Saccharomyces cerevisiae. We build a library of over 105 strains, each carrying a 120-nucleotide, chromosomally integrated, completely random sequence driving the potential transcription of a barcode. Quantifying the RNA concentration of each barcode in two environments reveals that 41–63% of random sequences have significant, albeit usually low, promoter activities. Therefore, even in eukaryotes, where the presence of chromatin is thought to repress transcription, chance transcription is prevalent. We find that only 1–5% of yeast intergenic transcriptions are unattributable to chance promoter activities or neighboring gene expressions, and these transcriptions exhibit higher-than-expected environment-specificity. These findings suggest that only a minute fraction of intergenic transcription is functional in yeast.It is debated whether the pervasive intergenic transcription from eukaryotic genomes has functional significance or simply reflects the promiscuity of RNA polymerases. We approach this question by comparing chance promoter activities with the expression levels of intergenic regions in the model eukaryote  It is debated whether the pervasive intergenic transcription from eukaryotic genomes has functional significance. Here, Xu et al. find that only 1–5% of yeast intergenic transcription is unattributable to chance promoter activity or neighboring gene expression. For example, although protein-coding regions make up only ~1.5% of the human genome and genic regions constitute about one-third of the genome, RNA transcripts are detected from >75% of the genome4. The biological significance of intergenic transcription, however, remains controversial6. The functional hypothesis asserts that intergenic transcripts largely result from the transcriptions of uncharacterized protein-coding genes or functional noncoding genes8. Indeed, some long intergenic noncoding RNAs (lincRNAs) are functional9, although the functionality of the vast majority of annotated lincRNAs is unclear10. Furthermore, it has been suggested that, even if the transcript of a noncoding gene is functionless, the act of transcription may regulate the expressions of other genes13. By contrast, the nonfunctional hypothesis15 posits that most intergenic transcripts excluding those resulting from the expressions of neighboring genes17 are nonfunctional products of chance promoter activities of intergenic sequences18. It is notable that, in the prokaryotic model organism Escherichia coli, ~10% of random sequences of ~100 nucleotides possess promoter activities comparable to that of a functional promoter and another ~60% can become such a promoter with just one mutation19. In the eukaryotic model organism Saccharomyces cerevisiae, noncoding RNAs can arise from nucleosome-depleted genomic regions by the promiscuous binding of RNA polymerase II20, but neither the probability with which a random intergenic sequence drives transcription nor the resulting transcriptional level is known, especially in the presence of chromatin that substantially represses transcription21. Most lincRNAs are not evolutionarily conserved23, which could mean a lack of function6 or a rapid turnover of lineage/species-specific function22. Intraspecific analysis yielded mixed results on the selective constraints on lincRNAs24.Many eukaryotes show pervasive transcriptions of intergenic regions25, a promoter as short as 69 nucleotides can be twice as strong as the CYC1 promoter26 and a 116-nucleotide synthetic promoter is similarly strong as the TDH3 promoter27. CYC1 and TDH3 are among 30% and 0.1% of the most highly expressed yeast genes, respectively. Hence, sequences of 120 nucleotides have ample opportunities to possess promoter activities yet are not too long to lower the experimental efficiency or increase the length variation of the synthesized oligonucleotides (because of the relatively high rates of insertion/deletion errors in oligonucleotide synthesis).In this study, we test the nonfunctional hypothesis by characterizing the frequency distribution of promoter activities of 120-nucleotide random sequences in yeast and comparing it with the frequency distribution of yeast intergenic expressions; the functionality of intergenic expression is invoked if the expression exceeds the chance expectation. Although the median length of yeast promoters is 455 nucleotides29 or were built on a core promoter scaffold33. We aim to estimate the probability distribution of promoter activities of random sequences, while past studies aimed to identify crucial elements of a particular functional promoter or sequence features of active promoters with a canonical scaffold.Our experiment differs from past studies of eukaryotic mutant promoters in both design and purpose. We investigate the promoter activities of completely random sequences, while past studies examined activities of promoters that were created by mutating a native promoter34, we integrated the above oligonucleotides to an intergenic position in the yeast genome that permits sensitive quantification of promoter activities  and a 20-nucleotide completely random sequence (the barcode) interleaved with invariant sequences (primer sites) Figs. , S1a. BeThe library, along with the controls, was cultured in three replicates in a rich medium (YPD) or a minimal medium (SCD) to the exponential growth phase. We extracted and amplified DNAs from barcodes Fig. S and sequWe focused on barcodes with at least 100 DNA reads in each replicate to allow relatively precise estimation of their expression levels; 49,169 and 146,291 barcodes passed this criterion in YPD and SCD, respectively. In YPD, the barcode DNA read number is highly correlated between replicates Figs. , S3a, b,PSP2—may not have the same expression level in different environments, hereinafter we use the median expression level of all yeast native genes in the relevant medium as the reference . About 0.024% and 0.029% of the random sequences have significantly higher promoter activities than the reference in YPD and SCD, respectively  binding sites (TFBSs) can easily arise in a random sequenceBSs Fig.  per randrs Figs. , S10, coed Figs. , S11, wied Figs. , S11. Geed Figs. , suggest39 of the same yeast strain and similar growth conditions as in our random promoter experiments. To allow fair comparisons with the expressions of 20-nucleotide barcodes, we divided each genic or intergenic region into 20-nucleotide contiguous windows, estimated the expression level of each window  by dividing the raw expression levels by the reference. We then compared the fraction of intergenic windows whose RELs significantly exceed various cutoffs with the corresponding fraction of barcodes. While both fractions decrease with the cutoff, the latter drops more quickly than the former and becomes smaller than the former when the REL cutoff is 0.3 in YPD , we observed larger and larger proportions of intergenic windows whose expressions cannot be explained by chance promoter activities  of neighboring genes (see Methods), it remains possible that some intergenic expressions reflect the bi-directional promoter activities or transcriptional readthroughs of neighboring genes. Indeed, we observed a significant positive correlation between the expression level of an intergenic region  and the mean expression level of its two neighboring genes . To assess the variation among the replicates, we individually analyzed the data from each replicate . The results from the individual analysis are overall similar to those from the combined analysis Table . FurtherS. cerevisiae, demonstrating the easiness for a random sequence to be transcribed by chance even in eukaryotes. However, the probability is ~0.025% for a random promoter to be significantly stronger than the median promoter activity of yeast native genes, contrasting the observation in E. coli where 2.5% of random promoters are as strong as the induced lac promoter40, which ranks in the top 3% of all E. coli native promoters in strength. This disparity could be due to the chromatin structure in eukaryotes21 and/or the lack of consensus sequence in yeast that is analogous to the short motifs bound by the canonical σ70-RNAP in E. coli40. Indeed, although random promoters with TATA boxes tend to be stronger than those lacking TATA boxes, a sizable fraction of the former (22.8% in YPD and 39.4% in SCD) do not have detectable activities  and the promoter strength in the absence of a core promoter scaffold. Contrasting the observation in native promotersty Figs. , S11. ThWe found that 1–5% of yeast intergenic transcriptions, especially those exhibiting relatively high expressions, are attributable to neither chance promoter activities nor neighboring gene expressions, so are putatively functional. Consistent with this finding is the observation of a higher-than-expected environment-specificity of the relatively high intergenic expressions. Due to the drastic genome size variation across eukaryotes, it is unclear whether our findings in yeast on intergenic expression are directly applicable to other eukaryotes. But our approach is likely adaptable for studying the functional significance of intergenic transcriptions in a wide variety of eukaryotes.YPD medium: 10 g/l of yeast extract, 20 g/l of peptone, and 20 g/l of glucose. YPAD medium: YPD medium plus 80 mg/l of adenine hemisulfate. SCD medium: 1.7 g/l of yeast nitrogen base (YNB), 5 g/l of ammonium sulfate, 0.79 g/l of complete supplement mixture (CSM), and 20 g/l of glucose. SCD − Ura medium: 1.7 g/l of YNB, 5 g/l of ammonium sulfate, 0.77 g/l of CSM without uracil, and 20 g/l of glucose.20) relative to the number of barcodes in the library means that sequencing or PCR errors are extremely unlikely to convert one barcode in the library to another one in the library. The random promoter and barcode were flanked by constant regions as primer binding sites  to screen for an intergenic region with a potentially high expression dynamic range. We found several candidates based on the following criteria: (1) at least 50 nucleotides long, (2) not overlapping with genes, (3) far from telomeres and centromeres, and (4) with a total of about 50–200 reads mapped to the region in 20 RNA-seq datasets in the above NCBI BioProjects. Using RT-qPCR, we quantified the expressions of the positive and negative controls (see the following section) integrated into five candidate locations  site. Next, we integrated the random promoter library at the SLP by CRISPR/Cas9 in a large-scale liquid transformation modified from an existing protocol43. Specifically, we followed the same procedure of a 100× transformation until the plating step. Instead of plating cells onto the selective plate, we grew all the transformants in liquid culture for selection. We expected to obtain about 1000 transformants per 1× transformation, equivalent to 100,000 transformants in a 100× transformation ideally. In the end, with five parallel transformations, we acquired about 200,000 transformants (estimated from sequencing). Besides random promoters, we also created a positive control (PSP2 promoter) and a negative control (no promoter) by CRISPR/Cas9. The PSP2 promoter is one of the weakest constitutive promoters used in yeast synthetic biology44 so is suitable for comparison with random promoters, which are expected to be weak.To improve the efficiency of CRISPR/Cas9-based integration of our library of random promoters, we first used CRISPR/Cas9 to integrate a synthetic landing pad (SLP) into the aforementioned genomic integration site of the laboratory yeast strain BY4741. The SLP contains a Five random promoters covering a large dynamic range (~50 fold) were chosen based on their relatively high consistency in expression level across replicates. Their promoter-barcode pairs were synthesized, amplified, and integrated into SLP to create these genotypes independently.10 cells were added to 2250 ml pre-warmed 2× YPAD medium . The cells were allowed to grow for 4.5 h at 30 °C and 200 rpm until the density reached at least 2 × 107 cells/ml. Standard transformation steps were then performed. 