De-repression of Transposable Elements by Histone Hyperacetylation Leads to Sterile Inflammation in Preeclampsia

De-repression of Transposable Elements by Histone Hyperacetylation Leads to Sterile Inflammation in Preeclampsia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article De-repression of Transposable Elements by Histone Hyperacetylation Leads to Sterile Inflammation in Preeclampsia Madapura Pradeepa, Manthan Patel, Ahmed Ali, Adrianna Dabrowska, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6121510/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Preeclampsia is a pregnancy-associated hypertension disorder that affects 5–10% of pregnant women each year, resulting in adverse outcomes for both mother and child. Although the pathophysiology of preeclampsia remains somewhat unclear, it is linked to inflammation, senescence, and accelerated ageing phenotypes. Here, we aimed to investigate the altered epigenetic and transcriptomic changes in preeclampsia by performing genome-wide enrichment analysis of histone acetylation at histone H4 lysine 16 (H4K16ac) and H3 lysine 27 (H3K27ac) along with RNA sequencing analysis in preeclamptic and control placentas. We discovered transposable element (TE) families, including long terminal repeats (LTRs), endogenous retroviruses (ERVs), long interspersed nuclear elements (LINE), and short interspersed nuclear elements (SINE), are upregulated in preeclampsia. TEs upregulated in preeclampsia showed higher levels of H4K16ac, suggesting the contribution of this epigenetic modification in the regulation of TE transcription in the preeclamptic placenta. Genes closer to H4K16ac marked and upregulated TEs are expressed at higher levels in preeclampsia, suggesting that these TEs regulate transcription of nearby genes through their enhancer activity. Furthermore, we demonstrate that the upregulation of TEs results in double-stranded RNA (dsRNA) accumulation in trophoblast cells in the preeclamptic placenta. These TE-derived dsRNAs are detected by antiviral nucleic acid sensors, such as retinoic acid-inducible gene I (RIG-I) like receptors (RLRs), resulting in sterile inflammation due to the activation of the antiviral innate immune system. Our findings indicate that the epigenetic de-repression of TEs in the human placenta activates the type-I interferon response, leading to sterile inflammation in the preeclamptic placenta. Biological sciences/Molecular biology/Chromatin/Histone post-translational modifications Health sciences/Diseases/Cardiovascular diseases Biological sciences/Molecular biology/Transcriptomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The human placenta governs pregnancy outcomes and plays a crucial role in the health of both the mother and the offspring. Placental villi contain mainly proliferating cytotrophoblast cells (CTBs), differentiated syncytiotrophoblasts (STBs) in the outer layer, and invasive extravillous trophoblasts (EVTs), along with mesenchymal cells. Altered proliferation, differentiation, and reduced invasion of trophoblast cells into the maternal decidua cause abnormal placentation, which results in preeclampsia (PE) and other complications. Preeclampsia is also associated with inflammation, senescence, and ageing phenotypes in the placenta. It accounts for 14% of maternal deaths annually and is a major cause of premature births 1 , 2 . The trophoblast epigenome is dramatically reprogrammed during pregnancy, associated with an increased global DNA methylation and heterochromatin (H3K9me3 levels) with gestational age. In contrast, in H3K27ac, histone modification associated with active transcriptional enhancers and gene promoters reduces with gestational age 3 . Although the mechanisms of pathophysiology are unclear, epigenetic changes in trophoblast cells in the placenta are associated with Preeclampsia 3 , 4 . Transposable elements (TEs) constitute nearly half of the human genome and have significantly contributed to rewiring the gene-regulatory landscape by acting as species- and tissue-specific transcriptional enhancers 5 – 11 . Multiple TE classes, including LTRs, LINE1s (here on referred to as L1s) and Alu, are exapted to function as tissue-specific enhancers, including placental-specific enhancers 5 , 11 – 15 . Most TEs are transcriptionally repressed in somatic tissues but are detected at higher levels during early development, embryonic stem cells, neuronal lineage and placenta 16 – 18 . Interestingly, ERV envelope-derived genes such as Syncytins have co-opted the fusogenic role in CTBs to form multinucleated STBs 19 . Despite these clear links suggesting the importance of TEs in placental development and function, the impact of TE deregulation on placentation and pregnancy complications is less clear. The loss of heterochromatin and DNA methylation associated with ageing, senescence, cancer and neurological diseases causes the de-repression of multiple TE families, including L1, ERV, LTRs and SINEs, including Alu, leading to elevated type I interferon (hereafter referred to as IFN-I) mediated innate immune pathway 20 – 28 . TE-derived double-stranded RNAs (dsRNAs) possess virus-like structures recognised by nucleic acid sensing pattern recognition receptors (PRRs) such as RLRs such as RIG-I and melanoma differentiation-associated protein 5 (MDA5). Similarly, the c-GAS STING pathway detects cDNAs derived from TEs. These cytoplasmic viral dsRNA and DNA-sensing PRRs recognise TE-derived nucleic acids to activate IFN-I in the absence of infection 22 , 29 – 32 . Ageing, cancer and the senescence-associated loss of heterochromatin lead to the upregulation of TEs resulting in a sterile inflammation phenotype 28 , 33 , 34 . In the Drosophila ageing model, stimulating retrotransposon activity similarly increases mortality and accelerates a subset of ageing phenotypes 35 . Notably, preeclamptic placentas display high levels of senescence and an accelerated ageing phenotype, although the cause of this phenotype is unclear 4 , 36 , 37 . H4K16ac is a highly abundant modification of histone H4, enriched in euchromatin, particularly at gene bodies, enhancers and transposable elements (TEs) 11 , 38 . Although the mechanism remains unclear, the deregulation of H4K16ac is implicated in both senescence and ageing 39 , 40 . Recent findings demonstrate the role of lysine acetyltransferase 8 (KAT8/MOF), the enzyme responsible for H4K16ac, and KAT3B (p300), a general transcriptional coactivator that mediates the acetylation of various histone lysines and non-histone proteins in the proliferation, differentiation, and invasion capabilities of trophoblast stem cells (TSCs) 41 – 43 . Acetyl-CoA metabolism and histone acetylation levels, including H4K16ac, have also been crucial for differentiating STBs from TSCs 41 . However, recent evidence demonstrates that H4K16ac does not directly impact the expression of genes 44 , 45 . Instead, H4K16ac regulates the transcription of L1, ERVs, and LTRs in human embryonic stem cells. TEs enriched with H4K16ac function as enhancers to regulate genes in cis 11 . In this study, we aimed to investigate the epigenetic and transcriptomic changes associated with preeclampsia by performing genome-wide profiling of H4K16ac and H3K27ac levels along with transcriptomic changes in preeclamptic placentas. We found that TEs are expressed at higher levels in preeclamptic placentas, and this upregulation is linked to elevated levels of H4K16ac and reduced DNA methylation. H4K16ac marked and transcribed TEs contribute to the expression of nearby genes through their transcriptional enhancer activity. Our findings show that the derepression of TEs in the placenta leads to the accumulation of dsRNA, which is detected by the cytoplasmic dsRNA sensing RLRs, leading to the activation of the IFN-I pathway. We conclude that epigenetic derepression of TEs activates IFN-I, leading to elevated sterile inflammation in the placenta, which could contribute to preeclampsia-associated inflammation, senescence and accelerated ageing-like phenotype. Results Details of Preeclampsia samples used in this study We aimed to investigate altered genome-wide histone acetylation and transcriptomic changes in preeclamptic placentas. For this purpose, we recruited pregnancies in three groups: preeclampsia with a gestational age over 37 weeks without intrauterine growth restriction (IUGR) as (PE), preeclampsia with a gestational age under 37 weeks and IUGR (PE + IUGR), pregnancy-induced hypertension without proteinuria (PIH) and normotensive control group (control). The second group comprises three preterm pregnancies under 37 weeks and one pregnancy over 37 weeks with IUGR (PE + IUGR). Birth weight and gestational age were significantly lower in the second group of preeclampsia with preterm delivery and IUGR compared to the other groups (Table 1, and Extended Data Table 2). We conducted two independent replicates of H3K27ac and H4K16ac CUT&Tag and RNAseq for preeclampsia and healthy control samples from different parts of the biopsies. We also validated key findings by performing RT-qPCR. To address the limitation of the small sample size and to control for the gestational age differences in the placental biopsies, we additionally analysed publicly available RNAseq data from multiple cohorts representing different severities of preeclampsia (Fig. 1 b). All together, we analysed a total of 106 Preeclamptic and 78 control placentas, this included 111 RNAseq data from preeclampsia and gestational age-matched placental control placentas. The additional details on the public cohorts are listed in extended data table 1. H3K27ac but not H4K16ac levels associated with gene expression level in placenta We used two independent sites of the placenta per biopsy for H3K27ac and H4K16ac CUT&Tag and poly-A enriched RNA sequencing (Fig. 1 a). Differential gene expression analysis of PE versus healthy control and PE + IUGR versus healthy control placentas revealed expected upregulation of preeclampsia-associated transcripts such as INHBA, ENG, FSTL3, LEPTIN, HTRA4 and FLT1 in preeclamptic placentas compared to healthy controls (Extended Data Fig. 1 ). Having confirmed preeclampsia-associated gene expression signature in our cohort, we next included two independent RNAseq cohorts containing preeclampsia and gestational age-matched controls (GSE114691 containing 21 control, 20 PE and 20 PE + IUGR; and GSE186257 containing 18 control and 26 