, as evidenced by their loss in  complex , they suSuz12-deleted organoids, like Ezh2-null basal cells [Brca1-deleted breast cancer [cdkn2a locus of an individual tumor might predict its response to Ezh2 loss. The observation that Suz12 deficiency in normal mammary cells leads to de-repression of cdkn2a, paralleling that seen with loss of Ezh2 [Ink4a and p14Arf expression. However, if this locus is perturbed, as is seen in many breast cancers [In the context of breast cancer, our data indicate that al cells , displayal cells . Indeed,t cancer  and in at cancer . Additio of Ezh2 , would p cancers , then loIn summary, our findings establish an essential role for PRC2 in the maintenance of mammary progenitor function and point to a critical function for PRC2 in maintaining chromatin states to ensure appropriate gene expression in MECs.All animal experiments were conducted using mice bred at and maintained in our animal facility according to the Walter and Eliza Hall Institute of Medical Research Animal Ethics Committee guidelines, approval number 2017.002.f/f [f/f [f/f [f/f T lymphocytes [Suz12f/f , Eedf/f f/f [f/f , Ezh2f/ff/f [f/f , MMTV-crf/f [f/f , and R26f/f [f/f  gene-tarphocytes  were a gPrimary mouse MEC cultures were isolated from both inguinal and/or thoracic mammary glands and prepared as described . MEC susOrganoids were cultured as described previously  in 8 μl Mice were injected with BrdU Cell Labeling Reagent  1.5 hours prior to collection. For histology, tissues were fixed in 4% paraformaldehyde overnight and embedded in paraffin. Sections (5 μm) were stained with hematoxylin–eosin (HE). For whole-mount analysis, mammary glands were harvested and fixed in Carnoy’s solution  and stained with Carmine alum. The extent of ductal outgrowth was measured on inguinal whole mounts as the distance from the center of the lymph node to the leading edge of the ductal mass.IHC and IF were performed as described . ParaffiLysates were prepared in RIPA buffer , and wes6 reads per sample. Sequencing runs were pooled for each replicate and sample, and adapters were trimmed with Trim Galore!  and mapped to the mm10 mouse genome using bowtie2 [http://broadinstitute.github.io/picard/), and bam files were run through macs2 (doi: 10.1186/gb-2008-9-9-r137) for peak calling using these parameters: “—nomodel—shift—75—extsize 150—qvalue 0.05.” For DA analysis, bam files were loaded in SeqMonk , and probes were created using a tiling approach with 150 bp windows end to end across the genome. Raw counts were then processed through the differential gene expression pipeline within edgeR [ATAC-seq was performed as described  with the bowtie2 , allowinin edgeR , and DA in edgeR  computeGf/f female mice using the RNeasy Mini kit (Qiagen). Two biological replicates were prepared of the basal-derived organoids and 3 biological replicates of luminal-derived. RNA-seq was carried out on an Illumina Nextseq 500 to produce 20–65 million 80 bp reads per sample. Read pairs were mapped to the mouse genome (mm10) using the subread aligner [t statistics relative to a fold change threshold of 1.5, and allowing for an abundance trend in the standard errors and for robust estimation of the Bayesian hyperparameters [Total RNA was extracted from basal- or luminal-derived organoids grown from single sorted luminal or basal populations from the mammary glands of R26creERT2/Suz12 aligner  implemen aligner  and its  aligner  and edge aligner  software aligner  was appl aligner . Differe aligner , computiMicroarray expression profiles of breast tumors were downloaded from Gene Expression Omnibus series GSE18229 . Probe i−ΔΔCT). Primer sequences were: Ezh2 For: 5′-ACATCCCTTCCATGCAACACC-3′; Ezh2 Rev: 5′-TCCCTCCAGATGCTGGTAACA-3′; Eed For: 5′-GTGAACAGCCTCAAGGAAGAT-3′; Eed Rev: 5′- ATAAGGTTACTCTGTGCTTC-3′; Gapdh For: 5′-TGACATCAAGAAGGTGGTGAAG; Gapdh Rev: AAGGTGGAAGAGTGGGAGTTGC-3′.For qRT-PCR analysis, total RNA from MECs was reverse-transcribed using Superscript III (Invitrogen), and cDNA were analyzed on a LightCycler 480 (Roche). Input cDNA concentrations were normalized to GAPDH. Product accumulation was evaluated using the comparative Ct method , at higher magnification (bottom), lungs from MMTVcreT/+Suz12f/f mice have fewer septa and increased mesenchyme, consistent with abnormal lung differentiation. Scale bars: 1.5 mm (top) and 50 μm (bottom). (C) Weight of 5–6 week old MMTVcreT/+Suz12f/f mice and control littermates of the indicated genotypes. (D) Ductal extension of mammary glands from 5–6 week old MMTVcreT/+Suz12f/f mice and control littermates of the indicated genotypes. Ductal extension is calculated as the distance from the center of the lymph node to the leading edge of the mammary outgrowth in R4 mammary gland whole mounts. Individual data points and the mean are shown. ** P < 0.01 for T/+ f/f compared with all other genotypes (one-way ANOVA for multiple comparisons). (D) qRT-PCR analysis of Suz12 mRNA expression in mammary glands from 6 week old MMTVcreT/+Suz12f/+ and MMTVcreT/+Suz12f/f mice. Expression was calculated relative to Gapdh, and values were normalized to the average of the conditions per experiment. Mean ± S.E.M. (n = 4). R26creERT2KI/+Suz12f/f MECs treated without (Wt) or with 4OHT (ko) to delete Suz12 were used as controls (n = 1). One of two experiments with two independent sets of primer pairs for Suz12 is shown. (E) Representative images of immunohistochemical staining of terminal end buds in mammary glands from 6 week-old MMTVcreT/+Suz12f/f and control littermates. Markers of proliferation (BrdU) and differentiation of MECs into hormone receptor positive mammary subsets  were included. Isotype-control stained sections are shown in the inset. Scale bars: 50 μm. Individual quantitative observations can be found in (A) Western blot for Ezh2 and Suz12 in whole mammary gland lysates prepared at the indicated developmental timepoints. Gapdh serves as a loading control. Molecular mass in KDa of the protein ladder is shown on the left-hand side. (B) Representative images of HE-stained sections of lungs from MMTVcre(TIF)Click here for additional data file.S2 FigT/+Eedf/f mice and control littermates of the indicated genotypes. Arrows indicate the leading edge of the mammary epithelium. Scale bars: 4 mm. Ductal extension was calculated as described in P < 0.05 for T/+ f/f compared with all other genotypes (one-way ANOVA for multiple comparisons). (B) qRT-PCR analysis of Eed mRNA expression in mammary glands from 6–7 week old MMTVcre+/+Eedf/f and MMTVcreT/+Eedf/f mice. Expression was calculated relative to Gapdh, and values were normalized to the average of the conditions per experiment. Mean and individual values are shown (n = 2). CD4cre+/+Eedf/f (f/f +/+) and CD4creT/+Eedf/f (f/f T/+) T lymphocytes were used as controls (n = 1). One of two experiments with two independent sets of primer pairs for Eed is shown. (C) Immunofluorescent staining for Eed in mammary glands from 6 week old MMTVcreT/+Eedf/f and control littermates. Scale bars: 50 μm. (D) Immunohistochemical staining for Ezh2 and H3K27me3 in mammary glands from 6 week old MMTVcreT/+Eedf/f and control littermates. Isotype-control stained sections are shown in the inset. Scale bars: 50 μm. Individual quantitative observations can be found in (A) Representative images of whole mounts (left) and ductal extension (right) of mammary glands from MMTVcre(TIF)Click here for additional data file.S3 FigEzh2 mRNA expression in MEC from R26creERT2KI/+Suz12f/f mice and the indicated control genotypes following addition of 4OHT to induce Suz12 deletion on day 2. Cells were cultured for 1 week prior to preparation of RNA. Copies of Ezh2 are expressed relative to GAPDH. (B) Western blot analysis of protein expression in MECs from R26creERT2KI/+Suz12f/f mice and the indicated control genotypes following addition of 4OHT to induce Suz12 deletion