0.1% of the final transformants were plated onto SCD − URA plates to estimate the transformation efficiency, and the rest of the transformants were resuspended in 2000 ml of 2× SCD − URA liquid culture and redistributed to 250 14 ml falcon tubes each containing 8 ml of culture. The falcon tubes were put on a large roller and incubated at 30 °C for 48 h. Afterwards, 1 × 109 cells were collected, washed, and resuspended into 200 ml YPD medium and cultured for 24 hrs. This step was to remove the Cas9 plasmid and to revive cells. About 5×109 cells were then collected and diluted in 15% glycerol to a density of 5 × 107 cell/ml and were stored at −80 °C.YPAD medium (250 ml) was inoculated by a single colony of the yeast strain BY4741 picked from a fresh YPAD plate; the yeast culture was incubated for 24 h at 200 rpm and 30 °C. About 1.25 × 106 cells/ml for 24 h. The resulting culture was diluted in 20 ml YPD or SCD medium to an initial density of 5 × 106 cells/ml in a 100 ml flask, with three replicates per medium type. The cell cultures were always incubated at 30 °C with 250 rpm. After 8 hrs of culturing, genomic DNA was extracted from 3 × 108 cells per replicate using MasterpureTM Yeast DNA Purification Kit, whereas mRNA was extracted from 1.5 × 108 cells per replicate using RNeasy Mini Kit.The glycerol stocks of the random promoter library and controls were mixed in a 100:5:1 ratio of the random promoter library to the negative control to the positive control. The mixture was precultured in a 250 ml flask with 50 ml YPD at an initial density of 5 × 10To retrieve the linkage information between promoters and barcodes, we used pairs of primers containing Illumina sequencing adapters to amplify the promoter-barcode cassette in the construction of the sequencing library. Using 200 ng of genomic DNA as templates, we conducted two parallel 18-cycle PCR reactions for each biological replicate. The resulting amplicons were combined, purified, and sequenced by 150-nucleotide paired-end Illumina sequencing (HiSeq 4000).To generate DNA reads for the barcode region, we used a two-step PCR strategy. In the first step, primers with unique molecular identifiers (UMIs) were used to amplify the barcode region in a PCR reaction with only three cycles. The UMIs used were 6-nucleotide random sequences to mark individual DNA molecules. We conducted four parallel PCR reactions per biological replicate. The resulting PCR products for each biological replicate were purified and concentrated. In the second step, we used pairs of primers containing both the sample index and Illumina sequencing adapters to amply the previous products in an 18-cycle PCR reaction. The resulting amplicons were combined, purified, and sequenced by 150-nucleotide paired-end Illumina sequencing (HiSeq 4000).To generate cDNA reads for the barcode region, we first reverse-transcribed the mRNA into cDNA from 2.4 µg mRNA per reaction (SuperScript® III First-Strand Synthesis System for RT-PCR). The cDNA was then amplified using the aforementioned two-step PCR strategy with UMIs. The resulting amplicons were combined, purified, and sequenced by 150-nucleotide paired-end Illumina sequencing (HiSeq 4000).Notably, the number of genotypes obtained in YPD is only about one-third of that in SCD. This difference may be due to the higher growth rate of yeast in YPD than in SCD. Specifically, some cells may have a longer lag time by chance during the preculture stage, rendering their frequencies lower than those with a shorter lag time. This effect is intensified in YPD because of the higher growth rate in YPD than in SCD, reducing the number of genotypes obtained in YPD.120. Whether our random library could accurately capture the distribution of chance promoter activities depends on whether our library is randomly distributed in the large sequence space. In theory, the expected Hamming distance between two random 120-nucleotide sequences is fX is the frequency of nucleotide X. Under equal frequencies for the four nucleotides, two random promoters should differ at 90 sites on average. However, we found that our random promoter library does not have equal frequencies for the four nucleotides, probably because of the variation introduced in the oligonucleotide synthesis. Instead, we found fA = 24.3%, fT = 32.4%, fG = 25.7%, and fC = 17.6%. So, the GC content of the random promoters in our library is 43.3%, which incidentally is closer than the GC content in our design (50%) to yeast’s genomic GC content (38%). The mean Hamming distance expected from the above nucleotide frequencies is 88.78. We observed an average Hamming distance of 88.71 in our library, suggesting that the promoters in our library are randomly distributed in the large sequence space given the nucleotide frequencies.The total sequence space for a 120-nucleotide random promoter is as large as 4Potential PCR and sequencing errors have no impact on identifying random promoters or random barcodes because the expected sequence difference between two different barcodes or promoters (see the next section) is much greater than typical PCR/sequencing errors. One drawback of the HiSeq 4000 platform is a relatively high probability of index hopping. However, index hopping is unlikely in our experiments for the following reasons. First, we used two PCRs Fig. S: the fir45 to cluster the barcodes with a tolerance of two mismatches, and used CD-HIT46 to cluster the promoters with a tolerance of six mismatches. We allowed relatively high numbers of mismatches because the promoters (or barcodes) are completely random such that two distinct promoters (or barcodes) have an exceedingly low probability to be different by fewer than 7 (or 3) nucleotides. We discarded any barcode cluster connected with different promoter clusters. For any promoter cluster connected with multiple barcode clusters, all of these barcodes were counted toward the promoter cluster.For the promoter-barcode pair sequencing, we first filtered the sequencing reads to ensure that the barcodes were 20-nucleotide long and promoters were 120-nucleotide long. We clustered promoters and barcodes separately. We used Bartenderi is measured by NRCicDNA/NRCiDNA.For cDNA and DNA sequencing of the barcodes, we counted only one of the reads when these reads shared the same UMI. Afterwards, we divided the read count of each barcode by the total read count in each sample to obtain the normalized read count (NRC) for the barcode. The expression level of barcode 41 and that nucleosomes are depleted in yeast promoters47, random promoters with higher GC contents are expected to have lower activities. However, a trend opposite to this expectation was found. This trend cannot arise from the potential impact of the GC content on the efficiency of expression measurement because it was the barcode not the promoter sequence that was amplified in expression quantification.Given that the GC content is positively correlated with nucleosome occupancy37, we identified the TFBSs of all these TFs on the forward and reverse strands of the random promoters, respectively. The forward strand is the strand with the same orientation as the barcode transcription that we intended to measure. For each TF and each strand, we separated promoters into two groups based on whether there is at least one binding site in the promoter sequence. We then tested whether the promoter activity differs between the two groups by a Wilcoxon rank-sum test.Based on the position weight matrices (PWMs) of 196 yeast TFs and the suggested cutoffs in the ScerTF databaseThe promoter sequences of native genes were defined by from −500 nucleotides to either the translation start site Fig. S or the t38. Specifically, the data from 10 wild-type samples were used. The RNA-seq data from the SCD medium were generated by Berg et al. 39, and only the data from three wild-type samples were used.We analyzed the RNA-seq data from the YPD medium generated by Chou et al. 48 and include both protein-coding genes and RNA genes  based on the annotations from Ensembl (http://useast.ensembl.org/Saccharomyces_cerevisiae/Info/Index) and SGD (http://sgd-archive.yeastgenome.org/sequence/S288C_reference/rna/). Intergenic regions are the entire genome subtracting protein-coding genes with their UTRs, RNA genes, centromeres, telomeres, long terminal repeats (LTRs), and LTR retrotransposons from the reference genome. We combined the annotated UTRs from two previous studies50 and made additional 200-nucleotide outward extensions of both 5′ and 3′ UTRs. For protein-coding genes without annotated UTRs, we allocated 200 nucleotides outward from each end of the coding sequence as 5′ and 3′ UTRs, respectively. For RNA genes, we also added 50 nucleotides to each end of the gene. By doing the above, we aimed to minimize the influence of transcription from nearby genes or special sequence features on intergenic expression measures. We also varied the UTR extension length but found the results similar  of the RNA-seq data. For genic regions, a similar approach was used. For all windows from the same gene, their expressions were averaged to obtain the expression estimate for the gene. To estimate genic expressions by a canonical method, we employed the software StringTieS. cerevisiae to obtain the relative expression level (REL) in each replicate. We then used a one-sample t-test to test if the REL is significantly different from 1 at a false discovery rate (FDR) of 0.05 by Benjamini–Hochberg’s procedure52. We similarly tested if the REL differs significantly from 0.1, 0.2, and so on. This allows computing the fraction of intergenic windows with RELs significantly higher than any REL cutoff  cutoff  in off Fig. , as welloff Fig. .i, we first merged its NRC from the three replicates to obtain the total NRC (TNRC) and estimated its expression level Ei = TNRCicDNA/TNRCiDNA. Because eight of the barcodes are associated with the positive control, we also had eight Ei values for the positive control. For each random promoter-associated barcode i, we obtained eight estimates of its expression relative to the positive control by Ei/Ej, where j = 1 to 8 refers to the eight barcodes