severe-PE samples), along with two additional cohorts that lacked gestational age-matched controls (Fig. 1 b and Extended Data Table 1). Differential expression analysis performed independently for gestational age-matched datasets or integrating all the cohorts 46 – 49 confirmed upregulation of preeclampsia-associated genes compared to control placentas (Figs. 1 a-d, and Extended Data Fig. 1 a and 1 b). Gene ontology analysis of the upregulated genes (from Fig. 1 c) showed an expected enrichment of preeclampsia-associated terms, including angiogenesis, hypoxia, inflammatory response and EVT cell types. Notably, metabolic pathway genes, including glycolysis, are enriched (Extended Data Fig. 1 e and 1 f) 50 . Next, we aimed to map genome-wide changes in histone acetylations in PE; we confirmed the overall data quality and similarity among CUT&Tag data from independent placental samples and replicates (Extended Data Fig. 2 a). H3K27ac showed enrichment at genes upregulated in PE. However, the H4K16ac level was unaltered and showed no enrichment at upregulated genes in preeclampsia (Fig. 1 e, Extended data Fig. 2 b and c). Meta-analysis revealed the expected increase of H3K27ac at gene promoters or transcription start sites. However, H4K16ac is increased at gene bodies and enhancer features in PE + IUGR compared to control (Extended Data Fig. 2 d and 3 b). Next, we analysed H4K16ac and H3K27ac levels at the protein-coding genes. Of the three clusters obtained from k-means clustering, cluster 1 showed genes having higher H3K27ac levels and higher expression in PE + IUGR compared to healthy controls (Fig. 1 e-g). The functional annotation of genes in cluster 1 recapitulated the terms associated with changes in PE, such as hypoxia, estrogen signalling, and glycolysis, as represented in MSigDb Hallmark gene sets, alongside diseases such as preeclampsia and hypertension (Fig. 1 f). Overall, we conclude that H3K27ac levels are associated with upregulated genes in PE, while H4K16ac levels are not associated with gene expression levels. This is consistent with recent reports suggesting that H4K16ac is dispensable for gene expression in human cell line models 11 , 44 , 45 . H4K16ac acetylated domains are enriched at L1s, LTRs and Alu s in Preeclampsia Furthermore, to evaluate the changes in acetylation levels across genomic features, we analysed the coverage of CUT&Tag reads within genomic bins of 1 kb using 0.5 kb sliding windows for H3K27ac. For H4K16ac, we used 10 kb bins with 5 kb sliding windows due to its broad enrichment pattern. We observed more genomic bins (domains) gained with H3K27ac (H3K27ac+) and H4K16ac (H4K16ac+) in the PE + IUGR placentas compared to the control samples (Fig. 2 a and b, Extended Fig. 3 a). Notably, H4K16ac + domains specific to PE + IUGR overlapped with higher number of full-length L1s (> 5kb), Alu , and LTR elements compared to genes. Meanwhile, H3K27ac + domains specific to PE + IUGR overlapped with a higher number of genes, Alu , and LTR elements but not at full-length L1s (Fig. 2 b-d). Individual Alu , LTR, and full-length L1s that overlapped with H4K16ac + domains (from Fig. 2 b) displayed an overall increase in H4K16ac in PE + IUGR compared to controls (Fig. 2 c and 2 d). H4K16ac + TEs regulate the expression of genes in the cis. Overlap-analysis of public histone modification ChIPseq datasets across different cell lines showed H4K16ac + domains in PE + IUGR overlap at enhancer and promoter marks such as H3K4me1 and H3K4me3 (Extended Data Fig. 3 b). We next compared H3K4me3 and H3K4me1 ChIPseq signal from the chorionic villi (ENCODE datasets) at the H4K16ac-rich TEs (Alu, L1s (> 5kb) and LTRs) in control and PE + IUGR respectively (Fig. 3 a). These H4K16ac + TEs displayed higher levels of histone modifications associated with regulatory elements such as H3K4me1 and H3K4me3 and lower repressive marks, H3K9me3 (Fig. 3 a). To determine the cis-regulatory effect of these H4K16ac + TEs, we analysed expression level of genes at different distances from H4K16ac + L1s, LTRs and Alu s. Genes closer to H4K16ac + TEs in PE + IUGR placenta had significantly higher expression than those distantly located genes (Fig. 3 b). In contrast, genes close to TEs (LTRs) that lacked H4K16ac in Control and PE + IUGR are expressed at lower levels in the Control and PE + IUGR placenta respectively (Extended Data Fig. 3 c). Higher levels of active enhancer-associated histone modifications and low levels of repressive histone modifications at H4K16ac + TEs (Extended Data Fig. 3 b & 3 d), suggests their cis-regulatory role. We found PE + IUGR-specific increase in H4K16ac but not H3K27ac at TEs such as MER52A, L1M2, L1PA2, HERVL-E, AluJb and AluSx located near the IFIT gene cluster (Fig. 3 c). Notably, these H4K16ac + TEs loop to IFIT1, IFIT2 and IFIT3 genes. This suggests that H4K16ac + TEs could function as enhancers to maintain the expression level of these IFIT genes. Since we found a higher level of TE transcripts in the preeclamptic placenta, we asked whether transcribed TE loci could function as enhancers to regulate genes in cis. Differential expression analysis of individual TEs using the TElocal tool revealed 6118 up-regulated compared to 4168 down-regulated TEs in PE + IUGR compared to healthy controls (Fig. 3 d). Notably, more L1 and LTRs are upregulated than downregulated in PE + IUGR, whereas a similar number of Alu s are up and downregulated. Analysis of the expression profile of genes near these TEs revealed that genes closer to upregulated TEs have higher expression levels in PE + IUGR than genes located away from upregulated TEs (Fig. 3 e). In contrast, genes closer to the downregulated TEs are expressed at lower levels in PE + IUGR. Notably, genes close to upregulated TEs showing higher expression in PE + IUGR are associated with EVTs, eclampsia and pregnancy complications (Extended Data Fig. 3 e). Overall, our data support the model that higher levels of H4K16ac at TEs and TE transcription contribute to the expression of genes close to these TEs, contributing to an altered gene expression programme in preeclampsia. Transposable elements are derepressed in the preeclamptic placentas. Analysis of TE expression in our cohort at the individual loci revealed 1611 upregulated and 915 downregulated L1s; similarly, 898 upregulated and 432 downregulated ERV internal elements and/or LTRs. A similar number of Alu elements were up and downregulated in PE + IUGR (n = 4 PE + IUGR) samples compared to controls (n = 5) with two replicates per sample (Fig. 3 d, Extended Data Fig. 4 a and b). Next, we analysed RNAseq data from five independent cohorts, including two with gestational age-matched placental biopsies. We used the TEtranscript tool 51 , to analyse TE expression level at the subfamily level, which avoids bias against mapping to evolutionarily younger TEs, revealed significant (padj < 0.05) upregulation of 40 and downregulation of 15 TE subfamilies. Unsupervised clustering based on differentially expressed TE subfamilies from five independent cohorts led to a clear separation of preeclampsia from healthy control samples, irrespective of the disease severity and PE classification (PE, severe PE, PE with IUGR and superimposed PE) and timing of the onset of PE (early or late) (Fig. 4 a). Moreover, a separate analysis of gestational age-matched samples (GSE114691 and GSE186257) and our cohort also showed similar pattern of TE expression changes in preeclampsia (Fig. 4 b and Extended Data Figs. 4 c-e). This analysis confirms that preeclampsia-specific differences in TE levels are not due to differences in the gestational age of the placenta. We next validated preeclampsia-specific upregulation of L1s and HERVs by performing RT-qPCR using primers that detect multiple HERVH loci and the 5’ UTRs of evolutionarily young L1 elements (L1HS–L1PA5) as well as L1 ORF1 (Figs. 4 c and 4 g). TEs were not significantly upregulated in pregnancy-induced hypertension without proteinuria (PIH, gestational hypertension), suggesting that the upregulation of TEs is specific to preeclampsia (Figs. 4 c and 4 g). Differentially expressed L1s were not due to pervasive transcription, as intergenic (for protein-coding genes) full-length L1s (> 5kb) showed significantly higher expression in PE + IUGR compared to healthy controls (Fig. 4 d). L1s exhibit higher H4K16ac and reduced DNA methylation levels in preeclamptic placentas. DNA methylation plays a vital role in transcriptional repression of TEs in somatic tissues. Reduced heterochromatin and DNA methylation contribute to ageing and senescence-associated increases in TE transcription 25 . The reactivation of HERVs and L1s associated with reduced DNA methylation increases with age 33 , 52 . Thus, we asked whether higher H4K16ac levels at L1s (Fig. 4 e) are associated with low DNA methylation levels. Using an ELISA-based global methylation assay, we measured 5-methyl Cytosine (5-mC) across L1 repeats in placental biopsies. PE and PE + IUGR placentas showed significant hypomethylation at L1s in preeclampsia compared to the control placenta (Fig. 4 f). L1s constitute ~ 18% of the human genome, suggesting a global hypomethylation in PE, consistent with the previous findings demonstrating hypomethylation signatures associated with preeclampsia 53 . We conclude that low DNA hypomethylation and higher H4K16ac contribute to the derepression of TEs in the preeclamptic placenta. Elevated type-I interferon pathway in Preeclamptic placenta. PRRs, including RIG-I receptor-like receptors (RLRs), MDA5 recognise TE-derived dsRNA, whilst cyclic GMP-AMP synthase (c-GAS) recognise reverse transcribed TE cDNA as invading viruses, triggering the antiviral IFN-I innate immune pathway 22 , 29 – 31 . We next asked whether the derepressed TE transcripts could activate the IFN-I pathway. Differential expression analysis of IFN-pathway genes (n = 250) 54 revealed upregulation of 22 and downregulation of 9 IFN-associated genes in PE (all PE combined for cohorts) placenta compared to controls (padj 0.5). Upregulated genes in PE samples included IFNA1, ISG15, MX1, MX2, and STAT2 (Fig. 5 a), which are IFN-I responsive genes, which are also associated with senescence phenotype. Pathway enrichment analysis of all upregulated IFN genes showed enrichment for the IFN-I pathway. In contrast, downregulated genes did not show enrichment for the IFN-I pathway (Fig. 5 b). Upregulated IFN genes did not show