on day 2. Cells were cultured for 1 week prior to preparation of protein lysates. Molecular mass in KDa of the protein ladder are shown on the left. (C) Image of genotyping PCR performed on organoids grown for 2 weeks from single basal or luminal progenitor cells from R26creERT2KI/+Suz12f/f mice or Wt mice. Organoids were left untreated (-) or treated with 4OHT on day 1 (1) or day 4 (4) of culture. The size of Suz12 Wt, floxed (flox), and recombined (del) alleles are indicated. The size (bp) of the DNA ladder is shown on the left-hand side. (D) Immunohistochemical staining for Suz12 on 2 week old organoids from R26creERT2KI/+Suz12f/f or control mice, treated with 4OHT on day 4 of culture. Control stained sections are shown in the inset. Scale bars: 400 μm. (E) Western blot analysis of 2 week old organoids from R26creERT2KI/+Suz12f/f mice or control mice, treated with 4OHT on day 4 of culture. Molecular mass in KDa of the protein ladder is shown on the left-hand side. (F) Representative images of repassaged organoids grown for 2 weeks from single basal cells from R26creERT2KI/+Suz12f/f mice, on day 1 and day 11 after passaging. Black arrowheads indicate clumps of cells that became cystic overnight after passaging. White arrowheads represent new noncystic colonies that formed from single cells. Scale bars: 200 μm. (G) Image of genotyping PCR performed on primary or repassaged organoids described in (B) after 11 days in culture. The size of Suz12 Wt, flox, and del alleles are indicated. Basal- or luminal-derived organoids were derived from 2 independent R26creERT2KI/+Suz12f/f mice. Controls included Suz12 Wt DNA (left) and no DNA (right). The size (bp) of the DNA ladder is shown on the left-hand side. Individual quantitative observations can be found in (A) qRT-PCR analysis of (TIF)Click here for additional data file.S4 FigSuz12 expression is indicated. (B) IGV profile of RNA-sequencing reads for Suz12 in basal- or luminal-derived organoids left untreated or treated with 4OHT on day 4 of culture. The metrics are shown on the left side of each plot for each sample. The direction of transcription is marked by an arrow. Exon 5, around which loxP sites are situated in the Suz12 floxed allele, is indicated by the boxed region. Scale bar: 5 Kb. (C) Venn diagrams showing the number of DE down-regulated (left) and up-regulated (right) genes in Suz12-deleted basal- and luminal-derived organoids. Overlap in DE genes is indicated, as well as the 5 top GO terms for that gene set. (D) Scatterplot relating log2-fold gene expression changes in Suz12-deleted basal- and luminal-derived organoids. Changes in basal-derived (x-axis) and luminal-derived (y-axis) organoids are highly correlated (P < 1e-16). Individual quantitative observations can be found in (A) MD plot showing log2-fold expression changes versus average log2-expression by RNA-sequencing in luminal-derived organoids deleted for Suz12. Up- and down-regulated genes with changes significantly greater than 1.5-fold are highlighted in red and blue, respectively (Treat-FDR < 0.05). (TIF)Click here for additional data file.S5 FigSuz12-deleted (ko) basal- and luminal-derived organoids relative to distance from the TSS is shown. The 40 and 178 nt peaks correspond to nucleosome-free regions and the mononucleosome, respectively. (B) An image of the ATAC-seq libraries for Wt (-) or Suz12-deleted (+) basal- and luminal-derived organoids, resolved on Tape-station. The ladder is in the first lane on the left-hand side. Individual quantitative observations can be found in (A) The averaged frequency of read insert length obtained by ATAC-seq for Wt and (TIF)Click here for additional data file.S6 FigCdkn2a (A), Foxd1 (B), and Hoxc locus (C) are shown. The metrics are shown on the left side of each plot for each sample. The direction of transcription is marked by an arrow. Scale bars are indicated. (D) A whole-mount 3D confocal image (left) and an optical section (right) of a Wt and Suz12-deleted (KO) basal-derived organoid labeled for K14 and K8/18. Scale bars: 30 μm (whole mount) and 15 μm (section). ATAC-seq, assay for transposase-accessible chromatin using sequencing; RNA-seq, RNA sequencing; Wt, wild-type.The RNA-seq and ATAC-seq profiles around (TIF)Click here for additional data file.S7 FigSuz12-deleted (KO) luminal-derived organoids deleted for Suz12, compared with Wt average log-expression. Regions that were significantly up- or down-regulated are shown in red or blue, respectively. (B) A plot showing GC nucleotide enrichment in genomic regions of ATAC-Seq reads of Suz12-deleted (KO) luminal-derived organoids, compared with Wt. Shown are the genomic and functional DNA element average for the mouse genome. (C) MACS Peaks calling analysis of Suz12-deleted (KO) luminal-derived organoids, compared with Wt. (D) Analysis of genomic regions associated with significantly DA windows identified by ATAC-seq analysis of Wt and Suz12-deleted (KO) basal- or luminal-derived organoids. (E) Box and whisker plot of significantly DA genes found to be associated with windows found in (A) by ATAC-seq analysis of Wt and Suz12-deleted (KO) luminal-derived organoids, and their corresponding expression by RNA-seq analysis. Whiskers represent the 5%–95% intervals. ** P < 0.0028  (F) Venn diagram showing the overlap in up-regulated DE genes between RNA-seq and DA genes by ATAC-seq in Suz12-deleted luminal-derived organoids. (G) Suz12-deleted transcriptional signature by breast cancer tumor subtype . Box plots show the aggregate gene expression score in each tumor subtype [Suz12-deficiency in basal-derived organoids (left) and luminal-derived organoids (right). The Suz12-deficient expression score is highest in the claudin-low subtype and lowest in the basal and Her2 subtypes . ATAC-seq, assay for transposase-accessible chromatin using sequencing; DA, differentially accessible; DE, differentially expressed; Her2, human epidermal growth factor receptor 2; KO, knockout; MACS, model-based analysis of ChIP-seq; MD, mean-difference; RNA-seq, RNA sequencing; Suz12, Suppressor of Zeste 12 protein homolog; Wt, wild type.(A) MD plot showing 150 bp windows identified as significantly DA (log-fold change) by ATAC-seq analysis of  subtype  for gene(TIF)Click here for additional data file.S1 DataObserved numbers and percentages of males, females, and total offspring of the indicated genotypes at weaning, as well as the expected mendelian frequency , are shown.(XLSX)Click here for additional data file.S2 Datat statistic, P value, and FDR, respectively, from the Treat method relative to fold-change threshold of 1.5. AveExpr, average log2 counts per million; FDR, false discovery rate; logFC, log2 fold-change; RNA-seq, RNA sequencing.Length = gene length in bases . Remaining columns give the (XLSX)Click here for additional data file.S3 DataColumns A–D describe the location of the genomic window, E describes FDR, F the closest gene and its strand in G, H gene ID, I gene description, J Seqmonk element type, K window position relative to gene, L distance to gene, M–R, log2 RPKM for each experimental condition, S basal-derived organoid Wt average, T basal-derived KO average, and U the difference between the basal-derived KO and Wt. ATAC-seq, assay for transposase-accessible chromatin using sequencing; FDR, false discovery rate; KO, knockout; RPKM, reads per kilobase per million mapped reads; Suz12, Suppressor of Zeste 12 protein homolog; Wt, wild-type.(XLSX)Click here for additional data file.S4 DataATAC-seq, assay for transposase-accessible chromatin using sequencing; RNA-seq, RNA sequencing; Wt, wild-type.(XLSX)Click here for additional data file.S5 DataATAC-seq, assay for transposase-accessible chromatin using sequencing; GO, Gene ontology; RNA-seq, RNA sequencing; Wt, wild-type.(XLSX)Click here for additional data file.S6 Data(XLSX)Click here for additional data file.