of the positive control. Based on the expression level of PSP2 relative to the median expression level of all yeast genes in RNA-seq data, we obtained the eight estimates of REL of each barcode, which is the expression level of the barcode relative to the median expression level of yeast genes. Finally, we used a one-sample t-test to test if the REL of a barcode is significantly different from 1 at FDR = 0.05 by Benjamini–Hochberg’s procedure. We similarly tested if the REL of a barcode differs significantly from 0.1, 0.2, and so on. This allows computing the fraction of barcodes with RELs significantly higher than any REL cutoff  cutoff  is significantly different from the negative control (using the eight expression estimates of the negative control) at FDR = 0.05 by Benjamini–Hochberg’s procedure.For barcode off Fig. , as welloff Fig. . We simiFirst, expression levels of barcodes or intergenic windows are measured relative to the reference, which is the median expression level of yeast native genes. Second, at each expression level cutoff , we calculated the fraction of barcodes or intergenic windows with significantly higher expressions than the cutoff in YPD and SCD, respectively. Third, for barcodes or intergenic windows with significantly higher expressions than the cutoff in one environment (YPD or SCD), we calculated the fraction of them with significantly higher expressions than the same cutoff in the other environment; the fraction is referred to as the shared fraction.For each intergenic region, we considered the transcriptions of the two directions separately. For a given transcriptional direction, the mean expression of all windows in an intergenic region was used as an estimate of the expression level of the intergenic region for that direction. To consider its neighboring gene expression, we used (1) the mean expression level of both of its neighboring genes (one on each side), (2) the expression level of the upstream neighboring gene given the transcriptional direction under consideration, or (3) the readthrough level of the upstream neighbor, which is the expression level of the upstream neighboring gene only when it has the same transcriptional direction as the direction under consideration; otherwise, the expression of the neighbor is set at 0. We then correlated the expression level of an intergenic region with the expression of its neighboring genes in each of the above three ways.REL bin where expressions of intergenic windows are fully attributable to chance promoter activities is 0.3–0.4 in YPD , we ranked them based on their neighboring gene expression level (following the first definition of neighboring gene expression in the preceding section) and then separated them into 50 groups with equal numbers of intergenic windows per group according to the ranking. We iteratively removed the group with the highest neighboring gene expression. For the remaining groups, we calculated the median expression level of their neighboring genes (ME1). From all intergenic windows (regardless of REL), we sampled the same number of windows as that in the remaining groups and calculated the median expression level of their neighboring genes (ME2); this sampling was repeated 1000 times to allow the estimation of the fraction of times when ME2 ≥ ME1. When this fraction exceeds the cutoff of 0.05, the remaining intergenic windows should be minimally influenced by the expressions of neighboring genes. We varied the cutoff and found the result similar  of the intergenic region, we calculated the number of nonoverlapping 120-nucleotide segments within the intergenic region by x] is the smallest integer equal to or greater than x. We then randomly chose N promoters from the random promoter library and compared the maximal expression of them (Esampled) with Eobs. We repeated the random sampling 100,000 times and used the fraction of times when Esampled > Eobs as the nominal P value for the null hypothesis that the expression of an intergenic region can be explained by chance promoter activities. The number of intergenic regions with expressions that cannot be explained by chance promoter activities is calculated by counting the number of intergenic regions with a P value <0.05 upon a Benjamini–Hochberg multiple-testing correction. The expressions of neighboring genes of the intergenic regions whose expressions cannot be explained by chance promoter activities are not significantly higher than the corresponding values for the rest of the intergenic regions  expression level of its windows as a proxy for its expression level ns Table . Hence, Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Our understanding of the mechanisms that modulate gene expression in animals is strongly biased by studying a handful of model species that mainly belong to three groups: Insecta, Nematoda and Vertebrata. However, over half of the animal phyla belong to Spiralia, a morphologically and ecologically diverse animal clade with many species of economic and biomedical importance. Therefore, investigating genome regulation in this group is central to uncovering ancestral and derived features in genome functioning in animals, which can also be of significant societal impact. Here, we focus on five aspects of gene expression regulation to review our current knowledge of functional genomics in Spiralia. Although some fields, such as single-cell transcriptomics, are becoming more common, the study of chromatin accessibility, DNA methylation, histone post-translational modifications and genome architecture are still in their infancy. Recent efforts to generate chromosome-scale reference genome assemblies for greater species diversity and optimise state-of-the-art approaches for emerging spiralian research systems will address the existing knowledge gaps in functional genomics in this animal group. Spiralia is, together with Ecdysozoa (e.g. insects and nematodes) and Deuterostomia (e.g. echinoderms and vertebrates), one of the three major lineages of bilaterally symmetrical animals , 2. AlthGene expression regulation occurs at both the chromatin level and the post-transcriptional and post-translational one, such as in the case of RNA-mediated regulation . ChromatCrassostrea gigas . Th. Th41]. in silico predictions in molluscs, with some research done in Platyhelminthes, rotifers and annelids [C. gigas, have received particular attention with the salient use of BS-seq revealing that the 5mC mark is primarily restricted to gene bodies of highly expressed genes [Octopus bimaculoides [Despite recent advancements, spiralian methylomes are notably scarce. Our understanding of DNA methylation in this group primarily comes from chromatography-based and methylation-specific restriction enzyme methods as well as annelids . Mollusced genes , 57–59. ed genes , 58, 60.culoides , challenculoides , 61, 62.culoides , 59.Platynereis dumerilii demonstrated the importance of 5mC in its regeneration capabilities [In silico prediction using the CpG observed and expected ratio showed higher global methylation levels at the embryonic stage than reported in C. gigas [C. gigas, P. dumerilii also experiences dynamic changes in its 5mC landscape during development and depletion in adulthood. This is not uncommon, as it has been observed in some vertebrates and invertebrates and is the result of TET-mediated demethylation [Annelids were surveyed early on for the 5mC machinery, but research on this topic has remained scarce over the years . Most stbilities . In siliC. gigas , 60, 70.hylation . It willS. mansoni, which is likely associated with the loss of DNMT1 and DNMT3 [Macrostomum lignano (adult) and Taenia solium (larva), consistent with the conservation of the DNMT genes in these species [T. solium, although the methylation patterns do not vary between CpG and non-CpG contexts [Adineta vaga described for the first time the presence of the 4mC mark catalysed by the horizontally-transferred N4CMT bacterial enzyme in an animal [Methylomes of three Platyhelminthes with parasitic lifestyles exhibit interesting 5mC patterns. While it is reportedly absent in nd DNMT3 , low met species , 73. Notcontexts , 52. Hown animal .C. gigas is widespread across spiralians. Unlike in vertebrates, there is no clear targeting of TEs in Spiralia. Thus, the broader question remains about DNA methylation’s role and importance to gene regulation in this animal group. Addressing these knowledge gaps requires expanding sampling efforts across this clade, particularly by dissecting the genomic distribution of 5mC at single-base resolution.In summary, the genome-wide 5mC profiles of spiralians generated to date already reveal great diversity across Spiralia. Gene body methylation is a recurrent signature in these methylomes. Still, it is unclear if the positive correlation between 5mC and gene expression observed in the oyster Histone-based regulation is one of the most versatile and logically intricate mechanisms of gene expression regulation , 76. AmoS. mediterranea [S. mansoni [Research interest in histones across a wide range of spiralian species, including molluscs , annelidterranea , and the mansoni .S. mediterranea neoblasts [In the first study utilising ChIP-seq, researchers identified distinct patterns of the transcriptionally permissive H3K4me3 mark deposited by the SET1 (SETD1A/B) and MLL1/2 (KMT2A/D) lysine-specific histone methyltransferases (KMT) orthologs in FACS-isolated eoblasts . As preveoblasts  identifi+ H3K27me3+ domains with a high degree of RNA Pol II pausing in planarian neoblasts. Likewise, the epigenetic regulation of oncogenes and tumour suppressor genes by MLL3/4 and SET1 has also been deemed evolutionarily conserved in stem cells, thanks to hPTM profiling in S. mediterranea [hPTM-targeting functional genomics in planarians has also confirmed the evolutionary conservation of bivalent domains across metazoan stem cells . These dterranea , 101. FuSchistosoma mansoni is one of the six agents that cause schistosomiasis [S. mansoni, and even the annotation of chromatin states [S. mansoni [S. mansoni life cycle [somiasis , 97, andn states . Early rn states . This is mansoni . It descfe cycle . This ulS. mansoni has focused on the characterisation of inhibitors of histone-modifying enzymes [S. mansoni, with some work done on the sex-determined regulation of gene expression [In this same line, a vast body of research in  enzymes , 108, pa enzymes , as pote enzymes . Furtherpression  and genepression . Immunolpression . Lastly,pression .in situ profiling with an exceedingly high signal-to-noise ratio. Except for a recently published protocol for ChIPmentation in S. mansoni [S. mediterranea, enabling the identification of regeneration-responsive enhancers and the proposition of a model to explain the chromatin accessibility rearrangements during wound healing for normal regeneration [S. mediterranea [smedwi-2 (a planarian PIWI ortholog) RNAi knockdown, the authors identified SMEDWI-2 as a critical repressor of transposon misregulation in neoblasts and a chromatin chaperone during cell differentiation [Recently, new methodologies such as