significant changes in H4K16ac and H3K27ac levels (Extended Data Fig. 5 a) between control and PE (PE and PE + IUGR) samples. This suggests that IFN-I upregulation is not due to a general increase in histone acetylation levels at these genes. Quantifying the expression level of IFN genes in another independent gestational age-matched cohort confirmed significant upregulation of IFN-I genes in PE and PE + IUGR samples compared to controls (Fig. 5 d and Extended data Fig. 5 b and 6 ). RT-qPCR validation in our placental biopsies further confirmed the upregulation of IFN-I genes in preeclamptic placentas (Fig. 5 g). Although there were no overall changes in histone acetylations at upregulated IFN-I genes. Interestingly, however, TEs located close to and loops with upregulated IFIT genes (Fig. 5 b) gain H4K16ac but not H3K27ac level (Fig. 3 c). This suggests that H4K16ac + TEs could maintain the expression level of some of the IFN-I pathway genes through their enhancer activity. Overall, we conclude that higher levels of L1s, ERV internal regions, LTRs and Alu s in the preeclamptic placenta correlate with elevated levels of the antiviral IFN-I pathway in preeclampsia. Cytoplasmic dsRNA sensing by RIG-I in syncytiotrophoblasts in PE placenta Quantification of the expression level of PRRs in the RNAseq datasets showed preeclampsia-specific upregulation of cytoplasmic dsRNA sensors, RIG-I and MDA5 (Fig. 5 d). In contrast, c-GAS and STING, a downstream sensor of c-GAS, showed downregulation in the PE and PE + IUGR placentas. We aimed to investigate whether a higher level of TE transcripts in PE placentas leads to the accumulation of dsRNA and whether RLRs recognise these transcripts. We performed a series of immunostaining using antibodies against RLRs (RIG-I and MDA5), along with antibodies that recognise dsRNA (J2) (Fig. 6 a and Extended Data Fig. 6 b). J2 antibodies are widely used to detect TE-derived endogenous dsRNAs that accumulate upon ageing- or senescence-associated de-repression of HERV, LTRs and Alu s. Notably, endogenous dsRNAs are generally below the detection limit of J2 antibody in the absence of viral infection or epigenetic drug-mediated de-repression of TEs. Confocal immunofluorescence imaging of preeclamptic placental biopsy cryosections showed enriched dsRNA (J2) signal in the outer layer of the placental villi (Fig. 6 a, Extended Data Fig. 6 b). Similarly, RIG-I and MDA5 levels were detected at a higher level in multiple independent preeclamptic sections compared to healthy controls, particularly at the outer layer of the placental villi. These results are consistent with the RNAseq data showing significant upregulation of RIG-I and MDA5 in PE placentas (Fig. 5 c). We found clear colocalisation of RIG-I and MDA5 with J2 at the outer layer of the placental villi, suggesting these RLRs recognise accumulated cytoplasmic dsRNA in the preeclamptic placentas. Furthermore, we found RIG-I colocalisation with cytotrophoblast marker Keratin 7 (KRT7) and SDC-1, a syncytiotrophoblasts marker, but not with Vimentin, a known mesenchymal marker in the placenta, suggesting that dsRNA accumulation and RIG-I sensing occurs at the outer syncytial layer of the placental villi. Moreover, RNAse-III treatment reduced the dsRNA signal, demonstrating the specificity of the J2 antibodies to endogenous dsRNAs (Fig. 6 b). Altogether, these immunofluorescence data supports our conclusion that TE-derived endogenous dsRNAs are recognised by RLRs, leading to elevated IFN-I signalling in preeclampsia. Discussion Most pregnancy complications, such as preeclampsia, IUGR, premature birth, and stillbirth, are linked to elevated levels of inflammation; the exact causes of inflammation in the placenta remain unclear. We demonstrate that TE upregulation linked to H4K16ac hyperacetylation activates the antiviral IFN-I pathway by recognising TE-derived nucleic acids by viral nucleic acid sensor proteins. Particularly, dsRNA sensors PRRs such as RIG-I and MDA5 are upregulated, while cytoplasmic DNA sensors c-GAS and STING are downregulated. Furthermore, we found a higher signal and colocalisation of dsRNA and RIG-I signal in trophoblasts in preeclamptic placenta, demonstrating activation of specific IFN-I pathways in preeclampsia. Haemochorial placentas in humans increase the risk of vertical viral transmission, thereby exerting selection pressure to develop robust antiviral mechanisms for foetal protection. Recent evidence indicates that TEs are implicated in elevated levels of sterile inflammation in placental villi due to the TE-derived IFN pathway 55 , which is beneficial and confers antiviral protection to the foetus during pregnancy 56 . However, TE-derived nucleic acids are associated with sterile inflammation in ageing, cancer, senescence and neurological disorders. Elevated inflammation is also associated with senescence and accelerated ageing in the preeclamptic placenta 57 – 60 . These findings suggest that although a low level of TE expression could benefit pregnancy through stimulation of antiviral pathways 61 , deregulation of TEs could also cause elevated sterile inflammation associated with premature ageing and senescence in the placenta. Elevated IFN-I could also contribute to syncytial knot-mediated sprouting and apoptosis, a common phenotype in preeclamptic placenta 62 , 63 . IFIT cluster genes ( IFIT1, IFIT2, IFIT3 and IFIT5 ) are generally not expressed in most cell types; they are the early responders to IFN-I activation upon viral infection and are involved in dsRNA signalling. Consistent RNAseq data showing upregulation of RIG-I, MDA5, and many IFN-I genes downstream to PRRs, including IFITs, are also upregulated in the preeclamptic placenta. Immunofluorescence data shows a higher level of dsRNA and RIG-I in the outer layer of the placental villi, which comprises CTBs and STBs. Upregulation of dsRNA sensing PRRs and clear colocalisation of dsRNA with RIG-I supports the model of TE-derived nucleic acids triggering the IFN-I pathway. We did not see a general increase in histone acetylation level at the upregulated IFN-I genes (Extended Data Fig. 5 a), suggesting that higher IFN-I upregulation is not due to global increase in histone hyperacetylation mediated derepression. However, enhancers near the IFIT gene cluster harbour many TE elements that gain H4K16ac in PE, suggesting H4K16ac + TEs could contribute to maintaining higher expression levels of IFIT genes. This is similar to the finding that shows SINE elements acting as enhancers to regulate IFN in mouse model 64 . A higher level of IFITM (an IFN-1-regulated gene) is shown to inhibit cell fusion in STBs, which can contribute to pregnancy complications 65 , consistent with the smaller birth and placental weight in early-onset PE 66 . Our findings demonstrate that TE derepression leads to elevated IFN-I in PE and PE + IUGR, which provides a possible molecular explanation for the preeclampsia phenotype, including reduced syncytiotrophoblasts in PE. Further in vitro experimentation is needed to establish the direct impact of TEs and upregulation of IFN-I pathway, including IFITs and ISGs, on TSC proliferation, differentiation of CTBs to STB and EVTs and invasion properties of EVTs. Further understanding of the function of individual IFN-I proteins will facilitate therapeutics development and identification of potential clinical biomarkers. Recent work shows an interesting link between pregnancy-specific upregulation of HERVs and elevated IFN-I response, and this TE-IFN-I axis is co-opted to activate haematopoietic stem cells and erythropoiesis 55 . Similarly, primate-specific Alu and rodent-specific B1 SINE RNA drive type III interferon (IFN-III) expression and antiviral protection in the placenta 56 . Unlike IFN-III in the placenta, which can protect the foetus from viral infection, we speculate that elevated IFN-I can pose a high risk of pregnancy complications due to morphological changes in the placenta. 67 TEs are known to be upregulated during early development, embryonic stem cells and trophectoderm-derived trophoblast stem cells in the placenta 16 – 18 . Trophectoderm-gained repressive H3K9me3 domains are preferentially deposited at hominoid-specific TEs such as LTR12, MER11B, HERVH, and HERVK9-int that are differentially enriched in placenta 68 . Epigenetic remodelling of these TEs is essential for early development, placentation and embryo implantation 17 . Mechanisms through TEs deregulated in the preeclamptic placenta are unknown. Higher H4K16ac domains in PE, particularly at TEs, suggest that a higher level of H4K16ac contributes to TE transcription in preeclamptic placenta, possibly due to loss of heterochromatin, as we found a reduced level of DNA methylation at L1s. Altered histone acetylations, including H4K16ac, can influence trophoblast phenotype, as the altered acetylation pathway is also known to affect TSC proliferation, differentiation and invasive properties of TSCs 41 . What causes altered H4K16ac levels in preeclampsia is unclear; we speculate that altered glycolysis and hypoxia pathways (Extended Data Fig. 1 d) could contribute to altered nuclear acetyl-CoA levels in the preeclamptic placenta. Altered nuclear acetyl-CoA is demonstrated to alter H4K16ac in trophoblast stem cells; this can influence the differentiation of TSCs to STBs 41 . The contribution of diet-induced changes in acetyl-CoA in altered levels of histone acetylation at TEs also cannot be ruled out, as the higher levels of nucleocytoplasmic acetyl-CoA can serve as a substrate for histone acetylation in growth or fed conditions compared to starved conditions (reviewed in) 69 . This agrees with the hypothesis that chromatin modifications enriched at TEs, constituting nearly 50% of the genome, can be a source or sink for metabolic by-products such as acetyl-CoA (discussed in) 70 . Alternatively, dsRNAs released from syncytial knots or necrotic trophoblast cells expressing TEs could also trigger IFN-I activation and an "antiviral" immune response in PE 67 . TE transcription is epigenetically repressed by DNA methylation and chromatin-modifying complexes that mediate H3K9me3, reviewed in 71 . Our data suggests that TE deregulation in preeclampsia is due to higher histone acetylation and reduced DNA methylation levels. We have previously demonstrated the H4K16ac role in TE transcription in stem cells 11 ; this study provides novel disease-relevant context on