Anopheles stephensi and remobilized through genetic crosses using a piggyBac transposase expressing line. A total of 620 remobilization events were documented, and 73 were further characterized at the DNA level to identify patterns in insertion site preferences, remobilization frequencies, and remobilization patterns. Overall, the use of the tetameric AmCyan reporter as the fusion peptide displayed a preference for insertion into the 5′-end of transcripts. Notably 183 – 44882 bp upstream of the An. stephensi v1.0 ab initio gene models, which demonstrated that the promoter regions for the genes of An. stephensi are further upstream of the 5′-proximal regions of the genes in the ab inito models than may be otherwise predicted. RNA-Seq transcript coverage supported the insertion of the splice acceptor gene trap element into 5′-UTR introns for nearly half of all insertions identified. The use of a gene trap element that prefers insertion into the 5′-end of genes supports the use of this technology for the random generation of knock-out mutants, as well as the experimental confirmation of 5′-UTR introns in An. stephensi.The piggyBac transposon was modified to generate gene trap constructs, which were then incorporated into the genome of the Asian malaria vector,  In situ hybridization and RNAseq technologies can achieve single-cell resolution and important improvements continue to be developed  regulated by a basal promoter which in the absence of an enhancer, is insufficient to result in detectable levels of the reporter gene product. Integration of the element within the domain of an active enhancer will result in the cis-activation of the basal promoter and expression of the reporter gene. While of great utility, enhancer-trapping does not enable the easy identification or isolation of the regulatory sequences responsible for the expression of the reporter gene since enhancers can act at considerable distances from the genes they are regulating. Alternatively, for insertion mutagenesis and gene identification purposes, promoter-, secretory-, exon- or polyA-traps are required  as well as determining the location of the integrated promoter-trap element. After this initial characterization is completed the line could be discarded and any subsequent interest in the gene or the original promoter-trap allele could be satisfied by recreating the mutation using RNA-guided endonuclease-based gene integration technologies.piggyBac-based promoter-trap designed and constructed for use in mosquito genomes and report on results from a forward genetic screen conducted in An. stephensi involving approximately 125,000 mosquitoes. A total of 620 promoter-trap events were observed, of which 73 were characterized for insertion into a gene. These events enabled existing gene models to be confirmed in some cases and in others for new models to be developed. Notably the annotation for the 5′-UTR regions for genes, and the expected promoter regions of genes, which in roughly half of the promoter trap insertions, was distal to the +1 of the transcript ORF and terminated within the 5′-UTR. The results from our study enhance the current annotation of the An. stephensi genome, and also provide insight for the design of putative ubiquitous and tissue-specific promoters within mosquitoes.Here we report on a PB3′SA-0AmCyan and PB3′SA-IRESAmCyan: These are promoter-trap vectors constructed from piggyBac elements consisting of 671bp and 690bp of piggyBac’s 5′ and 3′ terminal sequences, respectively (GenBank: J04364.2). Using the Gateway cloning system  a dominant visible marker and a promoter-trap reporter were inserted between the terminal sequences of piggyBac. The dominant visible marker consisted of mBanana  with or without a nuclear localization signal under the regulatory control of the neural-specific promoter 3XP3 (AmCyan (Clonetech) with a 3′ UTR from the SV40 VP1 gene. In PB3′SA-0AmCyan the AmCyan open reading frame had a 3′ splice acceptor site resulting in a phase-0 splice of AmCyan and the targeted transcript. PB3′SA-IRESAmCyan was identical to PB3′SA-0AmCyan except that after the 3′ splice acceptor site there was the internal ribosomal entry site (IRES) from Drosophila C dicistrovirus .piggyBac vector used to introduce the piggyBac transposase transcription unit under the regulatory control of the promoter from the Anopheles gambiae vasa gene and was designed based on the integrated vector stabilization strategy described by piggyBac element (pXL-BacII-3xP3-ECFP) with the fluorescent protein gene Enhanced Cyan Fluorescent Protein (ECFP) under the regulatory control of the promoter 3XP3  under the regulatory control of the promoter 3XP3  operating under the National Institutes of Health’s Office of Laboratory Animal Welfare guidelines.Adult mosquitoes were maintained at 29° and 80% relative humidity with continuous access to an aqueous solution of 10% sucrose. Larvae were reared in deionized water at 29° and provided with pulverized fish food  All of the transgenic insects used in this study were created using the services of The University of Maryland College Park Institute for Bioscience and Biotechnology Research’s Insect Transformation Facility.An. stephensi selected for susceptibility to infection by Plasmodium falciparum (This is a wild-type strain of lciparum .DsRedUMITF-PB-M5 (referred to as hspPB): This is a previously described transgenic line of SDA 500 with the piggyBac transposase transcription unit under the regulatory control of the promoter from D. melanogaster’s hsp70 gene and the red fluorescent protein gene DsRed under the regulatory control of the synthetic promoter 3XP3 (piggyBac transposase in this line can remobilize piggyBac elements in the soma and germ-line (DsRedUMITF-vasaPB  (referred to as vasPB): This is a transgenic line of SDA 500 created using the vector PB1.5vasaPBtnpsase. Primary transgenic insects containing the vector PB1.5vasaPBtnpsase were selected that simultaneously expressed ECFP and DsRed and were subsequently mated to SDA 500 of the opposite sex. Progeny from this cross that expressed only DsRed because of the excision of pXL-BacII-3xP3-ECFP from PB1.5vasaPBtnpsase were selected and used to create the line DsRedUMITF-vasaPB . Excision of pXL-BacII-3xP3-ECFP from PB1.5vasaPBtnpsase results in the loss of the only functional piggyBac 5′ inverted terminal repeat in this vector, leaving the vasa::piggyBac transposase transgene and the associated 3XP3::DsRed genetic marker stably located in the original site of integration. This line expresses piggyBac transposase in the germ-line.DO-05-13M1, DO-05-13F1C, DO-05-13F1D, DO-05-13F2A, DO-05-13F2B : These were transgenic lines of SDA 500 with PB3′SA-0AmCyan (line F1C) or PB3′SA-IRESAmCyan  inserted at single loci and maintained as homozygotes. None of these lines displayed any detectable AmCyan expression in the larval or adult stages, however the 3xP3::mBanana marker was visible in all lines.piggyBac promoter-trap element was remobilized in the An. stephensi genome by crossing virgin females containing a promoter-trap element  to males from the transposase expressing lines hspPB or vasaPB en masse . Females heterozygous for the promoter-trap element (expressing mBanana or nls-mBanana) and the transposase-expressing transgene (expressing DsRed) were mated en masse to SDA500 males . Larvae (3rd or 4th instar) and/or adults with the promoter-trap element (mBanana or nls-mBanana) were screened for the presence of non-parental patterns of AmCyan expression indicating that the promoter-trap element was now in a new location and was being transcribed and translated. The frequency of remobilization was estimated by dividing the number of AmCyan-expressing individuals over the total number of mosquitoes screened. The AmCyan expression patterns in individuals with a promoter-trap event were documented photographically. These individuals were either saved and lines established by outcrossing them to SDA500 and screening for and retaining progeny expressing the marker genes mBanana or nls-mBanana, or they were frozen on dry ice and stored at -80° for RNA/DNA extraction and subsequent analysis.To initiate a promoter-trap screen, the To identify the integration site of the