ChIPmentation have overcome some of the technical barriers of ChIP-seq. However, the most remarkable advancements have been made through the innovative CUT&Tag , 118 and mansoni , 121, thterranea . ThroughIn summary, the wealth of data generated through ChIP-seq provides researchers in planarian regeneration and schistosomiasis with a tremendously valuable resource. Nevertheless, this must be extended to other critical spiralian phyla, such as molluscs and annelids, and refined by routinely incorporating CUT&RUN and CUT&Tag to gain a better picture of how genome regulation underpins the fascinating phenotypic diversity of Spiralia.via loop extrusion. The current model for loop extrusion describes how cohesin is loaded onto the DNA and reels the strand from both directions until a CTCF domain is reached, resulting in a loop-like structure [3D genome architecture describes how chromatin is spatially organised at higher levels in the nucleus, its resultant functionality and what mechanisms dictate this organisation. Two primary levels of regulation have been described thus far: compartments and topologically associated domains (TADs). Compartments refer to chromatin that shares similar accessibility and activity status. So-called A compartments denote transcriptionally active chromatin, whilst B compartments are more likely to display a heterochromatic state . TADs, otructure . It has Methods for identifying and understanding 3D genome architecture primarily derive from chromatin conformation capture (3C) techniques . Hi-C waDrosophila and Anopheles, in addition to a CTCF, have insect-specific architectural proteins such as BEAF-32, ZIPIC and the Elba complex components [Hox cluster [Caenorhabditis elegans [D. melanogaster and D. triauria found most TADs had been largely reorganised [Compartmental domains appear to be involved in genome organisation across eukaryotes, but the extent to which TADs bordered by CTCF are used across Metazoa appears to vary. TADs play an important and extended role in jawed vertebrates, yet this fact does not necessarily extend across Bilateria. For example, Ecdysozoa, the sister clade to Spiralia, shows significant variation. mponents . Despite cluster , suggest elegans . Therefo elegans . Within rganised , 139. Asrganised .E. scolopes  in multiple tissues, including muscle, epidermis and the intestine; and when genes found enriched in these cells are depleted using RNAi, regeneration is incomplete [S. mediterranea have focused on examining the heterogeneity in the planarian neoblasts [S. mansoni has also received some research attention, having been characterised using single-cell sequencing as both whole dissociated animals and in terms of specific tissues at various life cycle stages to help identify cell developmental trajectories [In Platyhelminthes, single-cell technologies have been used in several ways, mainly to produce large cell atlases for complete . Other seoblasts , profilieoblasts , 168, aneoblasts . The parectories , 170, geectories , 172 andectories , 173.S. mediterranea, an established model organism for studying whole-body regeneration. More species need to be profiled to capture cell type and expression diversity more accurately, and expand our collective understanding of processes undertaken in spiralians, like regeneration and metamorphosis. To do so, high-quality genomes of more Spiralians are paramount. Still, for those already sequenced, it will be essential to continue profiling the diversity of cell types in adults and during development to act as valuable starting points for comparative transcriptomics in other Spiralians. Most importantly, phyla aside from Annelida, Mollusca and Platyhelminthes must also be explored at the single cell level.Comparing the studies conducted in spiralians with non-spiralian clades reveals a major disparity in the volume of work. Many more non-spiralian animal species have been profiled from much-varied groups compared with spiralians, including almost all chordate main clades, and species from other groups such as Cnidaria, Arthropoda, Nematoda, Ambulacraria, Porifera, Ctenophora, Placozoa and Xenacoelomorpha . MoreoveN4-methylcytosine; 5mC, 5-methylcytosine; 6 mA, N6-methyladenine; ATAC-seq, assay for transposase-accessible chromatin with sequencing; BEAF-32, boundary element-associated factor of 32 kDa; BS, bisulphite sequencing; CGI, CpG-rich islands; ChIP-seq, chromatin immunoprecipitation followed by sequencing; CTCF, CCCTC-binding factor; CUT&RUN, cleavage under targets and release using nuclease; CUT&Tag, cleavage under targets and tagmentation; DNase-seq, DNase I hypersensitive sites sequencing; DNMT, DNA methyltransferase; dsRNA, double-stranded RNA; EHMT2, euchromatic histone-lysine N-methyltransferase 2; Elba complex, Early boundary activity complex; EM-seq, enzymatic methyl-seq; EZH2, enhancer-of-zeste homolog 2; FACS, fluorescence-activated cell sorting; FAIRE-seq, formaldehyde-assisted isolation of regulatory elements; hPTM, histone post-translational modification; H3K4me1, monomethylation of lysine 4 of histone H3; H3K4me3, trimethylation of lysine 4 of histone H3; H3K9ac, acetylation of lysine 9 of histone H3; H3K9me3, trimethylation of lysine 9 of histone H3; H3K27ac, acetylation of lysine 27 of histone H3; H3K27me3, trimethylation of lysine 27 of histone H3; KMT, lysine-specific histone methyltransferase; lncRNA, long non-coding RNA; MBD, Methyl-CpG-binding domain; MLL, myeloid/lymphoid leukaemia ; MNase-seq, micrococcal nuclease digestion with deep sequencing; PDX, pancreatic and duodenal homeobox; piRNA, piwi-interacting RNA; PIWI, P-element Induced WImpy testis; RNAi, RNA interference; RNA Pol II, RNA polymerase II; RRBS, reduced representation bisulphite sequencing; scRNA-seq, single cell RNA-seq; SET, su(var)3–9 enhancer-of-zeste and trithorax; SETD, SET domain-containing; SPRITE, Split-pool recognition of interactions by tag extension; TAD, topologically associated domain; TET, ten eleven translocase; TRACS, transient regeneration activated cell states; UMI, unique molecular identifier; XLOX, Xenopus laevis homeobox; ZIPIC, Zinc-finger protein interacting with CP1904mC, Our understanding of functional genomics in Spiralia is limited, although this is one of the three main lineages of bilaterally symmetrical animals with many economic and biomedical significant species.We need a better understanding of genome regulation in spiralians, with histone modification profiling restricted to, e.g. ChIP-seq analysis in two flatworm species.Methylation levels vary in Spiralia, but the low number of available methylomes prevents dissecting the evolutionary history of this epigenetic mark and its impact on gene expression regulation in Spiralia.Further work in 3D genome architecture in multiple spiralian phyla is needed to conclude high-order genome regulation in this group and animals in general.scRNA-seq has provided critical insights into regeneration, immune cell diversity and stem cell heterogeneity in a handful of phyla in Spiralia, but trialling other groups is needed for a complete understanding of spiralian biology.


The incubation behavior of geese seriously affects their egg production performance. Studies on incubation behavior have identified functional genes, but the regulatory architecture relationship between functional genes and chromatin accessibility remains poorly understood. Here, we present an integrated analysis of open chromatin profiles and transcriptome to identify the cis-regulatory element and their potential transcription factors involved in regulating incubation behavior in goose pituitary. Assay for transposase-accessible chromatin sequencing (ATAC-seq) revealed that open chromatin regions increased in the pituitary during the transition from incubation behavior to laying. We identified 920 significant differential accessible regions (DARs) in the pituitary. Compared to the laying stage, most DARs had higher chromatin accessibility in the brooding stage. Motif analysis of open DARs showed that the most significant transcription factor (TF) occupied sites predominantly enriched in motifs binding to the RFX family . While the majority of TF motifs enriched under sites of the nuclear receptor (NR) family  in closed DARs at the incubation behavior stage. Footprint analysis indicated that the transcription factor RFX family exhibited higher binding on chromatin at the brooding stage. To further elucidate the effect of changes in chromatin accessibility on gene expression levels, a comparison of the transcriptome revealed 279 differentially expressed genes (DEGs). The transcriptome changes were associated with processes of steroid biosynthesis. By integrating ATAC-seq and RNA-seq, few DARs directly affect incubation behavior by regulating the transcription levels of genes. Five DAR-related DEGs were found to be closely related to maintaining the incubation behavior in geese. Footprinting analysis revealed a set of transcription factors  which displayed the highest activity at the brooding stage. SREBF2 was predicted to be the unique differentially expressed transcription factor whose mRNA level was down-regulated and enriched in hyper-accessible regions of PRL in the broody stage. In the present study, we comprehensively profiled the transcriptome and chromatin accessibility in the pituitary related to incubation behavior. Our findings provided insight into the identification and analysis of regulatory elements in goose incubation behavior. The epigenetic alterations profiled here can help decipher the epigenetic mechanisms that contribute to the regulation of incubation behavior in birds. Incubation behavior is an important and specifically evolved reproductive behavior in birds, which is critical for the survival of offspring and thus adult reproductive success. Incubation behaviors lead to ovarian atrophy and the cessation of egg production, which seriously affects the egg production performance of domestic birds. Incubation behavior has been effectively genetically eliminated underlying the high intensity of artificial selection pressures . HoweverGoose reproductive behavior is driven by light stimulation and is tightly regulated by the hypothalamus-pituitary-gonadal axis (HPG) ,3,4. TheIncubation behavior can be induced via the combined action of estradiol, progesterone, and PRL in ovariectomized female avian . It has Epigenome changes were thought to affect the domesticated phenotype by influencing gene expression in birds ,10. The The pituitary gland plays critical roles in regulating many key physiological functions related to growth and reproduction . PreviouGene expression is controlled by accessible chromatin. Genes with chromatin accessibility are more likely to be regulated by TF and are differentially expressed at the mRNA level. We suspect that the potential mechanism of hormonal signaling in the formation of incubation behavior is related to chromatin accessibility. To characterize the chromatin state landscape of the pituitary of brooding geese, integrative transcriptome and chromatin landscape analysis was performed to identify cis-regulatory