how altered histone acetylation at TEs can contribute to placental phenotypes associated with PE. Limitations of the study: Although upregulated Alu , LTR, HERV and L1 can form dsRNAs 72 – 74 , which of these TEs are the source of dsRNAs in preeclampsia is unclear. It is also unknown whether these preeclampsia-associated changes have a causal role in the PE phenotype or appear as a consequence of premature ageing and senescence phenotype in the preeclamptic placenta. In the absence of animal models of PE, further experimentation using trophoblast differentiation and organoid models will reveal the direct impact of TE deregulation on the inflammatory phenotype observed in preeclampsia. In summary, low sterile inflammation is important for antiviral protection during pregnancy. Elevated IFN-I signalling due to TE-derived nucleic acids contributes to sterile inflammation phenotype. Further experimentation using in-vitro TSC models is needed to establish the direct effect of deregulated TEs in a chronic inflammatory state associated with preeclampsia 75 . Further research into understanding the complex interplay between epigenetic deregulation of TEs and inflammation in preeclampsia will lead to interventions targeting epigenetic regulators and inhibiting TE activity or inflammatory pathways in inflammation and age-related diseases, including PE. Materials and Methods Ethics statement Patients were enrolled at the Royal London Hospital, Barts Health Trust from May 2021 to March 2022 as a part of a PE Epigenetics study with written informed consent before participating and ethical committee approval (REC 21/SS/0010) from the UK Health Research Authority. Demographic and clinical details were obtained from the Clinical Record Service (CRS) Cerner Millennium and BadgerNet Clevermed database. Immediately after birth, we collected the samples from the fresh placenta. Four samples (1cm x 1cm) of villous tissue were accessed by trimming away the basal plate with scissors and a scalpel, then cutting out a piece of the exposed villous tissue and discarding the basal plate tissue. Dissected villous tissue was immediately transferred to a dish containing PBS, and the tissue was cut into small pieces, then snap-frozen immediately and stored at -80°C. We performed two replicates of CUT&Tag for H3K27ac and H4K16ac and polyA-RNAseq from different parts of the placenta biopsies. Sequencing data that failed were excluded for further analysis. Recruitment criteria Babies born to women with PE were defined as recommendations from the International Society for the Study of Hypertension in Pregnancy ( ISSHP ) 76 , PIH, and normotensive control who delivered at the Royal London Hospital and were willing to provide informed consent. Exclusion criteria: Infants who were critically ill and babies with significant congenital and genetic abnormalities. Three groups were recruited: normotensive control, PE with GA> 37 weeks without IUGR, and PE <37 weeks and IUGR. IUGR is defined as weight for GA < 5th centile. The last group contained four preterm pregnancies 37 but with IUGR (Extended Table 2). RNA isolation and RT-qPCR Total RNA from placental biopsies RNA using TRI reagent solution (ThermoFisher Scientific, AM9738), genomic DNA was eliminated by treating RNA samples with Turbo RNAse free DNAse1 (ThermoFisher Scientific AM1907). For reverse transcriptase-polymerase chain reaction (RT-qPCR), cDNAs were prepared with LunaScript ® RT SuperMix Kit (NEB, E3010). qPCR was performed using qPCRBIO SyGreen Mix Lo-ROX (PCRBio) in LightCycler 480 instrument (Roche). Primer pairs for human L1 5’ UTR and L1 ORF1 to human L1s were designed to amplify elements of the human-specific L1HS preferentially and evolutionarily recent primate-specific L1PA (L1PA2–L1PA6) subfamilies were taken from 33 . Primers to the human IFNA family against a consensus sequence of all human IFNA gene sequences ( IFNA1 , IFNA2, IFNA4, IFNA5 , IFNA6 , IFNA7 , IFNA8 , IFNA10 , IFNA13 , IFNA14 , IFNA16 , IFNA17 and IFNA21 ) and IFNB1 are taken from 33 . The list of all primers used for RTqPCR is in Extended Data Table 2. Data were normalised to β-actin or PSIP1. RNA sequencing We used separate parts of the biopsies for RNAseq and CUT&Tag, except for two PE+IUGR samples and two control samples, which had one replicate each for H4K16ac CUT&Tag, and one control sample that had one replicate for H3K27ac RNAseq due to experimental failure or sample limitations (Fig. 1a). RNA was isolated from placental biopsies using TRI reagent solution (ThermoFisher Scientific, AM9738), and genomic DNA was eliminated by treating RNA samples with Turbo RNAse free DNAse1 (ThermoFisher Scientific AM1907). RNA sequencing library preparation using NEBNext ® Ultra ™ II Directional RNA Library Prep Kit for Illumina ® (NEB #E7765), followed by libraries, were sequenced as 150 bp paired-end reads using Novaseq 6000. LINE1 DNA methylation assay Genomic DNA from placental tissues was isolated using a Quick-DNA mini prep plus kit according to the manufacturer’s instructions (Zymo Research D4068). LINE-1 methylation levels were quantified using an ELISA-based Global DNA Methylation Assay LINE-1 kit; the assay was performed as described by the manufacturer (Active Motif cat. no. 55017). Briefly, genomic DNA from each sample was digested overnight with MseI enzyme (10 U/μL) at 37 °C. 100 ng of digested gDNA was hybridised with a LINE-1 probe in a thermal cycler (98 °C for 10 min, 68 °C for 1 hr, followed by a quick ramp to 25 °C). LINE-1 probe is a 5’ biotinylated oligo designed to hybridise to a 290 bp region of the LINE-1 repeat element, containing 88 cytosine residues, of which 12 are in a CpG context. Reactions were performed in triplicate along with the methylated and non-methylated DNA standard samples, prepared in parallel with placental genomic DNA samples. Digested DNA was transferred to a streptavidin-coated plate and incubated for 1 h at room temperature with mild agitation. Then, a 1:100 dilution of 5-methylcytosine monoclonal antibody was incubated for 1 hr at room temperature, followed by 1 hr of HRP-conjugated secondary antibody. The developing solution was added and incubated for 3 min; the stop solution was added when the standard samples showed colour change. Finally, the plate was read at 450 nm and 655 nm. CUT&Tag CUT&Tag from placental biopsies was performed according to the Steve Henikoff lab protocol 77 , with modifications to tissue processing as described below. Different parts of placental biopsies were processed to perform replicates of H3K27ac and H4K16ac CUT&Tag. To adapt CUT&Tag tissue sections, flash-frozen placental tissues (approximately 3–4 mm size) were manually homogenised with tight homogenisers in wash buffer (20 mM HEPES pH 7.5, 150 mM NaCl, 0.1% BSA, 0.5 mM Spermidine and cOmplete EDTA-free protease inhibitor tablet) into a homogenous suspension of intact cells. Cells were transferred to 1.5-ml low DNA binding tubes (Eppendorf), and solutions were exchanged on a magnetic stand (DynaMag-2, Thermo Fisher Scientific). Cells were pelleted by centrifugation for 3 min 600× g at room temperature and resuspended in 500 μl of ice-cold NE1 buffer (20 mM HEPES-KOH pH 7.9, 10 mM KCl, 0.5 mM spermidine, 1% Triton X-100, and 20% glycerol and cOmplete EDTA-free protease inhibitor tablet) and let it sit for 10 min on ice. Nuclei were pelleted by centrifugation for 4 min 1300× g at 4 °C and resuspended in 500 μl of wash buffer, and the wash buffer by placing the tubes on a magnet stand to clear and withdraw the liquid, then resuspended in 1.0 ml wash buffer and held on ice until beads are ready. In total, 10 μl of BioMag Plus Concanavalin-A-conjugated magnetic beads (Polysciences, Inc) in binding buffer (20 mM HEPES-KOH pH 7.9, 10 mM KCl, 1 mM CaCl 2 , and 1 mM MnCl 2 ) was added to each tube containing cells and rotated on an end-to-end rotator for 10 min. After a quick spin to remove liquid from the cap, tubes were placed on a magnet stand to clear and withdraw the liquid, and 800 μl of antibody buffer containing 1 μl of primary antibodies (normal rabbit IgG, Santa Cruz Cat no sc-2027, H3K27ac (Abcam, ab4729), H4K16ac (Abcam, ab109463) was added and incubated at 4 °C overnight in a nutator. Secondary antibodies (guinea pig α-rabbit antibody, Antibodies online cat. no. ABIN101961) were added 1:100 in Dig-wash buffer (5% digitonin in wash buffer) and squirt in 100 μl per sample while gently vortexing to allow the solution to dislodge the beads from the sides and incubated for 60 min on a nutator. Unbound antibodies were washed in 1 ml of Dig-wash buffer for a total of three times. In total, 100 μl of (1:250 diluted) protein-A-Tn5 loaded with adapters in Dig-300 buffer (20 mM HEPES pH 7.5, 300 mM NaCl, 0.5 mM spermidine with Roche cOmplete EDTA-free protease inhibitor) was placed on a nutator for 1 hr and washed three times in 1 ml of Dig-300 buffer to remove unbound pA-Tn5. Then, 300 μl tagmentation buffer (Dig-300 buffer + 5 mM MgCl 2 ) was added while gently vortexing and incubated at 37 °C for 1 hr on an incubator. Tagmentation was stopped by adding 10 μl 0.5 M EDTA, 3 μl 10% SDS, and 2.5 μl 20 mg/ml Proteinase K to each sample. All were mixed by full-speed vortexing for ~ 2 s and incubated for 1 h at 55 °C to digest. DNA was purified by phenol: chloroform extraction using phase lock tubes followed by ethanol precipitation. Libraries were prepared using NEBNext HiFi 2x PCR Master mix (Cat number M0541S) with a 72 °C gap filling step followed by 13 cycles of PCR with 10-s combined annealing and extension for enrichment of short DNA fragments. Pooled libraries were run on 1.5% ultrapure agarose gel, 200-700bp smear was excised, and DNA was extracted using Monarch gel extraction kit (NEB cat. No. T1020). Libraries were sequenced in Novaseq 6000 with 150bp paired-end reads at the Novogene sequencing service. Cryosectioning, Fixation, Immunofluorescence and Confocal Imaging Frozen placenta tissues were embedded in OCT Mounting media and were cut on a cryostat (Leica CM1860) in 12 µm sections. Sections were fixed at -20 in methanol for 10 minutes and washed with PBS. Fixed sections were permeabilised with PBS-triton 0.15% for 15 minutes and blocked with 3% (w/v) bovine serum albumin for 30 min at room temperature. The sections were then probed with anti-dsRNA mouse monoclonal antibody, J2 (Nordic Cat.10010200, Lot 18439, 1:50) RIG-I (Cell Signalling Technology Cat# D14GG