promoter-trap element in line M1, genomic DNA was extracted following For the remaining promoter-trap lines F1C, F1D, F2A, F2B, total RNA and DNA were extracted from individual mosquitoes using TRIZol  according to the manufacturer’s instructions. RNA was isolated first, followed by back-extraction of DNA mediated by the addition of TNES-6U .BstYI or BglII and the resulting fragments were ligated to Splinkerette adapters  and their sequences determined using the method of Sanger et al. (piggyBac was confirmed and the genomic insertion site was mapped to the An. stephensi genome (strain SDA500 version: AsteS1) . The RNA was re-precipitated with one volume of isopropanol and 1/10 volume of 3M NaOAc (pH 5.2). The resulting RNA pellet was washed twice with 80% ethanol and re-suspended in nuclease-free water. Thirty micrograms of the pooled RNA was treated with 30U of DNAse I , re-extracted using TRIzol, precipitated with one volume of isopropanol and the precipitate was washed twice with 80% ethanol. The precipitated RNA was dried and re-suspended into nuclease free water to a final concentration of 1 µg/µL. This RNA was sequenced using Illumina RNASeq with polyA selection (paired-end 50 nt reads) using the Illumina TruSeq mRNA library kit  by the Hudson Alpha Institute for Biotechnology (HAIB) .An. stephensi genome (AsteS1) using Tophat2 , and mapped to the An. stephensi genome. The sequences from the genomic insertion sites have been deposited to the GSS database at NCBI as accessions KS331897 - KS331997. Supplemental material available at Figshare: https://doi.org/10.25387/g3.6995042.Short read data from this manuscript have been submitted to the SRA database at NCBI as accession number SRR3195175 and the NCBI GEO database as accession number GSM2076255, which contains the gtf file that predicts the introns not present in the ab initio AsteS1 version of the An. stephensi containing the piggyBac promoter-trap element were established through microinjection , representing five distinct starting loci for piggyBac remobilization . There is some evidence that both parameters were important in this study. For example, the transposition activity of the promoter-trap element in line F2B showed reduced transposition activity, probably resulting from the influence of genomic location on piggyBac remobilization activity, an effect well documented in Drosophila melanogaster  , 4. NotapiggyBac transposon used to vector the promoter-trap reporter cassette and the genomic DNA immediately flanking the ends of the element (Table S2). We found that only about 25% of the 232 integration events recovered could be localized successfully using only this method. Of the insertions, nearly half were located within 5′-UTR regions not annotated in the ab initio genome models.Locating promoter-trap elements is essential in order to connect genotypes and the reporter gene-expression phenotypes associated with those genotypes. Two methods were used in this study, one at the DNA level and one at the RNA level, with varying degrees of success. First, of the 620 gene trap events detected in this study, 232 were used for integration site analysis, and of these, 73 insertions were successfully characterized using the PCR-based method to selectively amplify a small terminal region of the An. stephensi genome, the unmapped reads  were then extracted from the trimmed reads and remapped to the An. stephensi genome with the inclusion of a ‘pseudoscaffold’ consisting of the entire piggyBac-end flanked IRES-containing promoter trap element. A total of 13,967 reads mapped to the promoter trap element . The majority of the reads mapped to the mBanana and AmCyan fluorescent reporters , with additional coverage of the 3xP3 promoter region (586 reads) likely due to read-through from the AmCyan transcript. In addition, some coverage was present among the piggyBac ends  supporting the observation of integration of the promoter trap element into exonic sequence. The exonic insertions possibly resulted in readthrough of the promoter trap element from the right piggyBac end to the SV40 terminator sequence. Finally, the read coverage for the IRES sequence was low and contained only five reads. Since the 3′-splice acceptor for the IRES-containing promoter trap element is ∼200 bp upstream of the open reading frame of AmCyan, and since this construct was positive for AmCyan reporting promoter traps at a frequency greater than the 3′-splice acceptor alone, we postulate that there were additional unexpected splice acceptor sites upstream of the AmCyan ORF. To identify putative transcript and promoter-trap fusion events, the 5′-end of the AmCyan sequence was directly pattern searched against the An. stephensi genome and no exact match within the An. stephensi genome was found for the initial 14 nucleotides of the AmCyan open reading frame (in both orientations). Since no match was present in the genome, we reasoned that Illumina tags containing an exact match to the AmCyan open reading frame (minimum of 14 nucleotides) that were connected to sequence from An. stephensi represented true promoter trapped events at the RNA level. We further put two limits on these tags. First, the orientation of the AmCyan tag needed to run toward its mate pair, and the mate pair had to be an exact match (minimum of 47 nucleotides) to the AmCyan nucleotide sequence, and second, the An. stephensi tag needed to be at least 15 nucleotides long. After setting these limits, we identified a total of 145 paired end reads  and could be blasted to the An. stephensi genome to assess if they fit the model for the promoter trap element. A total of eight of the reads had perfect matches to the An. stephensi genome (with four unique occurrences) and formed a junction that fused with the 15 nucleotides upstream of the +1 of AmCyan, suggesting that a sequence present within the +50 amino acids of the IRES sequence contained a splice junction since the splicing of the An. stephensi sequence occurred at a canonical NGA site within the promoter trap element.The second method used to identify the location of the integrated promoter-trap element relied on the sequence analysis of the transcriptome. In this case, short-read Illumina RNA sequence data were examined for the presence of sequences corresponding to the reporter gene found on the promoter trap element. This method was also somewhat inefficient because from the transcriptomes of some 200 larvae, each representing a single promoter-trap event, we were only able to definitively locate the targeted integration site of 17 events. While the low recovery was likely due to relatively low transcript expression following sample pooling, we were able to recover and document several events at the RNA level that confirmed the functionality of the promoter trap elements at the transcript level. Following mapping of the reads to the An. stephensi genome, 49 of them were present in scaffolds that had assignment to the physical map for the mosquito. These 49 promoter-trap events were mapped to positions on the four chromosomes of An. stephensi . Conversely, the IRES containing elements, M1 exhibited random hopping throughout the genome ., which could have been due to the initial insertion sites of the elements, or possibly due to the larger length of the IRES containing promoter trap The F2A line could not be tested for random hopping because the insertion location could not be assigned to chromosome, while the F2B line could not be tested due to the few remobilization events recorded. In addition, the F2A and M1 lines did not show a preference for chromosome 3R, suggesting that the local hopping observed for F1C was due to events other than an overall preference for piggyBac insertion onto chromosome 3R.Of the 73 promoter-trap events mapped to the tephensi . Of the 2 = 122.76,24; P < 0.001) occurred within the first intron within a transcript- either a 5′-UTR intron, or the first intron within the coding region compared to all other introns within the genes combined  insertion events  in the An. stephensi SDA500 genome using the standalone version of weblogo (v 2.8.2)  and the RNA level (RNASeq) that gene trap elements, initiating from different start locations within the genome, independently