elements and their potential transcription factors involved in regulating goose incubation behavior. These results may provide new evidence concerning the correlations among chromatin accessibility, gene expression, and hormone signaling for goose incubation behavior.This study was carried out in strict accordance with the Experimental Animal Committee of Zhongkai University of Agriculture and Engineering (NO. 20201226012). All efforts have been made to minimize animal suffering.n = 3 for each stage). Assays were taken for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) (n = 2 for each stage). Serum samples were collected via venipuncture (n = 6 for each stage) and then stored at −80 °C before the next step. A section of the ovary was obtained and stained for observation (n = 6 for each stage).The Chinese natively bred Magang goose was obtained from the Wen Jianmin goose farm . We used twelve female geese in the present study; six were brooding (broodiness group) and the other six were continuously laying (laying group). Pituitary samples were collected, and transcriptome sequencing (RNA-seq) was performed . Nuclei pellets were incubated in a 50 µL transposition mix  for 30 min at 37 °C. Transposed DNA was then purified with VAHTS DNA cleaning beads  and then amplified for 12~15 cycles with PCR. Libraries were purified and assessed for fragment length distribution with a Bioanalyzer Qseq 100 Bio-Fragment Analyzer  and submitted for paired-end 150 bp sequencing on the NextSeq platform.Anser cygnoides genome obtained from the NCBI database (AnsCyg_PRJNA183603_v1.0) using Bowtie2 [The Fastp   above the 95% quantile or differential binding scores smaller/larger than the 5 and 95% quantiles were considered differential binding TFs.The non-redundant vertebrate TF motifs were downloaded from the JASPAR database (release 2020) . ComputaTotal RNA extracted as described above was used for transcriptome sequencing on Illumina HiSeq 4000 to generate 150 bp paired-end reads. Clean reads filtered by Fastp software were mapped to the goose genome (AnsCyg_PRJNA183603_v1.0) using STAR 2.0.4 software with default parameters, DESeq2  was perfhttps://metascape.org/, accessed on 1 May 2022) [p-value cutoff of 0.05 to judge statistically significant enrichment.The gene ontology (GO) enrichment analysis of genes was performed using Metascape (ay 2022) . KEGG enay 2022)  with a cp < 0.05 (*) or p < 0.01 (**) was used to indicate significance.The geese ovaries were collected immediately after slaughter. The dissected ovarian tissues were immediately fixed in paraformaldehyde. The slides were stained by Hematoxylin and Eosin (H&E) to investigate the structure, morphology, and histological changes. Serum samples were isolated from blood samples. The concentrations of PRL and LH in serum were measured by Enzyme-Linked Immunosorbent Assay (ELISA), according to the instructions of the Prolactin ELISA kit (Cat. No. EK18156)  and LH ELISA kit (Cat. No. EK11984) . Statistical analysis was performed by the Analysis of Variance (ANOVA). A probability level of p < 0.01) (p < 0.05) , large white follicles (LWFs), and small white follicles (SWFs), as well as the post-ovulatory follicles (POF) are visible, whereas the ovaries manifested as regression and atresia in the brooding stage geese. In addition, ovarian histology of the laying geese and broody geese were investigated. Consistent with previous results, many primary follicles and secondary follicles can be observed in the ovaries of the laying stage; however, the brooding geese had numerous primary follicles . We meas < 0.01) , while a < 0.05) .We isolated the nuclei from the geese pituitary and investigated the chromatin accessibility involved in incubation behavior. We profiled the chromatin accessibility regions in both the laying stages and the brooding stages . We genePearson correlation analysis and PCA analysis of ATAC-seq showed the profiles of chromatin accessibility in the pituitary were strikingly divergent between laying stage and brooding stage geese . A distrOpen chromatin regions can generally combine specific transcription factors to affect the transcription process. We investigated the differences between transcription factors in the pituitary during the laying and brooding stages at a genome-wide level. For comparison, we first calculated the proportion of genomic positions of peaks in each library and identified 9174 and 7155 differentially accessible regions of chromatin by merging results from the laying stage and the brooding stage, respectively. . Our datThe motif analysis suggested that the ranking of motif usage is a little different in the pituitary between the laying stage and the brooding stage. As expected, a significant enrichment of the binding motif of CTCF, SIX2, SIX1, BORIS, RORIN, and helix-turn-helix (HTH) superclass  was identified in the laying stage; likely reflecting that these transcription factors are the master regulator responsible for maintaining the physiological state of laying in pituitary e. Howeven = 806) showed higher accessibility in the broody samples  were identified between the laying and brooding pituitary using DiffBind (abs (Fold) ≥ 0.6, FDR ≤ 0.01). These DARs were annotated into 847 genes based on their distance to the nearest TSSs. The DARs were clearly classified into two distinct clusters based on the accessibility in each fraction based on the K-means clustering method a. The tw samples a.Based on the genome-wide functional regions, the majority of up-regulated peaks were mapped to intronic regions, followed by intergenic regions, and promoter regions; while the down-regulated peaks mostly come from intergenic regions, intronic, and exon regions of genomes. There was no significant proportional change between up-regulated and down-regulated DARs in the intergenic and promoter-TSS regions. However, the proportion of down-regulated DARs, in both exon and TTS regions, was significantly increased, whereas the proportion of up-regulated DARs was relatively decreased. On the contrary, a higher proportion of peak up-regulation occurred in intron regions, which may suggest that enhancers are an important regulatory layer in the broody stage b.p-values and enrichment score of the motifs. Here we refer to the peaks in cluster1 as closed DARs, and the peaks in cluster2 as open DARs. Motif analysis of open DARs at the incubation behavior stage showed that the most significant transcription factor occupies sites enriched in motifs for the HTH superclass , homeobox , NF1, and NeuroD1 in open DARs, while an enrichment of motifs for NR , homeobox (Bapx1), EFL-1,AT3G10030,ERF115, LIN-15B, and MYB  were identified in the closed DARs at the incubation behavior stage  in the pituitary underlying the two reproductive stages . PrincipGO enrichment analysis revealed that up-regulated DEGs were associated with lipid transfer activity and transporter activity in molecular function c, and doIn our study, the pituitary gland showed comparatively less variation at the level of gene expression and more change involving chromatin accessibility in goose incubation behavior. To determine the effects of chromatin accessibility on gene expression, no significant correlation was observed. We performed overlapping analysis of DARs and DEGs. Change in chromatin accessibility regulated a small set of DEGs. Five genes were both differently expressed and showed differential chromatin accessibility between the laying stage and the brooding stage . LOC1060p-value) above the 95% quantile or differential binding scores smaller/larger than the 5 and 95% quantiles were defined as differentially bound TFs between the laying stage and the brooding stage  binding on different open chromatin regions. We collected the vertebrate TF binding profiles from the JAPSAR database . After cng stage . The difng stage .Magang geese are typical short-day breeders, which have strong incubation behavior . Ninety In the present study, we comprehensively profiled the chromatin accessibility and transcriptome in the pituitary tissue of both laying and brooding geese. Accumulating evidence has emerged showing that changes in chromatin accessibility are associated with their regulatory role in the endocrinology function of the pituitary gland ,23,45. APhenotypic changes mostly result in the activity of cis-regulatory DNA elements, driven by changes in transcription factor (TF) binding . At the However, the NR superfamilies  were significantly enriched in closed DARs at the incubation behavior stage. The transcriptional activity of NRs is modulated by various ligands, including hormones and lipids . By sensConversely, the majority of differential gene expression between the laying and brooding stages were largely similar to those reported in previous bird studies in transcriptome level ,16. CholThe integration of ATAC-seq and RNA-seq results showed that chromatin changes are uncoupled from differential gene expression. We observed five gene changes in both chromatin accessibility and expression level. GPX3, which is known to be epigenetically regulated, showed significant down-regulation of mRNA expression levels, and the chromatin accessibility was altered. Loss of Gpx3 induces oxidative stress and increases prostatic intraepithelial neoplasia proliferation in prostatic cancer , and silWe downloaded the motif probability matrix corresponding to transcription factors from the Jaspar database for evaluating the activity of transcription factors at different stages. Four RFX transcription factors , BHLHA15, SIX1, and DUX4 were highly active in the brooding phase, suggesting that these factors are likely involved in goose incubation behavior. The RFX transcription factor family was identified as the highly active transcription factor in the brooding stage in our footprint analysis with a significantly up-regulated chromatin accessibility. This finding is very interesting because several previous studies have shown that the RFX transcription factors play a crucial role in the ciliogenesis involved in the spermatogenesis ,59,60.TFIn this study, we applied ATAC-seq and RNA-seq to identify the key DARs and DEGs in the pituitary that affect the incubation behavior of geese. We first identified 920 DARs and important transcription factors that might regulate the emergence of incubation behaviors by ATAC-seq from pituitary tissues of laying geese and broody geese. We obtained genes significantly related to steroid biosynthesis in the transcriptome. In addition, the combined analysis of ATAC-seq and RNA-seq identified five genes closely related to the incubation behavior of geese; SREBF2 may bind to the hyper-accessible region of the PRL gene and affect its expression changes. Finally, footprinting analysis revealed a set of transcription factors that displayed the highest activity at the brooding stage. Taken together, we provide a new insight into the regulatory elements of goose incubation behavior from a pituitary perspective, help reveal epigenetic mechanisms regulating bird incubation behavior, and provide a new clue for genome-assisted breeding in the poultry industry.