Lot D14G6, 1:200), KRT7 (Thermofisher Cat. MA1-06316, 1:200), MDA5 (Cell Signalling Technology Cat# 5321 Lot D74E4, 1:200), Vimentin (V9, Abcam ab8069, 1:500) at 4 o C overnight. After washing with PBS, the sections were probed with Goat anti-Rabbit IgG AlexaFluor 488 (Abcam Cat. ab150077), Goat anti-Mouse IgG AlexaFluor 647 (Abcam ab150115) and DAPI at room temperature for 60 minutes. Sections were then mounted with Dako Mounting medium (Agilent Cat#S3023) and visualised with LSM880 inverted laser scanning confocal microscope (Zeiss). RNase-III treatment was performed in ShortCut RNase-III buffer following manufacturer protocol (NEB, Cat M0245S), followed by immunostaining with J2 antibody; Picogreen (Thermo Scientific Cat. P7589, 1:5000) was used to stain double-stranded DNA. Slides were imaged using a Zeiss multiphoton confocal microscope at 20x magnification. Images were processed using FIJI, and the Pearson colocalisation coefficient was calculated using FIJI using the Coloc 2 plugin with the following default settings in the plugin: Coste’s threshold regression and Coste’s randomisation threshold of 10 and PSF 3. Analysis of CUT&Tag data Mapping 150bp paired-end reads for the CUT&Tag-seq were trimmed for adapters using the Trimmomatic tool and aligned locally to the hg38 genome through Bowtie2 (version 2.4.5) with these parameters for pair-end mapping: --very-sensitive-local --no-unal --no-mixed --no-discordant --phred33 -I 10 -X 700 78 . The best alignment was retained using default bowtie2 options for multiple aligned reads. The bam files were sorted, indexed using samtools, and used to generate bigwigs for individual replicates of H3K27ac and H4K16ac. Merged bam files were obtained across control, PE and PE+IUGR using samtools merge 79 . These bam files were then sorted, followed by indexing and generating bed and bigwigs for individual histone modifications. Details of samples and replicates for CUT&Tag are listed in extended data table 4. Histone acetylation domain analysis Read counts were obtained for the H4K16ac and H3K27ac for either 10kb with a sliding window of 5kb (for H4K16ac) or 1kb with 500bp sliding window (for H3K27ac) genomic bins on hg38 genome across control, PE and PE+IUGR groups using bedtools multicov tool. Domains containing less than 50 reads sum across all samples were filtered out from analysis for H4K16ac, and non-zero counts were used for H3K27ac analysis. Differential analysis was performed on these counts by DESeq2, and results were plotted using the r-package Enhanced Volcano, showing the differentially acetylated regions (DARs) for H4K16ac and H3K27ac, respectively (regions listed in the source data file). The threshold for the DARs was set at p-adj or FDR 5kb) overlapping with the differentially acetylated regions were obtained for H4K16ac and H3K27ac, respectively, using the bedtools intersect tool. Overlapping histone modification profiles across other histone modifications’ ChIPseq datasets were generated by submitting the DARs to the Cistrome browser 80 for H4K16ac and H3K27ac, respectively. Bigwig generation and plotting Sorted bam files were subjected to bigwig generation via deepTools (version 3.5.1) 81 bamCoverage tool with --binSize 20 –normalizeUsing CPM --scaleFactor=1.0 --smoothLength 60 --extendReads 150 --centerReads options. The signal was normalised to IgG or PE+IUGR vs Control through bigwigCompare. The bigwig files were used for plotting signals or visualisation in the genome browser. The genome-browser views were obtained by viewing the signal tracks in the UCSC genome browser or IGV. Normalised bigwigs (log2 PE+IUGR/control) for histone modifications and RNAseq were generated by using bigwigComapre function in deeptools. Signal plotting at various genomic landmarks and bed coordinates was done using deepTools . Matrices were generated using deepTools computeMatrix reference-point or scale-regions option. These matrices were used to plot heatmaps or average summary plots using the plotHeatmap or plotProfile function in deepTools. RNAseq data analysis The reads obtained from public placental RNAseq datasets (Fig. 1b) and our cohorts (two biological replicates per sample) were subjected to quality check using FastQC followed by mapping to the human genome, hg38, using STAR 82 by defining parameters specific for single or pair-end and default settings. The bam files generated were merged for the replicates, followed by indexing and bigwig generation using the tools described for CUT&Tag samples earlier, with normalisation using RPKM. The counts for the genes were obtained using the featurecounts tool from the SubRead package, and TE counts at the subfamily were obtained using the TEtranscript tool with default options. The count matrices (genes or TEs) were subjected to DESeq2 for the differential analysis with default options. The PCA plot was generated using plotPCA function on rlog transformed data for RNAseq data generated in this study. For differential expression analysis of TEs (at subfamily and loci-level) and Genes, we removed the outlier sample S20 (control) from downstream analysis. While doing an integrative analysis of the public and our cohorts, different PE classes (across different cohorts) were controlled by taking into the design for DESeq2, along with each dataset serving as a batch. The batch effect removal was performed for the differences in cohorts (each cohort serving as a batch) using limma::removeBatchEffect on the variance stabilised counts (VST) transformed count matrix. Differentially expressed genes (DEGs) or differentially expressed TE subfamilies were counted as those having Benjamini-Hochberg corrected FDR (padj) <0.05. The DEGs were functionally annotated using EnrichR and Metascape for combined analysis. For our cohort, the functional enrichment was performed using clusterProfiler 83 . The differential expression of DEGs and TE-subfamilies was visualised as volcano plots or heat maps using EnhancedVolcano or pheatmap and complexHeatmap packages, respectively. For the heatmap, z-scores obtained on variance stabilised (VST) counts were used to compare the control and PE cases. Independent analysis for differential expression in GSE114691 and GSE186257 datasets was done using the online tool iDEP 2.0 84 . For differential enrichment analysis at individual TE element (loci) levels, counts were obtained by using TElocal tool for control (n=5) and PE+IUGR (n=4) samples. These counts were used for the differential enrichment analyses using the DESeq2 package in R. The differential expression of these TEs was visualised as a heatmap using ComplexHeatmap package in R. Similarly, we also obtained differentially acetylated TE loci in PE+IUGR vs control for H4K16ac and H3K27ac. For LINEs, the signal was plotted as a heatmap for H4K16ac, H3K27ac and RNAseq. The RNA signal across the intergenic (for protein-coding genes) full-length L1s (>5kb) was calculated as RPKM from the read counts obtained across the control and PE+IUGR samples. This RPKM signal was then plotted as a scattered dot plot using GraphPad Prism 10. The signal was plotted as the log10 value of the RPKM on the Y-axis. Paired violin-plot comparisons were compared using the ANOVA Friedman test for multiple paired data. Cis -regulatory effect of hyper-acetylated and over-expressed TEs Further comparisons on the hyper- or hypo-acetylated (H4K16ac) TEs for their potential as enhancers were confirmed by comparing the H3K4me1 (ENCFF710ASO), H3K4me3 (ENCFF169MHR) and H3K9me3 (ENCFF541CWH) available from the ENCODE datasets for Chorion villi. Further, TEs (Alus, LTRs and full-length L1s) were filtered for H4K16ac-rich (read coverage > 150 sum total across group either Control (n=5) and PE+IUGR (n=4) respectively) or H4K16ac-poor (read coverage <10 sum total across group either Control (n=5) and PE+IUGR (n=4) respectively). These TEs were used to fetch genes at varying genomic distance bins using bedtools closest. For studying the cis-effect of the H4K16ac+ or H4K16ac– TEs in control and PE+IUGR, genes were grouped into different bins of genomic intervals of 10kb to 200kb from the TEs that intersected with the H4K16ac+ or H4K16ac– domain. We compared the RNAseq read density (as FPKM) of those genes (for control and PE+IUGR, respectively) for genomic distances as box plots with the ANOVA Kruskal-Wallis test for multiple comparisons using GraphPad Prism10. A similar comparison was also made for differentially expressed TEs (LINEs, SINEs, and LTRs). Here, we compared the log2 fold-change (PE+IUGR vs control) values as boxed-violin plots using tidyplots package in R for the genes at varying distances from up- or down-regulated TE elements. IFN Pathway Analysis For analysis of interferon-regulated genes, IFN-regulated gene lists were downloaded from the Interferome database 85 . For comparison as a heatmap or functional annotation, only genes which are significantly dysregulated (p-adj <0.05) for PE vs control (for all cohort combined analysis) comparison were used. Functional annotation was done using Metascape. Comparison of histone acetylations (H4K16ac and H3K27ac as CPM) and RNAseq (as log2 Fold change PE vs control) at differentially expressed IFN-regulated genes was done as box plots. For the GSE114691 cohort, a similar comparison of read counts was computed as boxed-violin plots for IFN-regulated genes and cytoplasmic dsRNA sensing and DNA-sensor proteins. Gene enrichment pathway analysis for interferon signaling was carried out utilising the SBGNview R package according to the vignette. Briefly, gene counts were extracted from the output of TElocal, and the Gage R package was used to determine enriched pathways. The subsequent output was filtered for the pathway of interest: Interferon alpha beta signalling (reactome:: R-HSA-909733) and the log fold changes generated by DESeq2 were imposed upon them utilising SBGNview (https://doi.org/10.18129/B9.bioc.SBGNview). Statistical tests For box-plots, and comparison of differentially expressed TE-subfamilies, the Dunn test function in the R tool rstatix with Bonferroni correction was used for multiple-group comparisons between the groups. All DESeq2 output was filtered for either FDR/ Benjamini-Hochberg adjusted p-value (padj < 0.05) or Wald test p-value (p < 0.05), as mentioned in the figure legends. P-values for RT-qPCR assays and gene-distance box plots were calculated using the ANOVA Kruskal-Wallis test or Dunn’s test with Bonferroni corrections for multiple comparisons using GraphPad Prism10 or R respiectively. Paired violin-plot comparisons were compared using the ANOVA Friedman test for multiple paired data. Data availability: The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus (GEO) and are accessible through the GEO Series accession numbers GSE261306 and GSE261276. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE261306 Reviewer access code Ydwbiewuvdonjqz https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?&acc=GSE261276 Reviewer access code ivudgookzdgnbap CUT&Tag raw data and processed data files (bigwigs) can be accessed at NCBI with an accession ID, and RNAseq raw data files can be accessed with an accession ID. All the datasets generated, and public datasets used in this study are detailed in (Source Table). CUT&Tag and RNAseq were performed using two different parts of the placental biopsies; replicates that failed QC were not used for the analysis. Code availability: All the analyses in this manuscript have been carried out using publicly available tools. No custom code was generated for this purpose. The methodology contains the details of the steps involved in the analysis. Declarations Acknowledgements: We thank patient families for participating in the preeclampsia epigenetics study and donating placental biopsies. We thank QMUL Epigenetics Centre, Sarah Teichmann and Ioannis Sarropoulos (Sanger Institute), Miguel Branco, Helen Rowe, Hemanth Tummala and Pierre Maillard groups (QMUL) for reagents and discussion. This research used the BALM facility at Blizard Institute, Apocrita HPC, supported by QMUL Research-IT. Funding: Medical Research Council UKRI/MRC grant (MR/T000783/1) (MMP, MP, FB) , Barts charity small grant (MGU0475) (MMP). Newton Mosharafa scholarship from the British Council, Egypt, and the Central Department of Missions, Egypt (AS, ASA). For Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. Competing interests: QMUL has filed a patent application related to the findings of this study. Author contributions: MMP, MP, AS and ASA acquired the funding, conceived and designed the study, and supervised the work. AS wrote the preeclampsia epigenetic study protocol and obtained ethical approval. ASA recruited the subjects and collected placental tissue samples and clinical data. MP and ASA performed the CUT&Tag, RNAseq experiments. FB and AD performed RTqPCR with contributions from MMP. MP analysed CUT&Tag, ChIPseq, and RNAseq data with contributions from CI. RK and AD performed cryosectioning, immunofluorescence and imaging. MMP and MP wrote the manuscript. All the authors have read and approved the final version of the manuscript. References Say L et al (2014) Global causes of maternal death: A WHO systematic analysis. Lancet Glob Health 2 Goldenberg RL, Culhane JF, Iams JD, Romero R (2008) Epidemiology and causes of preterm birth. 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Taiwanese Journal of Obstetrics and Gynecology vol. 48 28–37 Preprint at https://doi.org/10.1016/S1028-4559(09)60032-2 Yockey LJ, Iwasaki A (2018) Interferons and Proinflammatory Cytokines in Pregnancy and Fetal Development. Immunity vol. 49 397–412 Preprint at https://doi.org/10.1016/j.immuni.2018.07.017 Horton I, Kelly CJ, Dziulko A, Simpson DM, Chuong EB (2023) Mouse B2 SINE elements function as IFN-inducible enhancers. Elife 12 Buchrieser J et al (2019) IFITM Proteins Inhibit Placental Syncytiotrophoblast Formation and Promote Fetal Demise. https://www.science.org Dahlstrøm B, Romundstad P, Øian P, Vatten LJ, Eskild A (2008) Placenta weight in pre-eclampsia. Acta Obstet Gynecol Scand 87:608–611 Chatterjee P, Weaver LE, Chiasson VL, Young KJ, Mitchell BM (2011) Do double-stranded RNA receptors play a role in preeclampsia? Placenta 32:201–205 Yu H et al (2022) Dynamic reprogramming of H3K9me3 at hominoid-specific retrotransposons during human preimplantation development. Cell Stem Cell 29:1031–1050e12 Shi L, Tu BP (2015) Acetyl-CoA and the regulation of metabolism: Mechanisms and consequences. Current Opinion in Cell Biology vol. 33 125–131 Preprint at https://doi.org/10.1016/j.ceb.2015.02.003 Murphy PJ, Berger F (2023) The chromatin source-sink hypothesis: A shared mode of chromatin-mediated regulations. Dev (Cambridge) 150 Almeida MV, Vernaz G, Putman ALK, Miska EA (2022) Taming transposable elements in vertebrates: from epigenetic silencing to domestication. Trends in Genetics vol. 38 529–553 Preprint at https://doi.org/10.1016/j.tig.2022.02.009 Ahmad S et al (2018) Breaching Self-Tolerance to Alu Duplex RNA Underlies MDA5-Mediated Inflammation. Cell 172:797–810e13 Chiappinelli KB et al (2015) Inhibiting DNA Methylation Causes an Interferon Response in Cancer via dsRNA Including Endogenous Retroviruses. Cell 162:974–986 Tunbak H et al (2020) The HUSH complex is a gatekeeper of type I interferon through epigenetic regulation of LINE-1s. Nat Commun 11 Freeman DJ et al (2004) Short- and long-term changes in plasma inflammatory markers associated with preeclampsia. Hypertension 44:708–714 Tranquilli AL et al (2014) The classification, diagnosis and management of the hypertensive disorders of pregnancy: A revised statement from the ISSHP. Pregnancy Hypertension vol. 4 97–104 Preprint at https://doi.org/10.1016/j.preghy.2014.02.001 Kaya-Okur H, Henikoff S (2019) Bench top CUT&Tag V.2 Nature Communications Human Cell Atlas Method Development Community. 10.17504/protocols.io.z6hf9b6 Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory- efficient alignment of short DNA sequences to the human genome. Genome Biol 10 Danecek P et al (2021) Twelve years of SAMtools and BCFtools. Gigascience 10 Liu T et al (2011) Cistrome: An integrative platform for transcriptional regulation studies. Genome Biol 12 Ramírez F, Dündar F, Diehl S, Grüning BA, Manke T, DeepTools (2014) A flexible platform for exploring deep-sequencing data. Nucleic Acids Res 42 Dobin A et al (2013) Ultrafast universal RNA-seq aligner. Bioinf 29 STAR:15–21 Yu G, Wang LG, Han Y, He QY (2012) ClusterProfiler: An R package for comparing biological themes among gene clusters. OMICS 16:284–287 Ge SX, Son EW, Yao R, iDEP (2018) An integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics 19 Rusinova I et al (2013) INTERFEROME v2.0: An updated database of annotated interferon-regulated genes. Nucleic Acids Res 41 Additional Declarations Yes there is potential Competing Interest. QMUL has filed as UK Patent Application related to findings of this manuscript. Patent Application no. 2409378.3 Supplementary Files PateletalSupplNCB.docx Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6121510","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":424039744,"identity":"779935ae-5c37-4445-9d79-d49dcbab4da7","order_by":0,"name":"Madapura Pradeepa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYPACZgj1AYj5IMwE4rQwzgASbCRpYeYhRos5A3cC040aa3lzidyHn23KtiW2MTA//MDYloZTi2UD7wbmnGPphjtnpBtL55y7DdTCZizB2JaDU4vBAZAWtsOMG26kMUjntoG0MJgxMLZVENDy77A9UAvzb0uwFvZvhLXkth1OBGphk2YEa+EB2YLbYZbNvBsO5/alJ+/secZm2XPutnEbM0+xRMI53N43Z+/d+Djnm7XtdvY05hs/ym7L9rO3b/zwoSwZt8OAMXIAzABzQZECiqMEnBpgKlG0jIJRMApGwShAAwAG/k6nY5/r4AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9095-9247","institution":"Blizard Institute","correspondingAuthor":true,"prefix":"","firstName":"Madapura","middleName":"","lastName":"Pradeepa","suffix":""},{"id":424039745,"identity":"79471d5e-82b8-418e-a195-387ab1cbd34f","order_by":1,"name":"Manthan Patel","email":"","orcid":"","institution":"Queen Mary University of London","correspondingAuthor":false,"prefix":"","firstName":"Manthan","middleName":"","lastName":"Patel","suffix":""},{"id":424039746,"identity":"bc984820-14dc-4981-bbda-69bed4920fd1","order_by":2,"name":"Ahmed Ali","email":"","orcid":"","institution":"Blizard Institute","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Ali","suffix":""},{"id":424039747,"identity":"3c316efa-73ee-4e1c-9fc8-28c1460a554d","order_by":3,"name":"Adrianna Dabrowska","email":"","orcid":"","institution":"Blizard Institute","correspondingAuthor":false,"prefix":"","firstName":"Adrianna","middleName":"","lastName":"Dabrowska","suffix":""},{"id":424039748,"identity":"1e5043bb-e4dd-4959-af08-88127567aacd","order_by":4,"name":"Fanny Boulet","email":"","orcid":"","institution":"Blizard Institute","correspondingAuthor":false,"prefix":"","firstName":"Fanny","middleName":"","lastName":"Boulet","suffix":""},{"id":424039749,"identity":"da2b4610-e55c-43ad-a991-08c2249a24ec","order_by":5,"name":"Rashmi Kulkarni","email":"","orcid":"","institution":"Blizard Institute","correspondingAuthor":false,"prefix":"","firstName":"Rashmi","middleName":"","lastName":"Kulkarni","suffix":""},{"id":424039750,"identity":"74dea342-9454-4fd4-8618-8d4a5e25f6e1","order_by":6,"name":"Charlie Ince","email":"","orcid":"","institution":"Blizard Institute","correspondingAuthor":false,"prefix":"","firstName":"Charlie","middleName":"","lastName":"Ince","suffix":""},{"id":424039751,"identity":"7c6a4ecf-c1db-4eb0-a231-9664bbd41eff","order_by":7,"name":"Ajay Sinha","email":"","orcid":"","institution":"Blizard Institute","correspondingAuthor":false,"prefix":"","firstName":"Ajay","middleName":"","lastName":"Sinha","suffix":""}],"badges":[],"createdAt":"2025-02-27 13:41:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6121510/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6121510/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79259933,"identity":"d58be42b-d1a8-446e-a222-86b516dd61ef","added_by":"auto","created_at":"2025-03-26 09:22:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3073950,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGenes and pathways dysregulated in Preeclampsia\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ea) The study design for the cohort used in this study b) Placental biopsy cohorts analysed in this study with details of Preeclampsia classifications, number of samples, and gestational age (GA) of the biopsies. (Demographic details of public cohorts used are listed in source data 2) c) Volcano plot showing significant (padj or FDR \u0026lt;0.05, log2FoldChange ±1) differentially expressed genes in PE (all forms of Preeclampsia from multiple cohorts (from (b)) collapsed as PE, vs healthy control placentas (list of DEGs in source data file 1). Genes known to be upregulated in the preeclamptic placenta are labelled. d) Genome-browser (hg38) snapshots showing average RNAseq reads across preeclampsia-associated LEP and FSTL3 genes in control (n=6), PE (n=5) and PE+IUGR (n=4) samples for two biological replicates. e) Heatmap for log2 ratio (PE+IUGR/control) of H4K16ac (left), H3K27ac (middle), and RNAseq (right) signal across protein-coding genes with k-means clustering (3 clusters). f) Functional annotation of genes upregulated and hyper-acetylated for H3K27ac in PE+IUGR (from cluster 1 in (e)), MsigDB hallmark sets on left and DisGenet on right panel (annotated using ShinyGO 0.81). g) Genome-browser snapshot showing H3K27ac, H4K16ac and RNAseq density at CGB genes for PE+IUGR (red) and control (black). Public placental RNAseq datasets used are listed in source data 1. DEGs for PE versus control are mentioned in source data 3.