landed into nearby TTAA sites within the same intron within the same gene.An. stephensi, as previously identified in Aedes aegypti for a carboxypeptidase gene (Reported promoter traps were predominantly found in metabolically-active tissues, such as the fat body, muscles, and imaginal discs. The promoter trap construct will report only when expressed, this metabolically-active tissues may allow for greater expression of the AmCyan reporter. As such, the promoter trap may be easier to report when inserted into genes with greater levels of gene expression in highly metabolic tissues. The identification of putative promoters within the fat body, imaginal discs, and neural tissues propose interesting tools for future studies for the functional analysis of genes. For example, the fat body is a highly metabolic tissue that could be used to express and export proteins of interest into the hemolymph of piggyBac transposon has a preference to insert into the 5′-end of transcripts (piggyBac. In addition, however, we were unable to determine at the RNA-level if the IRES element was functional in our promoter trap due to an unanticipated 3′-splice acceptor -15 bp upstream of the AmCyan open reading frame, thus it is likely that the IRES element was spliced out of the transcript and could not be tested for functionality. Future approaches could include modification of the terminal AG of the splice acceptor sequence for the additional putative splice acceptor elements identified upstream of the AmCyan open reading frame in the IRES-containing construct. Differences regarding local hopping and random remobilization between the IRES and non-IRES containing promoter traps was observed, however it is known that the insertion start location highly influences the remobilization of the transposable element (In our study, we found that the IRES and non-IRES containing promoter traps behaved similar to each other with respect to insertion site preference within transcript. Given that previous studies have identified that


Cytosine hydroxymethylation (5hmC) in mammalian DNA is the product of oxidation of methylated cytosines (5mC) by Ten-Eleven-Translocation (TET) enzymes. While it has been shown that the TETs influence 5mC metabolism, pluripotency and differentiation during early embryonic development, the functional relationship between gene expression and 5hmC in adult (somatic) stem cell differentiation is still unknown. Here we report that 5hmC levels undergo highly dynamic changes during adult stem cell differentiation from intestinal progenitors to differentiated intestinal epithelium. We profiled 5hmC and gene activity in purified mouse intestinal progenitors and differentiated progeny to identify 43425 differentially hydroxymethylated regions and 5325 differentially expressed genes. These differentially marked regions showed both losses and gains of 5hmC after differentiation, despite lower global levels of 5hmC in progenitor cells. In progenitors, 5hmC did not correlate with gene transcript levels, however, upon differentiation the global increase in 5hmC content showed an overall positive correlation with gene expression level as well as prominent associations with histone modifications that typify active genes and enhancer elements. Our data support a gene regulatory role for 5hmC that is predominant over its role in controlling DNA methylation states. Since stem progenitors and differentiated progeny have identical genomes, their differential gene expression states are achieved through genome modulation by epigenetic factors. The precise nature of the epigenetic mechanisms involved in intestinal homeostasis are still poorly understood as are the role of the epigenetic modifications and the complexes that define them4.Intestinal epithelium is produced when stem progenitors at the base of intestinal crypts exit from their proliferative state and differentiate6. Subsequently, 5hmC and TETs  have been profiled in many pluripotent and somatic cell types15 as well as neoplasias25. Consistent with a role in epigenetic reprogramming, the absence of TETs disrupts DNA methylation patterns26, hampers embryonic development28, impairs somatic cell transfer29 and promotes neoplasia31. In addition to being an intermediate in active DNA demethylation32, 5hmC has been shown to be a predominantly stable DNA modification13. Within the genome, 5hmC is located at transcriptionally active genes and regulatory elements14 and chromatin associated complexes36.Cytosine hydroxymethylation (5hmC) has been identified as the oxidation product of methylated cytosines (5mC) by the Ten-Eleven-Translocation enzymes (TETs)37, show reduced levels of 5hmC relative to the differentiated epithelium40. In this study we have mapped gene expression and 5hmC in purified stem progenitors and differentiated epithelium of the adult mouse intestine to identify which 5hmC-marked genes play a role in intestinal differentiation.The proliferating gut crypt progenitors, from which tumours can arise41 SI adenomas  and Cd24a_Neg (differentiated progeny) populations were used to isolate total RNA to measure gene expression by poly-A mRNA-seq and genomic DNA to map 5hmC by hmeDIP-seq  and differentiated cells (Cd24a_Neg) Fig. . The Cd2seq Fig. .Villin1 (Vil1) and Mucin2 (Muc2) and loss of stem cell marker Musashi 1 (Msi1)  was not significantly increased in Cd24a_Negs  and increased levels of Transducer of ErbB-2.1 (Tob1)  < 0.001 in differentiated cells Fig.  and S2. i1) Fig. . In agrells Fig. . The Panegs Fig.  as expec 44 Fig. . Moreoveb1) Fig.  support Tets transcripts levels were moderate and did not always mirror the increase in global levels of 5hmC upon differentiation, similar to our pervious observations for reduced 5hmC in human colon neoplasia25. Tet1 levels were low in Cd24a_Mid progenitors and went down with differentiation, Tet2 was reasonably abundant in progenitors with a mild increase in differentiated progeny and Tet3 the most abundant of the Tets with levels maintained in the Cd24a_Neg differentiated cells  was also significantly enriched in genes downregulated in Cd24_Neg cells  and histones  were downregulated whereas most factors involved in demethylation of DNA and histones were either moderately upregulated  or their levels maintained Fig. . Two exc59 showed a clear separation between the Cd24a_Mid and Neg samples together with increased affinity in the Cd24a_Neg cells  and 3′UTRs (p < 1.00e-15) were obtained. The remaining ~40% of 5hmC peaks were intergenic of which ~5% were within 5 kb of the transcription start sites – TSS)  by DiffBindlls Fig. , in linells Fig.  and S10.SS) Fig. . Gain ofSS) Fig. . Myc hadons Fig. , showingons Fig.  at these61. An IgG-only control was not included in our experimental design. The DNA pull-down conditions are identical for Cd24a_Mid and Neg cells and thus it would be assumed that non-specific sequences are unlikely to be statistically significantly different between the two conditions. With this in mind we conducted a motif discovery analysis (MEME-ChIP62) to assess sequence contents in peaks that gained or lost 5hmC in progeny. Given the peak size and p value cut-off used to select peaks with 5hmC change  is also relatively low. We therefore did not filter our data to remove CA repeats.Immunoprecipitation sequencing for DNA modifications has been reported to be intrinsically enriched for short tandem repeats (STRs) due to non-specific antibody binding affinity57 primarily for intragenic peaks that gain 5hmC  peaks, that marks actively transcribed geneshmC Fig. . A much hmC Fig. . H3K27ac25. H3K27me3 and bivalent H3K4/K27me3 sites also showed a minimal overlap with 5hmC. However, genes with promoters marked by H3K4me3 within 5 kb of the TSS (n = 11104) were for the most part highly expressed in Cd24a_Mid and Cd24a_Neg cells and more frequently gained 5hmC , albeit a considerable number lost 5hmC  at the cut-off used for significant changes in 5hmC (p.adj < 0.001)  we observed no correlation between expression levels and 5hmC for both intragenic and intergenic 5hmC peaks , with significant fold changes in 5hmC contents of all intragenic peaks per gene showed an overall positive but moderate correlation (r = 0.57 for p.adj < 0.001 in