RARβ gene in the liver of HNF4α knock-out mice was higher versus wildtype controls, whereas oppositely, RARβ promoter activity was 50% reduced by the overexpression of HNF4α in HepG2 cells, and treatment with retinoic acid (RA), a major metabolite of vitamin A, increased RARβ promoter activity 15-fold. The human RARβ2 promoter contains two DR5 and one DR8 binding motifs, as RA response elements (RARE) proximal to the transcription start site. While DR5 RARE1 was previously reported to be responsive to RARs but not to other nuclear receptors, we show here that mutation in DR5 RARE2 suppresses the promoter response to HNF4α and RARα/RXRα. Mutational analysis of ligand-binding pocket amino acids shown to be critical for fatty acid (FA) binding indicated that RA may interfere with interactions of FA carboxylic acid headgroups with side chains of S190 and R235, and the aliphatic group with I355. These results could explain the partial suppression of HNF4α transcriptional activation toward gene promoters that lack RARE, including APOC3 and CYP2C9, while conversely, HNF4α may bind to RARE sequences in the promoter of the genes such as CYP26A1 and RARβ, activating these genes in the presence of RA. Thus, RA could act as either an antagonist towards HNF4α in genes lacking RAREs, or as an agonist for RARE-containing genes. Overall, RA may interfere with the function of HNF4α and deregulate HNF4α targets genes, including the genes important for lipid and glucose metabolism.HNF4α, a member of the nuclear receptor superfamily, regulates the genes involved in lipid and glucose metabolism. The expression of the  There is increasing interest in how micronutrients, such as vitamin A, play roles in the regulation of macronutrient metabolism. The major metabolite of vitamin A, all-trans-retinoic acid (RA), is known to regulate many physiological processes through its function as an activating ligand, or sometimes a repressive ligand, for nuclear retinoic acid receptors (RAR) including RARα, RARβ, and RARγ, each of which partner with retinoid X receptors , forming dimeric complexes that bind specifically to retinoic acid response elements (RARE) present in the promoter of target genes ,2,3,4. TRARβ gene itself [RARβ mRNA levels are significantly regulated by dietary vitamin A and by RA itself in the liver of adult rats, as the expression of RARβ mRNA was about 50 to 70% lower in vitamin-A-deficient rats compared with the vitamin-A-sufficient controls [RARβ mRNA in rats with low vitamin A status. This positive autoregulation of RARβ by retinoid compounds could be a means of elevating the level of RARβ receptor protein to, in turn, amplify the effect of RA on its target genes. Among the probable target genes for RARβ is CYP26A1, a member of the CYP26 subfamily of cytochromes P450 (CYP) enzymes, which specifically encodes a CYP that catalyzes the oxidative hydroxylation of RA, resulting in its inactivation [CYP26A1 gene is highly responsive to the presence of RA, as shown by the elevated expression of CYP26A1 mRNA during embryonic development, where CYP26A1 controls RA concentration in specific regions, as well as in the adult liver and cultured liver cells [CYP26A1 gene promoter, including one RARE located proximal to the TSS through which RARs bind and regulate the RA-mediated induction of CYP26A1 [Among the directly controlled genes targeted by RA for transcriptional activation is the e itself ,8. In eae itself . In earle itself ,10,11, tcontrols ,11. Prevtivation . The CYPer cells ,14,15,16 CYP26A1 ,18. CYP26A1 promoter and increases CYP26A1 mRNA expression in HepG2 cells following treatment with RA [Another important area of research concerns whether and how nuclear receptors interact to facilitate or attenuate gene expression. For CYP26A1, although RARs play significant roles in its induction, other nuclear transcription factor(s) may also play significant roles. One of these may be hepatocyte nuclear factor 4α (HNF4α), a highly conserved member of the nuclear receptor superfamily of ligand-dependent transcriptional factors . Previou with RA . HNF4α i with RA . HNF4α i with RA ,23,24. S with RA . Specifi with RA . HNF4α iHnf4a gene has been shown to result in embryonic lethality, we initiated this study to investigate the impact of acute loss of HNF4α in mouse livers on the expression of the Cyp26a1 gene by employing a temporally inducible hepatocyte-specific Hnf4a-null mouse line [Cyp26a1 gene expression would be reduced in the livers of Hnf4a-null mice, contrary to our expectation, we found that the expression of the Cyp26a1 gene is elevated higher in the absence of hepatic HNF4α in mice treated with RA compared with that in RA-treated F/FHnf4a littermate controls. This suggests that there may be complex interactions between HNF4α and retinoid signaling. Therefore, in the present study, we explored the role of HNF4α further, finding evidence that RA may act both as an agonist and as an antagonist of the HNF4α receptor in a manner dependent on the presence or absence of RARE sites in the promoter of target genes.As complete elimination of the use line ,28. WhilCyp26a1 gene in HepG2 cells upon treatment with RA [Cyp26a1 gene in the liver of intact mice. F/FHnf4a (control) and F/F;AlbERT2creHnf4a mice, with an inducible deletion of HNF4α in the liver, were treated orally with either oil as a vehicle or with RA for 6 h, after which the animals were euthanized for the collection of liver tissue. The total RNA was extracted from individual liver samples and analyzed for individual genes by qRT-PCR using 18S ribosomal RNA as the internal control. No Hnf4a mRNA was detected in the liver samples from F/F;AlbERT2creHnf4a mice treated with either the vehicle or RA (F/FHnf4a mice (controls), Hnf4a mRNA increased by 50% after RA treatment compared with the vehicle-treated Hnf4afl/fl mice . We found that the average mRNA level of RARβ was about five-fold higher in the liver of F/F;AlbERT2creHnf4a mice compared with F/FHnf4a mice  published a microarray study that showed that the expression level of F/F mice D. Retino/fl mice D. The knRARβ gene expression was induced 19-fold after 4 h and more than 50-fold after 24 h of treatment with RA, RARα and RARγ mRNA increased less than 50% at those two time points. As anticipated, HepG2 cells did not express endogenous CYP26A1 mRNA prior to retinoid treatment; however, upon treatment of the cells with RA, the CYP26A1 mRNA levels were increased by more than 500-fold after 4 h and by more than 5000-fold after 24 h. These results demonstrate both that the beta isoform of RAR is most sensitive to RA, and that the CYP26A1 gene is highly inducible by RA, providing a good model for further studies of the impact of HNF4a on the expression of these genes. HepG2 cells, a well-differentiated cell model for human hepatocytes , have beRAR genes in response to RA, we tested whether HNF4α could act on the promoter of the RARB gene. For this, we used a cloned fragment of the human RARβ gene spanning from −1.7 kbp from TSS to +0.217 kpb of the 5’UTR region [pGL3-Basic-luc vector used as the reporter gene. HepG2 cells were co-transfected with the RARβ promoter construct and the pRLTK plasmid together with either human HNF4α, the combination of RARα and RXRα (referred to below as RARα/RXRα), or all three transcription factors, for 24 h, after which the cells were treated further with either the vehicle or RA for 1 to 24 h prior to measurement of the luciferase activity. After 24 h of transfection, the cells overexpressing RARα/RXRα exhibited a 50% increase in RARβ promoter activity  promoter using the MatInspector computer program (www.genomatix.de accessed on 4 March 2017). In addition to the two DR5 RARE that are present proximal to TSS, we identified three putative HNF4α binding sites within this 1.7 kbp promoter region, as illustrated in pRLTK as an internal control, were co-transfected with either HNF4α, RARα/RXRα, or all three transcription factors, and then following transfection, the cells were treated with either vehicle or RA for 24 h. Compared with the FL construct , elimination of the 5’-end of the promoter region containing the putative HNF4α binding sites did not significantly impact the ability of HNF4α or RARα/RXRα to activate the RARβ promoter , which was predicted to be involved in the stability of the ligand binding pocket, had no effect on the activity of HNF4α  and R235 (Mutant #6), each of which has a direct interaction with the carboxyl group of the endogenous fatty acid, resulted in a loss of more than 80% of the transcriptional activity of HNF4α toward the APOC3 and CYP2C9 gene promoters, while most of its transcriptional activity was retained on the promoters of RARβ and CYP26A1, compared with the activity of WT HNF4α  is downstream of the putative but non-functional HNF4α response elements; (b) is highly conserved between human, mouse, and rat species; and (c) contains at least two DR5 RAREs, namely RARE1 and RARE2 together with a DR8, which is also believed to be responsive to retinoic acid receptors. RARE1 has been reported to be responsive to RAREs, but not to other hormone nuclear receptors [RARβ in hepatocytes following treatment with RA. The results from the DNA sequence analysis of the human aGt CAAAGt Ca sites as a general consensus element [EPO gene has been shown to be a direct transcriptional target gene of RA signaling during early erythropoiesis (prior to embryonic day E12.5) in the fetal mouse liver [EPO expression is dominated by HNF4α through the same RARE. As another example, hepatic glucokinase catalyzes the phosphorylation of glucose to glucose 6-phosphate, a step that is essential for glucose metabolism in the liver, as well as for the induction of glycolytic and lipogenic genes. The promoter of the glucokinase gene (Gck) has been shown to contain a functional RARE site that interacts with not only retinoic acid receptors but also with HNF4α and chicken ovalbumin upstream promoter transcription factor II (COUP-TFII) in rat primary hepatocytes, to integrate vitamin A and insulin signaling [CYP26A1 and increases the expression of the gene in HepG2 cells following treatment with RA [HNF4α is considered one of the most promiscuous DNA binders among the nuclear receptors and among transcription factors in general [ element , HNF4α h element ,38 and D element , presentignaling . Previou with RA . APOC3 and CYP2C9 genes. Whereas the carboxylic acid headgroup of the fatty acid interacts with side-chain groups of Arg235 and of S190, the aliphatic portion of the fatty acid occupies a long narrow pocket that is lined with hydrophobic side chains of the amino acid residues, including M235, L228, L229, and I355. Moreover, similar to the R212 residue in rats, HNF4α mutation of R221, which has been speculated to be involved in stabilizing the ligand binding pocket [In addition to possessing a DNA binding domain, HNF4α, similar to the other members of the hormone nuclear receptor superfamily, contains a distinct ligand binding domain. However, unlike the hormone nuclear receptors, HNF4α apparently does not need any added exogenous ligand for its function as a transcriptional activator. The crystal structure of the HNF4α ligand binding domain revealed that the protein, which adopts a canonical fold, is present in two conformational states, closed and open forms, within each homodimer . AlthougAPOC3 [CYP2C9 [RARβ and CYP26A1 genes, each of which contain multiple RAREs, could be activated by HNF4α only when the cells were treated with RA following transfection. We found mutation of the critical residues in the ligand binding pocket of HNF4α, namely S190 and Arg235, which