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6121510/v1/1d7a6b9ce9c89b93d00ed4a6.png"},{"id":79261019,"identity":"2b630d3f-c588-48de-9018-4fd778e766f0","added_by":"auto","created_at":"2025-03-26 09:30:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1985688,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eH4K16ac level is increased at retrotransposons in Preeclampsia. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003ea) Volcano plot (left) showing significant (FDR \u0026lt;0.05, red) differential H4K16ac (10kb) marked genomic domains in PE+IUGR compared to healthy controls. Bar plots (right) for the number of protein-coding genes (genes) along with full-length L1 (\u0026gt;5kb), Alu and LTR elements overlapping differentially acetylated H4K16ac domains in PE+IUGR (b) Volcano plot (left) showing significant (FDR \u0026lt;0.05, red) differential H3K27ac (1kb) marked genomic domains in PE+IUGR compared to healthy controls. Bar plots (right) for the number of protein-coding genes (genes) along with full-length L1 (\u0026gt;5kb), Alu and LTR elements overlapping differentially acetylated H3K27ac domains in PE+IUGR. c) Heatmaps for IgG normalised H4K16ac signal for control and PE+IUGR across LTRs, Alu\u003c/em\u003es\u003cem\u003e, and full-length L1s overlapping hyper (top) and hypo (bottom) acetylated H4K16 domains in PE+IUGR compared to control. d) Genome browser snapshot showing increased H4K16ac levels in PE+IUGR at a representative TE-cluster compared to H3K27ac. The list of CUT\u0026amp;Tag replicates is mentioned in extended data table 4. Differentially acetylated regions for H3K27ac and H4K16ac are listed in source data 6 and 7, respectively.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6121510/v1/0da2702ad2f858ccd07fc774.png"},{"id":79259936,"identity":"91a14eae-4cd7-4814-a62c-6ce59f1c0088","added_by":"auto","created_at":"2025-03-26 09:22:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2888092,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eH4K16ac-marked TEs contribute to enhancer activity \u003c/strong\u003e\u003cem\u003ea) Metaplot showing a comparison of H3K4me1 (green), H3K4me3 (blue) and H3K9me3 (red) from chorionic villi at H4K16ac+ in control (left plot) and in PE+IUGR (right plot) full-length L1s (left panel), LTRs (center panel) and Alus (right panel). b) Box plots comparing the gene expression (log 10 FPKM, y-axis) for the genes proximal (\u0026lt;50kb) and distal (\u0026gt;50kb) to the H4K16ac+ L1 in control (left) and PE+IUGR (right). Box plots comparing the gene expression (log 10 FPKM, y-axis) for the genes proximal (\u0026lt;50kb) and distal (\u0026gt;50kb) to the H4K16ac+ L1 (left panel) in control (left) and PE+IUGR (right). Gene expression (log 10 FPKM, y-axis) comparison for the genes within 10kb, 10-50kb, 50-100kb and 100-200kb genomic distance bins from the H4K16ac+ LTRs and H4K16ac+ Alus for control (left) or PE+IUGR (right) of each panel. c) Genome-browser view showing H4K16ac, H3K27ac and RNAseq tracks at TEs and IFIT genes d) RNAseq profile (log2 FC PE+IUGR/control) at differentially expressed LINEs (left), LTRs (center) and SINEs (right) in PE+IUGR vs control (p-value \u0026lt; 0.05). e) like (b) but for genes located at varying distances from up-regulated (left) and down-regulated (right) TEs. \u0026nbsp;p-values for box-plots were calculated using ANOVA and the Kruskal-Wallis test for multiple comparisons.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6121510/v1/94933f819dd59e86f80fdfb8.png"},{"id":79259930,"identity":"1b80d96b-1875-4a5f-8928-0f4de96a5c3d","added_by":"auto","created_at":"2025-03-26 09:22:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2103579,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMultiple TE subfamilies are deregulated in preeclamptic placentas: \u003c/strong\u003e\u003c/em\u003e\u003cem\u003ea) Heatmap of the z-scores on VST normalised counts of TE-subfamilies that are differentially expressed in preeclamptic placentas (Group) from five independent cohorts (Study) (Fig. 1b) with different PE conditions (Classes) as annotated in each cohort (FDR padj\u0026lt;0.05, differentially expressed TEs listed in the additional data file 2). b) MA plot for differentially expressed TEs in PE+IUGR vs controls for gestational age-matched controls from GSE114691. TEs consistently deregulated from panel 3a are labelled. c) RTqPCR quantification of L1 5'-UTR and L1 ORF1 transcripts (normalised to PSIP1) in controls, pregnancy-induced hypertension (PIH) and PE (PE and PE+IUGR) placentas. d) Scattered dot plots comparing the expression of full-length intergenic L1s (as RPKM on the y-axis) across control, PE and PE+IUGR samples (p values indicate the significance of non-parametric Friedman test for multiple paired comparisons). e) Heatmap showing log2-ratio normalized PE+IUGR/control signal for H4K16ac, H3K27ac and RNAseq at H4K16ac+ LINEs (n=608). \u0026nbsp;f) ELISA based L1 5-methyl Cytosine (5-mC, DNA-methylation) assay showing the percentage of L1 5'-UTR methylation between control, PE and PE+IUGR samples. (p values for DNA methylation and RT-qPCR assays were calculated using ANOVA and the Kruskal-Wallis test for multiple comparisons.) g) RTqPCR quantification of HERVH transcripts (normalised to PSIP1) in controls, pregnancy-induced hypertension (PIH) and PE (PE and PE+IUGR) placentas. h) Heatmap of RNAseq reads (log2 PE+IUGR/controls, average of two independent replicates for each sample in control (n=6) and PE+IUGR (n=4) for randomly chosen representative candidate TE-subfamilies including three (highlighted in green in (a) from across individual TE loci. Differentially expressed TEs for PE vs control are mentioned in source data 4 (for all cohorts integrative analysis) and for PE+IUGR vs control 5 (for data generated in this study).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6121510/v1/b8c17abfb9b34f7d751483d0.png"},{"id":79259931,"identity":"de6885d9-d728-4788-8edb-e3416da3d178","added_by":"auto","created_at":"2025-03-26 09:22:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1085861,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eType 1 IFN pathway is upregulated PE placenta.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ea) Heatmap comparing the z-scores on Variance Stabilizing Transformation (VST) normalised counts for IFN genes differentially expressed in preeclamptic placentas (Group) from five independent cohorts (Study) (Fig. 1b) with different preeclampsia conditions (Classes) as annotated in each cohort (FDR padj\u0026lt;0.05). b) Gene ontology network analysis (using metascape) of upregulated (left) and downregulated (right) IFN-related genes (padj\u0026lt;0.05). \u0026nbsp;c) Box plots showing normalised read counts of RNAseq data for cytoplasmic dsRNA sensing RIG-I and IFIH1 (MDA5), cytoplasmic DNA sensing c-GAS and STING1 (top) and upregulated IFN-I responsive genes in control, PE and PE+IUGR samples from (panel 4a) (bottom). Data is from a reanalysis of published gestational age-matched placental biopsies (GSE114691) (p values represent Dunn's test for multiple comparisons). More genes are plotted in Extended data Fig. 5. e) Genome-browser track showing average RNAseq reads from control (n=6), PE (n=5), and PE+IUGR (n=4), each having two biological replicates at IFN-1 (IFNA1, IFNA13, IFNE, ISG15) and IFN-II (IFNGR1) genes. g) RT-qPCR in PE+IUGR and control samples for IFNß, IFIT1, and IFNA primers (Extended Data Table 2) that amplify multiple IFNA cluster genes. p values for RT-qPCR assays were calculated using ANOVA and the Kruskal-Wallis test for multiple comparisons.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6121510/v1/b5def3fdda3763e8ed1a2738.png"},{"id":79259935,"identity":"28ab2af2-18e3-4316-90a4-e45322b29c23","added_by":"auto","created_at":"2025-03-26 09:22:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5309644,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eColocalisation of PRRs with dsRNA in preeclamptic placenta\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ea) Representative immunofluorescence staining of healthy control and preeclamptic term placental sections with anti-dsRNA (J2), in combination with RIG-I and MDA5 antibodies. \u0026nbsp;More images from independent control and preeclamptic placentas for J2 with RIG-I and MDA5 antibodies are in Extended Data Fig 6b. The Pearson’s colocalisation coefficient (r) values between J2 and RIG-I in control and preeclampsia samples are below. Similarly, immunofluorescence was performed using RIG-I antibodies in combination with Keratin 7 (KRT7, marker of cytotrophoblast cells) and Vimentin (VIM, marker of mesenchymal cells) antibodies. No primary antibodies were used as a negative control, and nuclei were counterstained with DAPI. b) Mock and RNase III (which degrades dsRNAs) treated preeclamptic placental sections were used to validate the specificity of the J2 antibody to dsRNA. Pico green was used to stain double-stranded DNA (20 x magnification, Scale bars: 20µM).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6121510/v1/3837c80356782d3c48d9910f.png"},{"id":79261918,"identity":"eba4ba4c-1e69-4207-89b7-6bedae9faccd","added_by":"auto","created_at":"2025-03-26 09:38:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15878831,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6121510/v1/2ef161f8-bafc-4b72-9fa5-65c975b09680.pdf"},{"id":79259937,"identity":"6ff5077e-a2fe-499e-bf9d-535699f7a951","added_by":"auto","created_at":"2025-03-26 09:22:32","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17094650,"visible":true,"origin":"","legend":"","description":"","filename":"PateletalSupplNCB.docx","url":"https://assets-eu.researchsquare.com/files/rs-6121510/v1/42fc78771462e6e7c590cb19.