expression and 5hmC change)  Fig. . In agrege) Fig. . It is wge) Fig. . Neverthge) Fig. . Ten perge) Fig. .A similar behaviour was observed for the correlation between gene expression change and 5hmC change at intergenic peaks Fig.  albeit t65 or activator34 activities. In this regard, two additional observations are relevant. First, genes with very low (no) expression had considerable levels of 5hmC in progenitors, and the 5hmC levels were significantly reduced upon differentiation, whereas genes with undetected 5hmC showed low levels of expression in both progenitors and differentiated cells .Recent reports have also indicated that broadly permissive chromatin structures typify differentiation of the small intestine and that the phenotypic changes are primarily driven by transcription factor activities71 suggests epigenetic factors would strongly influence the functional outputs of transcription factor activities.Our data highlight pronounced changes in epigenetic factors in mouse small intestinal differentiation. Whether these changes follow or instruct intestinal differentiation, or both, remains largely unknown. However, orchestrated targeting of epigenetic complexes in intestinal neoplasiaIntestinal tissue was obtained from C57BL6/J and ApcMin mice that were housed and bred in the Cancer Research UK Cambridge Institute Biological Resource Unit (CRUK-CI BRU) in compliance with the statutes of the Animals (Scientific Procedures) Act, 1986, UK Home office guidelines and approved by the University of Cambridge Animal Welfare and Ethical Review Body.13. Slides were scanned onto an Aperio scanner for analysis. Antibodies go through a strict validation pipeline including a no primary antibody control staining to ensure secondary antibodies do not cross react with the tissue , Mouse anti-5mC monoclonal  and Rat anti-Mki67 monoclonal  was previously described43. Single cell suspensions of the whole small intestinal denuded epithelium were sorted into Cd24a-Mid_Cd45-negative_UEA-1-negative and Cd24a-Negative_CD45-negative_UEA-1-negative populations using Pacific blue conjugated Rat anti-CD24 , Alexa647 conjugated Rat anti-CD45  and Atto-488 conjugated UEA-1 (ULEX)  on a FACSAria (BD Biosciences) flow cytometer.Mouse intestinal epithelium was obtained by EDTA based fractionation as previously described72. Gene read counts were then derived using the MMSeq73 workflow. Differential gene expression analysis was carried out on these read counts using the Bioconductor package DESeq74. DEXSeq package75 was used to quantify reads within intervals obtained from Ensembl NCBIM37.67 gtf.Total RNA was extracted with the miRNeasy kit (Qiagen) following manufacturer instructions. Total RNA from four Cd24a_Mid and four Cd24a_Neg samples were submitted for library preparation by the CI-genomics core facility using the TruSeq RNA Sample Preparation Kit (Illumina). Barcoded samples were sequenced on a single lane of Illumina HiSeq to a depth of more than 200 million paired end (PE) 100-based pair (BP) reads. After demultiplexing, this yielded between 17.7–26.5 million PE reads per sample. These reads were trimmed to 50BP and aligned to mouse transcriptome version NCBIM37.67 using Bowtie version 0.12.825 using protein G magnetic beads (LifeTechnologies) bound with 5hmC rabbit polyclonal antibody  and 2 micrograms of adapter modified barcoded genomic DNA . Illumina sequencing reads were demultiplexed and aligned against the mm9 genome assembly using BWA. Quality metrics of the hmeDIP-seq enrichments were obtained with ChIPQC76. DiffBind package59 was used to quantitatively compare reads within peak sets obtained with MACS and differential affinity with the edgeR workflow after read counts from input DNA were subtracted. Mean read coverage around TSS was calculated using ‘GenomicRanges’ and ‘Rsamtools’ (Bioconductor); read coverage was normalized per million mapped reads, subtracted from input and mean TSS coverage plotted. Feature Enrichment analysis used the PAVIS online tool60. Summary statistics for hmeDIP-seq reads are in Supplementary Table Genomic DNA was obtained by phenol chloroform extraction and sonicated with a Bioruptor (Diagenode) to an average fragment size of 500 bp. The 5hmC pulldown was performed as recently described50 was used for gene ontology analyses of RNA-seq and hmeDIP-seq data.The goseq packageMotif analysis of hmeDIP-seq peaks was performed using the online tool with default parameters. The primary sequences within selected peaks were obtained with bedtools.The (CA)n Simple_repeat intervals were extracted from the UCSC RepeatMasker table. Intersection of 5hmC peaks intervals with (CA)n repeat intervals was conducted using bedtools. The (CA)n repeats had to be fully contained within 5hmC peaks (i.e. F = 1).B2m as normalizer  and cDNA prepared with SuperscriptIII reverse transcriptase (Invitrogen) and random primers. Targets were quantified with 1x Fast Sybr (ABI) and 1x Quantitect assays (Qiagen) or Taqman assays by the delta CT method using Supplementary informationSupplementary information2


CD36 expression by a Massively Parallel Reporter Assay (MPRA) and analyzed the results with a novel Bayesian statistical method. Ten eQTLs located 13kb to 55kb upstream of the CD36 transcriptional start site of transcript ENST00000309881 and 49kb to 92kb upstream of transcript ENST00000447544, demonstrated significant transcription shifts between their minor and major allele in the MPRA assay. Of these, rs2366739 and rs1194196, separated by only 20bp, were confirmed by luciferase assay to alter transcriptional regulation. In addition, electromobility shift assays demonstrated differential DNA:protein complex formation between the two alleles of this locus. Furthermore, deletion of the genomic locus by CRISPR/Cas9 in K562 and Meg-01 cells results in upregulation of CD36 transcription. These data indicate that we have identified a variant that regulates expression of CD36, which in turn affects platelet function. To assess the clinical relevance of our findings we used the PhenoScanner tool, which aggregates large scale GWAS findings; the results reinforce the clinical relevance of our variants and the utility of the MPRA assay. The study demonstrates a generalizable paradigm for functional testing of genetic variants to inform mechanistic studies, support patient management and develop precision therapies.CD36 is a platelet membrane glycoprotein whose engagement with oxidized low-density lipoprotein (oxLDL) results in platelet activation. The CD36 gene has been associated with platelet count, platelet volume, as well as lipid levels and CVD risk by genome-wide association studies. Platelet CD36 expression levels have been shown to be associated with both the platelet oxLDL response and an elevated risk of thrombo-embolism. Several genomic variants have been identified as associated with platelet CD36 levels, however none have been conclusively demonstrated to be causative. We screened 81 expression quantitative trait loci (eQTL) single nucleotide polymorphisms (SNPs) associated with platelet  CD36 levels for functionality using a high-throughput assay. Of the ten variants that were identified in that assay, one doublet, rs2366739 and rs1194196, were confirmed using additional molecular and cellular assays. Deletion of the genomic region containing rs2366739 and rs1194196 resulted in overexpression of CD36 in a cell culture system. This finding indicates a control locus which can serve as a potential target in modulating CD36 expression and altering platelet function in cardiovascular disease.Platelets are anucleate cells that are best known as regulators of vascular hemostasis and thrombosis but also play important roles in cancer, angiogenesis, and inflammation. CD36 is a platelet surface marker that can activate platelet in response to oxidized low density lipoprotein (oxLDL). CD36 has been associated with numerous cardiovascular traits in human including blood lipid levels, platelet count, and cardiovascular disease prevalence in human genetic studies. Human variability in platelet CD36 levels are associated with the platelet response to oxLDL. However, the genetic mechanisms responsible for the variability of CD36 levels are unknown. We examined 81 genetic variants associated with  Cardiovascular disease (CVD) remains the number one cause of death globally . MyocardCD36 is a transmembrane protein belonging to the class B scavenger receptor family expressed in platelets and variety of other cells . It bindCD36 RNA levels are lower in the black population and in women ..25].CD36 mRNA (Because eQTLs rs2366739 and rs1194196 are within 21 base pairs of each other and in high linkage disequilibrium we constructed single oligos which contains either reference (T-A for rs2366739-rs1194196) or alternate alleles (C-T) for both eQTLs. These genotypes account for 96% of the observed haplotypes from all populations. Out of the five tested constructs, rs819456 and rs2366739-rs1194196 showed significant transcriptional difference between their reference vs alternate allele as depicted by the luciferase levels . The rs2D36 mRNA  21, 27]CD36 mRNAD36 mRNA  was oppoD36 mRNA ). FinallD36 mRNA .