are involved in the interaction with the carboxyl group of fatty acids, did not have any significant effect on the HNF4α transcriptional activation toward the promoters of RARβ and CYP26A1 genes in HepG2 cells treated with RA. However, mutation of M235, L228, and L229, which coordinate with the aliphatic portion of the fatty acid, were shown to be important in human HNF4α transcriptional activity toward the promoters of both CYP26A1 and RARβ genes. In addition, I347, which is involved in maintaining the structural integrity of the ligand-binding pocket of HNF4α [HNF4α has been shown to activate both APOC3  and CYP2 [CYP2C9  genes inRARβ gene expression in the livers of adult mice; (b) overexpression of HNF4α in HepG2 cells suppressed the RARβ gene promoter, but upregulated the promoter when the cells were treated with RA; and (c) HNF4α may act through at least one of the two functional RAREs present in the promoter of the RARβ gene in the region proximal to its transcription start site. As fatty acids have been reported as endogenous ligands for HNF4α, our results imply that RA may interfere either directly or indirectly with the interaction of these fatty acids with the amino acid side chains of critical residues in the HNF4α ligand binding domain and, as a result, RA may suppress the genes activated by HNF4α that contain no RAREs, while being able to activate those genes possessing RAREs through HNF4α. In either case, excess vitamin A, including RA itself, may interfere in the transactivation function of HNF4α and thereby deregulate the genes involved in lipid and glucose metabolism, as well as xenobiotic metabolism. As a result, excess vitamin A may contribute to dyslipidemia, diabetes, and metabolic syndrome, abnormalities for which HNF4α has been shown by others to be a significant factor. Because HNF4α is involved in several critical macronutrient pathways, and RA is the major functional metabolite of vitamin A as well as being a clinically important drug, recognizing the intersection of these factors helps to deepen our understanding of the complex ways in which hepatic lipid and carbohydrate metabolism are regulated.In summary, we showed that (a) loss of HNF4α resulted in the induction of the 2−air incubator. The cells were plated and used at 60 to 80% confluency. RA was purchased from Sigma-Aldrich Chemical Company  and prepared as stocks at 1 and 10 mM in ethanol. pGL3-Basic-luc, pRLTK, and pGEMT-Easy plasmid vectors were purchased from Promega  and pcDNA3.1 plasmid vector used for expression was from Invitrogen . MMLV Reverse transcriptase, RNase inhibitor, oligo dT nucleotide, dNTP, and T4 polynucleotide kinase were from Promega , while High Fidelity Tag DNA polymerase, Lipofectamine 2000 transfection reagent, and Trizol reagent were all from Invitrogen. Plasmid DNA Purification Kits and the DNA gel extraction kit were from Omega Bio-Tek . All of the restriction enzymes were obtained from New England Biolab . For the preparation of all other reagents in our laboratory, we followed the protocols reported in [Human hepatoma HepG2 cells were obtained from the American Type Culture Collection  and maintained in Eagle’s Minimum Essential Medium (EMEM) supplemented with 10% fetal bovine serum (FBS) and 0.5% penicillin−streptomycin (1x)at 37 °C in a 5% COorted in .Hnf4a-null mice  were described previously [F/FHnf4a and F/F;AlbERT2creHnf4a male mice were fed a diet containing tamoxifen (1g/kg) for three days and then returned to regular rodent chow for 3 days prior to treatment with either vehicle corn oil (0.1 mL/mouse) or RA in oil  administered by oral gavage. Six hours after dose administration, the animals were euthanized and tissues were harvested and snap-frozen in liquid nitrogen. The hepatocyte-specific eviously . Animal For our studies, the total RNA was extracted from individual liver samples using Trizol reagent, dissolved in DEPC-treated deionized water, and then analyzed both quantitatively and qualitatively by Nanodrop spectrophotometry . The individual RNA samples were run in denatured agarose gel by electrophoresis in order to check the quality of the RNA. For mRNA expression analysis, 2 µg of total RNA sample was first reverse transcribed in a 25 µL reaction and then diluted with autoclaved deionized water to 150 µL. The, 5 µL of the diluted sample was used to quantify the gene expression level by real time qPCR with SYBR Green containing reaction mix  using gene specific primer pairs, as shown in HepG2 cells were grown in EMEM with 10% fetal bovine serum and 1x penicillin−streptomycin in six-well plates until they reached to about 70 to 80% full confluency. The cells were then treated with RA at a final concentration of 1 µM for 0, 4, and 24 h, each in four repeats, after which the cells were washed with 1 × PBS and then subjected to RNA extraction with the Trizol reagent. The total RNA samples were cleaned further using the method reported previously . The RNApGL3-Basic-hCYP26A1-E4-luc (submitted to addgene.org), pGL3-Basic-hRARβp-luc (human RARβ2 promoter), pGL3-Basic-hCYP2C9p-luc, pcDNNA3.1-hRARα.hRXRα (submitted to addgene.org), and pcDNA3.1-hHNF4α were reported previously [pcDNA 3.1(+) at the BamHI and XbaI sites. All of the mutants were submitted for DNA sequencing analysis for verification at the Nucleic Acid Sequencing Facility of the Pennsylvania State University. For cloning the promoter of the RARβ mouse gene, a 1.9 kbp fragment spanning from −1918 bp to −17 bp upstream of the ATG codon was amplified by PCR from mouse genomic DNA using the primer pair shown in Taq DNA polymerase following the protocol recommended by the manufacturer. Similarly, a fragment of the promoter of human ApoC3 gene spanning from −1420 bp to +36 bp from the TSS was constructed by PCR from human genomic DNA using the primer pair shown in pGEMT-Easy plasmid vector by TA cloning and sub cloned into pGL3-Basic luciferase as previously described for other gene promoters [Construction of the plasmid vectors including eviously ,33,34,43romoters ,45. The pGL3-Basic-luc plasmid vector containing the promoter construct and pRLTK plasmid containing the Renilla-luc gene for transfection efficiency (7:1 w:w for DNA) and two expression vectors of 0.1 µg for each plasmid DNA. An equivalent amount of empty vector DNA (pcDNA3.1+), as the control, was used to fill the DNA for expression vectors. Following transfection, the cells were incubated with full growth medium containing 10% fetal bovine serum and 1 µM RA in ethanol at a final concentration of 0.001% ethanol. After incubation at 37 °C for up to 24 h, the cells were washed with PBS and then lysed to assay for firefly-luc and Renilla-luc activities using the DRL luciferase assay system from Promega in Luminator 20. Promoter activity was defined as the ratio of firefly-luc to Renilla-luc activity.HepG2 cells grown in EMEM with 10% fetal bovine serum and 1 x penicillin/streptomycin in T-75 flask were trypsinized and transferred into 24-well plates 1–2 days before transfection and grown to about 60 to 70% confluency. The cells were then transfected for 24 h with a total of 0.8 µg plasmid DNA and 2 µL of Lipofectamine 2000 per well in an EMEM medium containing 3% fetal bovine serum with no antibody following the protocol recommended for Lipofectamine usage. In a typical transfection experiment using two expression vectors, the plasmid DNA included 0.6 µg p < 0.05 was considered statistically significant.Each reported activity is the average of at least three wells with standard deviation of the mean. Data from animal experiments are reported as standard error of the mean. Where indicated, the data were analyzed by one-way ANOVA followed by Fisher’s least significant difference test using Prism 9 Statistical Software . A value of


Regulation of gene expression through enhancers is one of the major processes shaping the structure and function of the human brain during development. High-throughput assays have predicted thousands of enhancers involved in neurodevelopment, and confirming their activity through orthogonal functional assays is crucial. Here, we utilized Massively Parallel Reporter Assays (MPRAs) in stem cells and forebrain organoids to evaluate the activity of ~7,000 gene-linked enhancers previously identified in human fetal tissues and brain organoids. We used a Gaussian mixture model to evaluate the contribution of background noise in the measured activity signal to confirm the activity of ~35% of the tested enhancers, with most showing temporal-specific activity, suggesting their evolving role in neurodevelopment. The temporal specificity was further supported by the correlation of activity with gene expression. Our findings provide a valuable gene regulatory resource to the scientific community. Enhancers are non-coding elements that play a crucial role in the regulation of gene expression during brain development. Despite the availability of various techniques available to identify enhancers, their functional activity is relatively less understood, leaving a gap in our understanding of how enhancer behavior might regulate complex transitions of neurodevelopment. To address this, we utilized forebrain organoids, a 3D model system which closely mimics the complex cellular environment of the developing human brain, and employed Massively Parallel Reporter Assay (MPRA) to validate enhancer activity at various stages of forebrain differentiation, from induced pluripotent stem cells (iPSCs) to neuronal progenitors and cortical neurons. Our study provides a comprehensive catalog of over 2,300 enhancers, showcasing their temporal activity profiles during early neuronal development and offering valuable insights into their likely biological functions. This research advances our understanding of enhancer dynamics in brain development and offers new avenues for further investigations in this field. A central role of enhancers is to regulate gene expression by binding transcription factors (TFs) and other regulators able to modulate the transcription of target genes [It has been over 40 years since the discovery of the first DNA sequence capable of enhancing the transcription of a reporter gene . Since tet genes . Enhanceet genes  and theyet genes  which exet genes , 6, 7. Aet genes , 9, and et genes , 11, Chiet genes , 13, andet genes , have beWhile these approaches are highly informative and allow identifying the location of potential enhancers, none of these represent the definitive proof of their activity. To address this, reporter assays have been widely used to confirm the activity of predicted enhancers by testing their ability to drive expression of a heterologous reporter gene. Previously, this method was limited to testing one sequence at a time using transgenic technologies , 16, butHere, we used a genomic-integrated MPRA, the Lentiviral-based Massively Parallel Reporter Assay – LentiMPRA;  – to evaTo characterize the activity of a set of enhancers putatively involved in early human neurodevelopment, we used lentiMPRA in forebrain organoids to test the activity of a subset of the 96,375 gene-linked enhancers identified by ChIP-seq in our previous study conducted in human fetal cortex and human forebrain organoids . In the To investigate whether our candidate enhancers can elicit a time-dependent transcriptional response during early neurodevelopment, we infected three induced pluripotent stem cell (iPSC) lines with our lentiMPRA library and measured enhancer activity at the iPSC stage and at two terminal differentiation (TD) points during forebrain organoid differentiation: an earlier stage (TD0) when organoids were almost