−267) [−81) [rs2366739 has been described as an CD36 eQTL not just in our platelet data but in whole blood data from Võsa et al. (P = 1.3 x 10−267) . In addi7) [−81) . These rOne of the mechanisms by which gene expression is regulated is the binding of transcription factors to regulatory elements. To test if the difference in transcription between TA and CT alleles of the rs2366739-rs1194196 constructs is due to alteration in transcription factor binding affinity, we performed an electrophoretic mobility shift assay (EMSA) to compare binding of K562 nuclear extracts to probes derived from the two different haplotypes. The results show formation of a DNA:protein complex with twice as much affinity to the TA genotype probe than to the CT genotype probe .CD36 RNA expression, CD36 transcript levels were measured by qRT-PCR. In the cells with the rs2366739-rs1194196 locus removed, CD36 mRNA was ~13 times greater than the clones with the region intact  altering expression of a linked reporter gene, (2) altering protein binding in a gel-shift assay, and (3) altering endogenous CD36 expression when the locus containing these variants was deleted by genome editing. Importantly, rs2366739 has previously been associated with both platelet count (P = 9.13 x 10−10) and platelet volume (P = 5.27 x 10−11) [Our data presented here represents the first comprehensive analysis and testing of genetic variants associated with x 10−11) . Taken tThe analysis methods employed for our CD36 MPRA are another important contribution of this work. We applied t- and U-tests that have been employed in prior MPRA studies, but we also introduced a new and more comprehensive Bayesian approach. Our new method recapitulates the findings of the prior methods, but also identifies a variant that was missed by the prior approaches. A key advantage of our new statistical method is a generative stochastic model that probabilistically accounts for the discrete nature of NGS count data as well as sources of variation in the MPRA experiment without having to discard zero counts. CD36 expression in a complex manner that is affected by genotype.One of the weaknesses of this study is that we have been unable to identify the DNA-binding protein factor whose binding is altered by the variants. The rs2366739-rs1194196 is located in a genomic locus that contains ChIP-Seq signals for Histone H3K27 acetylation and is in a DNA hypersensitive region in primary and cultured hematopoietic stem cells. However CD36 expression in both K562 and Meg-01 cells, suggesting a negative regulatory activity also exists. There are differences between these experiments which can contribute to understanding our observations: The luciferase and EMSA experiments are performed in vitro using 70bp of genomic sequence surrounding rs2366739- rs1194196, either by itself as an EMSA probe or cloned into a luciferase vector. The genomic deletion is a 573bp deletion of DNA in a genomic chromatin context. In the genomic context, the locus is located 56 kbp and 92 kbp from the two transcriptional start sites of CD36. In the plasmid context, the transcription start site is immediately downstream from the cloned fragment. We hypothesize that additional negative regulatory factors bind to genomic DNA, outside the limited context used in MPRA and the 70bp fragment tested by luciferase and EMSA K562(CCL-243) and Meg-01 (CRL-2021) cells from ATCC were maintained in RPMI 1640 media  containing penicillin-streptomycin and 10% FBS.CD36 MPRA library, we used a p-value threshold of p < 1x10-6 to select the expression quantitative trait loci (eQTLs) most highly associated with CD36 expression in the PRAX study surrounded by 150bp of hg38 genomic context [ALAS2, HBG2, PKRRE, and URUOS expression as a set of positive controls  and Bayesian analyses were applied to identify variants that caused alterations to transcription activity by use of RNA-to-DNA ratios to examine allelic differences. Analysis of the barcode counts proceeds by computing the MPRA activity of each barcode as the log-ratio of the depth-normalized RNA counts to depth-normalized DNA counts and each variant’s corresponding “transcription shift”. The transcription shift of a variant is defined as the difference in activity between the alternate and reference alleles.The application of traditional frequentist tests to MPRA activities has been previously described , 26. TheWe designed and ordered oligonucleotides  containing the candidate MPRA-functional SNPs and 40bp of flanking genomic sequence . The ampNuclear extracts from K562 cells were isolated using the NE-PER Nuclear and Cytoplasmic Extraction Kit (ThermoFisher). 70 base pair sequences containing either the reference alleles for rs2366739 and rs1194196 (referred to as TA) or alternate alleles (referred to as CT) were amplified by PCR using MPRA plasmid library as template. Digoxigenin (DIG) labeled nucleotides  were used to create amplified sequences with DIG labeled base pairs. The sequences were purified by agarose gel electrophoresis and the QIAEX II Gel Extraction Kit .2, 1 mM DTT. 1 μg of Poly dI-dC was added to reduce nonspecific binding. The reaction was carried out for 30 mins on ice. The sample was mixed with 10X orange loading dye  and loaded on 6% acrylamide gel and ran for 4–5 hours with 0.5% TBE buffer. The DNA-protein complex was transferred on positively charged Biodyne nylon membrane  using 0.5% TBE for 45 minutes. The membrane was incubated in blocking solution for 30 mins at RT, followed by Anti-Digoxigenin-AP antibody containing blocking solution for 30 mins at RT. The membrane was then washed twice for 15 minutes each using washing solution, visualized using chemiluminescence, and quantified.10μg of nuclear extract was incubated with DIG labeled probes in buffer containing 10% glycerol, 20 mM HEPES, 30 mM KCl, 30 mM NaCl, 3 mM MgClhttp://crispr.mit.edu/) and CRISPOR (http://www.crispor.org)[To generate genomic deletion mutants, the CRISPR/Cas9 system was used as previously reported . Two guispor.org). Two guiPairs of complementary DNA oligos  were individually phosphorylated with T4 polynucleotide kinase  and then annealed. Each DNA oligo duplex had 5' overhangs  designed to be directly cloned into the BbsI or BsaI-digested and dephosphorylated AIO-GFP(Cas9) vector using the Quick Ligation Kit (NEB). The first and second sgRNA was cloned into the BbsI and BsaI sites, respectively, and confirmed by colony PCR and sequencing. The plasmid was transfected in K562 using Lipofectamine2000 (ThermoFisher) or Meg-01 with Nucleofection.. After 24 hours, the GFP positive cells were sorted by flow cytometry and individually seeded in a 96 well plate. Single colonies were expanded further and a cell line was established from a single clone. Deletion was confirmed by PCR and sequencing.Total RNA was isolated using Trizol Reagent (ThermoFisher). 3μg total RNA was used for first strand cDNA synthesis with the SuperScript III First-Strand Synthesis System (ThermoFisher). To evaluate relative expression levels of mRNAs, we performed qRT-PCR with the Power SYBR Green PCR master mix  normalized to Actin. We carried out real time PCR reaction and analyses in 384-well optical reaction plates using the CFX384 instrument .S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.


Thymic epithelial cells (TEC) effect crucial roles in thymopoiesis including the control of negative thymocyte selection. This process depends on their capacity to express promiscuously genes encoding tissue-restricted antigens. This competence is accomplished in medullary TEC (mTEC) in part by the presence of the transcriptional facilitator AutoImmune REgulator, AIRE. AIRE-regulated gene transcription is marked by repressive chromatin modifications, including H3K27me3. When during TEC development these chromatin marks are established, however, remains unclear. Here we use a comprehensive ChIP-seq dataset of multiple chromatin modifications in different TEC subtypes to demonstrate that the chromatin landscape is established early in TEC differentiation. Much of the chromatin architecture found in mature mTEC was found to be present already over earlier stages of mTEC lineage differentiation as well as in non-TEC tissues. This was reflected by the fact that a machine learning approach accurately classified genes as AIRE-induced or AIRE-independent both in immature and mature mTEC. Moreover, analysis of TEC specific enhancer elements identified candidate transcription factors likely to be important in mTEC development and function. Our findings indicate that the mature mTEC chromatin landscape is laid down early in mTEC differentiation, and that AIRE is not required for large-scale re-patterning of chromatin in mTEC. TEC are critical for attracting blood-borne hematopoietic precursor cells and controlling their differentiation and selection to mature, functionally competent T cells via the sequential processes of positive and negative thymocyte selection (Autoimmune Regulator (Aire) gene, amongst others, to ensure transcription of around 4,000 tissue restricted antigens (TRA) within mTEC  of AIRE-regulated genes are more frequently marked by the repressive chromatin modification trimethylation of lysine-27 of histone H3 (H3K27me3) and less frequently by the promoter-associated H3K4me3 . Patternlo and mTEChi determine AIRE regulatory status of tissue specific genes is poorly understood. In this study we demonstrate that much of the chromatin architecture observed in mTEChi is already present in mTEClo and construct computational models, based on the chromatin architecture around tissue specific genes, which accurately predict a gene's likelihood to be regulated by AIRE.The extent to which chromatin modifications in mTECC57BL/6 mice were obtained from Janvier . Mice were maintained under specific pathogen-free conditions. Experiments were in accordance with Swiss federal, cantonal, and institutional regulations.Fragmented thymi were digested repeatedly for 15–20 min at 37°C with 1 unit/ml Liberase TM (Roche Diagnostic) and 100 μg/ml DNaseI (Roche Diagnostic) in PBS, to obtain single cell suspensions. After the final digest, cells were pooled and labeled with biotinylated anti-EpCAM for positive enrichment by AutoMACS system (Miltenyi Biotec), and stained using the following directly labeled antibodies and reagents: FITC-anti-IAb , PE-anti-Ly51 , Alexa700-anti-CD45 , biotinylated anti-EpCAM , PECy7-anti-Sca-1 , Streptavidin-labeled PerCP-Cy5.5 (BioLegend), and Cy5-UEA1 (Vector Laboratories). The cells were exposed to 4′, 6-diamidino-2-phenylindole (DAPI) to identify dead cells and then sorted by flow cytometry  achieving a TEC purity of over 93%. Sorted TEC were pelleted and cross-linked for ChIP and kept at −80°C until use.Chromatin immunoprecipitation (ChIP) was performed as previously described  using PrWe used FastQC to assess read quality and Trimmomatic to remove adapter sequences (transposons or their reverse complement), trim the first and last 3 bases of each read based on sequencing quality, trim sequences based on a sliding window 4:15), and retain reads with a minimum length of 20 bases . BWA ve. Pre-ali, and retlo and mTEChi sorted in the same manner as described above underwent lysis, tagmentation, and PCR amplification as described in the ATAC-seq protocol , crop the first and last 3 bases of each read based on sequencing quality, and remove the 3′ 10 bases of each read to remove partial transposon sequences . We usedrial DNA , 16.hi were isolated as detailed above and sorted into SMART-seq2 lysis buffer containing RNase inhibitors  2-pass alignment . Gene quxi is the gene expression in tissue i where n is the number of tissues.The tissue specificity of genes was estimated using tau on the RNA-seq data available from the mouse ENCODE project , 32. xi hi AIRE ChIP-seq data was downloaded from GSE92597. Additional RNA-seq and histone ChIP-seq data was obtained from the mouse ENCODE project  and those without tissue restricted expression . Similar effects were seen for the magnitude of tissue specificity . Overall, this suggests that AIRE-dependent and AIRE-enhanced genes have a similar chromatin pattern in mTEChi and mTEClo.We hypothesized that similarities in individual chromatin marks around TSS between mTEC Figures , 7. A clp < 0.0001; Supplementary Figure p < 0.0001; Supplementary Figure hi , suggesting that differences in chromatin architecture at AIRE binding sites are unlikely to be the cause of transcriptomic differences between mTEC subtypes.Despite the chromatin architecture around TSS being similar in mTECd mTEChi . We found mTEChi . Intereslo and mTEChi. We therefore assessed the enrichment of transcription factor binding motifs curated from JASPAR, the open access data base of non-redundant transcription factor binding sites, to assess the enrichment of motifs within peaks containing enhancer chromatin modifications differentially identified between TEC subtypes  . Enhancer marks specifically present in mTEChi were enriched for a multitude of mouse phenotypes associated with abnormal lymphocyte development and function  as well as gene pathways upregulated in response to ionizing radiation . H3K27me3 peaks in mTEClo, but notably not in mTEChi, were enriched for known targets of the Polycomb Repressive Complex 2 .Even without large-scale alterations in chromatin patterns, it is likely that differences in chromatin marks between TEC subtypes may be enriched near genes involved with particular biological and molecular processes. An overlap analysis using each of the available chromatin marks in cTEC, mTEChi. Machine learning through neural network analysis was able to predict the AIRE status of genes from multidimensional measures of chromatin architecture in both mTEClo and mTEChi, although with significantly higher accuracy in mature over immature mTEC. Together these findings suggested that the chromatin architecture is broadly similar between mTEClo and mTEChi but is further refined through the course of mTEC differentiation with a more marked reduction in most active chromatin marks around AIRE-induced genes in mTEChi than in mTEClo. Moreover, an analysis of chromatin modifications was also able to identify potential novel master transcription factors of TEC development and functional pathways in which chromatin modifications specific to TEC subtypes were significantly enriched.We have identified differences in chromatin architecture between AIRE-regulated and AIRE-independent genes. Dimensionality reduction of the observed histone modifications revealed a clear separation of genes by AIRE regulatory status in mTEC. This distribution was also associated with tissue specificity and proportional expression in mTEClo prior to mTEChi differentiation. Previous studies only examined histone modifications in mTEChi. Consequently, the chromatin landscape prior to this point in TEC differentiation was previously unknown , as these may be instrumental in driving the bifurcation between cTEC and mTEC fate from the early, bipotent progenitor stage onwards  in TEC. d in TEC –38. Egr3elopment . Tbx21 a in mTEC . Of part onwards . Furtherhi was interesting because AIRE is thought to cause DNA double-strand breaks as part of its dynamic remodeling of chromatin (lo were enriched for genes known to be conventional targets of the Polycomb Repressive Complex 2 but this was not the case in mTEChi. This suggests that dynamic remodeling of repressive chromatin marks may differ over the course of mTEC maturation.Our gene ontology analysis (Supplementary Figure hromatin . It was hi have been purified for cells expressing specific tissue specific genes revealed that their chromatin accessibility and that of co-expressed genes were substantially higher relative to other loci (hi, this observation suggests that population level measures of chromatin modifications are unlikely to capture the state of individual cells (An important limitation of the approaches currently applied to study the chromatin architecture of TEC is that requirements for large cell numbers mean that histone modifications represent a population average as these can only practically be surveyed on pooled cells. Studies in which mTECher loci . Given tal cells , 9, 10. al cells .This study was carried out in accordance with the recommendations of local guidelines, Kantonales Veterinäramt BS. The protocol was approved by the Kantonales Veterinäramt BS.AH, NS-D, SZh, SM, and GH designed the experiments. NS-D, SZh, and SM performed the experiments. AH analyzed the data. AH, CP, and GH wrote the manuscript. All authors critically revised the manuscript.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.