exclusively composed of proliferating progenitors and a more mature stage (TD30) when organoids were still harboring progenitors and actively generating layer 5–6 cortical postmitotic neurons S2 Fig; .Using DNA sequencing, we discovered that 95.1%  of the tested enhancer sequences in the original library were successfully recovered, with an average of 39.9 unique barcodes associated with each enhancer sequence . Out of the total 7,261 sequences, 94.9% passed stringent quality control . In the MPRA-active enhancers were defined as those having a signal significantly above the background distribution . AltogetThe background and the actual signal distribution were wide and overlapped significantly, limiting a clear discrimination between active and inactive enhancers, and reducing the power of the MPRA approach . Based oWe next intersected MPRA-tested enhancers with external ATAC-seq-derived peaks obtained from bulk and single cell data from fetal brains and forebrain-directed organoids , 35 as wTo further qualify the nature of MPRA-active enhancers, we compared the numbers of TFBSs within active and inactive enhancers. Our analysis revealed that while positive control enhancers had significantly more TFBSs compared to both scrambled negative controls and tested enhancers , we did We next took advantage of the fact that in the original dataset of Amiri et al. we identified putative target genes for the tested enhancers in their native DNA location, by either proximity in linear DNA and/or 3D DNA conformation datasets . We thenWe then correlated the difference in enhancer activity (measured by lentiMPRA) across time points with the difference in expression of the gene(s) linked to the enhancer in the endogenous genomic context. Comparing enhancer activity and gene expression at TD0 versus the iPSC stage, we found that MPRA-active enhancers upregulated in iPSCs or at TD0 were typically correlated (positively or negatively) with the difference in expression of their endogenous linked genes  and S7. One of the most active enhancers, validated both at TD0 and TD30 (S3 Table), partially overlapped with an exon located ~400kb downstream of the transcription start Site (TSS) of the Cut Homeobox 1 (CUX1) gene, a TF playing a critical role in upper layer cortical neuron differentiation, dendrite branching, and synapse formation , 40. AmoSpatial and temporal regulation of gene expression through regulatory regions, specifically enhancers, is essential for shaping the structure and function of the human brain during development. A number of biochemical techniques, such as ChIP-seq, ATAC-seq, DNase-seq have been used for identifying enhancers and characterizing their activity in neurodevelopment. Among those, the MPRAs have been adopted to test an enhancer’s ability to activate a synthetic reporter in a high throughput and context-independent manner, providing an orthogonal readout of enhancer activity. In this study, we used lentiMPRA in forebrain organoids to evaluate, over the course of brain development, the activity of ~7,000 gene-linked enhancers previously identified in human fetal tissues and brain organoids from a combination of histone-mark ChIP-seq and DNA conformational studies. Besides validating 2,352 enhancers, our analysis of the MPRA-active enhancers in relation to upstream binding TFs and downstream targets suggested important implications to the development biology of the human brain.By analyzing RNA-seq data from forebrain organoids collected at the same time points, we found that while there was no relation with number of potential TFBSs or TFs expression levels, MPRA-active enhancers are associated with highly expressed genes in the endogenous genomic context. Furthermore, differential enhancer activity, as determined by the lentiMPRA assay, tended to be correlated, in a positive or negative fashion, with differential gene expression across timepoints of neural differentiation. This provides an interesting insight into the capability of MPRA to detect enhancers associated with genes dynamically regulated during different stages of neurodevelopment.On a technical side, our study highlights a few essential limitations of MPRA techniques. Perhaps the major limitation of MPRA is its low sensitivity , estimated to be about 35% in our experiments, which likely explains limited reproducibility across replicates. For the later differentiation time points this could also be due to lentivirus regulatory element transgene silencing, which was previously reported to be an issue over differentiation of stem cells into other cell types . More daSimilarly to any bulk assay, using MPRA in a heterogenous system such as organoids, which are characterized by cellular diversity, reveals another caveat of the technique. Given the absence of cell-type information coupled to enhancer activity, it is likely that MPRA may be strongly biased towards revealing those enhancers present in the most abundant cell types. Indeed, when intersecting with scATAC-seq data obtained from the same organoids preparations at TD0 and TD30, there was a significant enrichment of MPRA-active enhancers only in the most abundant cell population, radial glia cells (Table S4) . Future Candidate enhancers were identified in Amiri et al 2018  by chromThe lentiMPRA plasmid library was constructed as previously described in Gordon et al., 2020 . BrieflyThe association between enhancer sequences and the barcodes was ascertained using Illumina MiSeq v2.0 sequencing with pair-ended 150 bp read length. The reads overlap for 30 bp in the middle of the enhancer sequences. Three MiSeq libraries were sequenced to obtain enough number of barcodes covering each tested enhancer sequence with the total number of 168 million reads. The barcodes with 15 bp length were sequenced at the same time with the same read names as the enhancer sequences with the same batch of MiSeq.The lentiMPRA library was packaged into lentivirus using the plasmid library, psPAX2 (RRID: Addgene_12260) and pMD2.G (RRID: Addgene_12259) and its titration was determined as previously described [Two iPSC lines were used for the lentiMPRA experiment: ACE1815  and 11251. Lines were generated from human skin fibroblasts obtained from a skin biopsy of normal individuals using a viral-free episomal reprogramming method , 47 at tThe lentiMPRA library was transduced into iPSCs at the undifferentiated stage. To provide 75%−80% confluency the next day, iPSCs were seeded on Matrigel-coated 10 cm dishes in mTeSR1 media supplemented with 10 uM Y-27632. After 24 hours, the cells were infected with the lentivirus library at an average of Multiplicity Of Infection (MOI) of 4–5 and incubated for 3 days with daily media changes to remove non-integrated virus. The experiment involved three independent replicate cultures, including two lines from different individuals (ACE1815 and 11251), and one technical replicate (ACE1815-1 and ACE1815-2). All cultures were infected at the same time and using the same lentiviral library.iPSC lines were differentiated into forebrain organoids as described in Jourdon et al 2023 . BrieflyThe infected cells were harvested at three different time points: in iPSCs after 3 days from the infection before starting organoid differentiation (iPSC stage), and in iPSC-derived forebrain organoids at earlier (TD0) and more mature (TD30) terminal differentiation points. Genomic DNA and total RNA were simultaneously extracted using the AllPrep DNA/RNA Mini Kit  following the manufacturer’s protocol. RNA samples were treated with Turbo DNase  to remove any residual DNA contamination. Sequencing libraries were prepared as previously described . BrieflyOrganoids were randomly selected and fixed in 4% PFA in PBS for 2–4 hours. The organoids were then cryopreserved in 25% sucrose overnight, embedded in O.C.T. (Sakura), and frozen on dry ice before being stored at −80°C. Serial cryosections were obtained with a thickness of 12–16 µm. Immunostaining was performed by incubating the sections in blocking solution  for 1 hour, followed by incubation with primary antibodies  and secondary antibodies . The slides were then mounted with coverslips using VECTASHIELD (Vector Labs) and imaged on a Zeiss microscope equipped with an apotome module and ZEN 3.3 (ZEN pro) software. Three cell lines were used for immunocytochemical analyses, and a minimum of four organoids per line were analyzed. Images were acquired randomly to cover the entire extent of the organoid. Antibody list: FOXG1  and PAX6 , EOMES , FOXP2 , GAD1 , HuC/D , SOX1 , CTIP2 .The association between enhancer sequences and barcodes was identified using MPRAflow association package  with thenextflow run association.nf --fastq-insert "R1.fastq.gz" --fastq-bc "barcode.fastq.gz" --design "design.fa" --name "MPRAflow" --fastq-insertPE "R2.fastq.gz" -w {workdir} --labels "labels.txt" --cigar 270M.The count for number of reads for each RNA and DNA barcodes in all 9 samples for all enhancers were calculated using MPRAflow count package  with thenextflow run count.nf --dir "DNA_RNA" --e "experiments.csv" --design "design.fa" --association "MPRAflow_filtered_coords_to_barcodes.pickle" --labels "labels.txt" --umi-length 15 --name "countUMI" --outdir {workdir} –mpranalyzeMPRAnalyze  was thenA mixed Gaussian distribution was applied to calculate the distribution of negative controls using the 135 negative control activity in 9 samples. The curve fit function in Scipy package  was applThe p-value for the activity of an enhancer to be higher than the negative control distribution was calculated using the enhancer activity (RNA/DNA) and the negative control distribution. The significantly active enhancers were then identified using the p-value of 0.05 after Bonferroni correction using the total number of enhancers tested.Using the negative control Gaussian distribution, we defined a cutoff for the RNA/DNA ratio that corresponds to a significant p-value. We then calculated the area under the positive control Gaussian and above the cutoff. The sensitivity was then estimated as a ratio of the area and the total area under the positive control Gaussian distribution.Enhancer activity (RNA/DNA) of candidate regulatory sequences and positive reference sequences was calculated by MPRAnalyze. Subsequently, Seaborn 0.11.0  clustermFIMO  was emplfimo --o {out_dir} JASPAR2022_tfbs input.fa.TFBSs identified by FIMO with FDR <= 0.05 were taken for further analyses. We quantified the number of TFBSs in each sequence and compared the distribution between validated and non-validated candidate regulatory sequences using Kolmogorov–Smirnov test. We further calculated the expression (RPKM) of the TFs which bind to these TFBSs in 97 TD0/TD30 organoid samples. The expression of TFs in validated and non-validated candidate regulatory sequences was compared using Kolmogorov–Smirnov test.Genes regulated by candidate enhancers were identified by Amiri et al.  taking cIntersection with external datasets were performed using BedTools  and the https://stuartlab.org/signac/articles/pbmc_vignette.html). Cell types were annotated using our annotated scRNAseq dataset and the label transfer method implemented in Seurat following online vignette . scATACseq peaks were then called using all reads in a sample or subsets of reads from each annotated cell type by running Signac function CallPeaks with default parameters.For each 10X scATACseq sample, fastq files were first processed by cellranger-atac v2.0.0 with default parameters and 10X prebuilt reference arc-GRCh38-2020-A-2.0.0. The resulted cell-by-peak count matrix was first processed and filtered by Signac following online vignette (