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nQMUL has filed as UK Patent Application related to findings of this manuscript. Patent Application no. 2409378.3","formattedTitle":"De-repression of Transposable Elements by Histone Hyperacetylation Leads to Sterile Inflammation in Preeclampsia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe human placenta governs pregnancy outcomes and plays a crucial role in the health of both the mother and the offspring. Placental villi contain mainly proliferating cytotrophoblast cells (CTBs), differentiated syncytiotrophoblasts (STBs) in the outer layer, and invasive extravillous trophoblasts (EVTs), along with mesenchymal cells. Altered proliferation, differentiation, and reduced invasion of trophoblast cells into the maternal decidua cause abnormal placentation, which results in preeclampsia (PE) and other complications. Preeclampsia is also associated with inflammation, senescence, and ageing phenotypes in the placenta. It accounts for 14% of maternal deaths annually and is a major cause of premature births \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe trophoblast epigenome is dramatically reprogrammed during pregnancy, associated with an increased global DNA methylation and heterochromatin (H3K9me3 levels) with gestational age. In contrast, in H3K27ac, histone modification associated with active transcriptional enhancers and gene promoters reduces with gestational age \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Although the mechanisms of pathophysiology are unclear, epigenetic changes in trophoblast cells in the placenta are associated with Preeclampsia \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Transposable elements (TEs) constitute nearly half of the human genome and have significantly contributed to rewiring the gene-regulatory landscape by acting as species- and tissue-specific transcriptional enhancers \u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Multiple TE classes, including LTRs, LINE1s (here on referred to as L1s) and Alu, are exapted to function as tissue-specific enhancers, including placental-specific enhancers \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Most TEs are transcriptionally repressed in somatic tissues but are detected at higher levels during early development, embryonic stem cells, neuronal lineage and placenta \u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Interestingly, ERV envelope-derived genes such as Syncytins have co-opted the fusogenic role in CTBs to form multinucleated STBs \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Despite these clear links suggesting the importance of TEs in placental development and function, the impact of TE deregulation on placentation and pregnancy complications is less clear.\u003c/p\u003e \u003cp\u003eThe loss of heterochromatin and DNA methylation associated with ageing, senescence, cancer and neurological diseases causes the de-repression of multiple TE families, including L1, ERV, LTRs and SINEs, including Alu, leading to elevated type I interferon (hereafter referred to as IFN-I) mediated innate immune pathway \u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22 CR23 CR24 CR25 CR26 CR27\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. TE-derived double-stranded RNAs (dsRNAs) possess virus-like structures recognised by nucleic acid sensing pattern recognition receptors (PRRs) such as RLRs such as RIG-I and melanoma differentiation-associated protein 5 (MDA5). Similarly, the c-GAS STING pathway detects cDNAs derived from TEs. These cytoplasmic viral dsRNA and DNA-sensing PRRs recognise TE-derived nucleic acids to activate IFN-I in the absence of infection \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e–\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Ageing, cancer and the senescence-associated loss of heterochromatin lead to the upregulation of TEs resulting in a sterile inflammation phenotype \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In the Drosophila ageing model, stimulating retrotransposon activity similarly increases mortality and accelerates a subset of ageing phenotypes \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Notably, preeclamptic placentas display high levels of senescence and an accelerated ageing phenotype, although the cause of this phenotype is unclear \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eH4K16ac is a highly abundant modification of histone H4, enriched in euchromatin, particularly at gene bodies, enhancers and transposable elements (TEs) \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Although the mechanism remains unclear, the deregulation of H4K16ac is implicated in both senescence and ageing \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Recent findings demonstrate the role of lysine acetyltransferase 8 (KAT8/MOF), the enzyme responsible for H4K16ac, and KAT3B (p300), a general transcriptional coactivator that mediates the acetylation of various histone lysines and non-histone proteins in the proliferation, differentiation, and invasion capabilities of trophoblast stem cells (TSCs) \u003csup\u003e\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e–\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Acetyl-CoA metabolism and histone acetylation levels, including H4K16ac, have also been crucial for differentiating STBs from TSCs \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. However, recent evidence demonstrates that H4K16ac does not directly impact the expression of genes\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Instead, H4K16ac regulates the transcription of L1, ERVs, and LTRs in human embryonic stem cells. TEs enriched with H4K16ac function as enhancers to regulate genes in cis \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to investigate the epigenetic and transcriptomic changes associated with preeclampsia by performing genome-wide profiling of H4K16ac and H3K27ac levels along with transcriptomic changes in preeclamptic placentas. We found that TEs are expressed at higher levels in preeclamptic placentas, and this upregulation is linked to elevated levels of H4K16ac and reduced DNA methylation. H4K16ac marked and transcribed TEs contribute to the expression of nearby genes through their transcriptional enhancer activity. Our findings show that the derepression of TEs in the placenta leads to the accumulation of dsRNA, which is detected by the cytoplasmic dsRNA sensing RLRs, leading to the activation of the IFN-I pathway. We conclude that epigenetic derepression of TEs activates IFN-I, leading to elevated sterile inflammation in the placenta, which could contribute to preeclampsia-associated inflammation, senescence and accelerated ageing-like phenotype.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eDetails of Preeclampsia samples used in this study\u003c/h2\u003e\n \u003cp\u003eWe aimed to investigate altered genome-wide histone acetylation and transcriptomic changes in preeclamptic placentas. For this purpose, we recruited pregnancies in three groups: preeclampsia with a gestational age over 37 weeks without intrauterine growth restriction (IUGR) as (PE), preeclampsia with a gestational age under 37 weeks and IUGR (PE\u0026thinsp;+\u0026thinsp;IUGR), pregnancy-induced hypertension without proteinuria (PIH) and normotensive control group (control). The second group comprises three preterm pregnancies under 37 weeks and one pregnancy over 37 weeks with IUGR (PE\u0026thinsp;+\u0026thinsp;IUGR). Birth weight and gestational age were significantly lower in the second group of preeclampsia with preterm delivery and IUGR compared to the other groups (Table 1, and Extended Data Table 2). We conducted two independent replicates of H3K27ac and H4K16ac CUT\u0026amp;Tag and RNAseq for preeclampsia and healthy control samples from different parts of the biopsies. We also validated key findings by performing RT-qPCR. To address the limitation of the small sample size and to control for the gestational age differences in the placental biopsies, we additionally analysed publicly available RNAseq data from multiple cohorts representing different severities of preeclampsia (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). All together, we analysed a total of 106 Preeclamptic and 78 control placentas, this included 111 RNAseq data from preeclampsia and gestational age-matched placental control placentas. The additional details on the public cohorts are listed in extended data table 1.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eH3K27ac but not H4K16ac levels associated with gene expression level in placenta\u003c/h3\u003e\n\u003cp\u003eWe used two independent sites of the placenta per biopsy for H3K27ac and H4K16ac CUT\u0026amp;Tag and poly-A enriched RNA sequencing (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea). Differential gene expression analysis of PE versus healthy control and PE\u0026thinsp;+\u0026thinsp;IUGR versus healthy control placentas revealed expected upregulation of preeclampsia-associated transcripts such as INHBA, ENG, FSTL3, LEPTIN, HTRA4 and FLT1 in preeclamptic placentas compared to healthy controls (Extended Data Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Having confirmed preeclampsia-associated gene expression signature in our cohort, we next included two independent RNAseq cohorts containing preeclampsia and gestational age-matched controls (GSE114691 containing 21 control, 20 PE and 20 PE\u0026thinsp;+\u0026thinsp;IUGR; and GSE186257 containing 18 control and 26 severe-PE samples), along with two additional cohorts that lacked gestational age-matched controls (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb and Extended Data Table\u0026nbsp;1). Differential expression analysis performed independently for gestational age-matched datasets or integrating all the cohorts\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e confirmed upregulation of preeclampsia-associated genes compared to control placentas (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea-d, and Extended Data Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). Gene ontology analysis of the upregulated genes (from Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec) showed an expected enrichment of preeclampsia-associated terms, including angiogenesis, hypoxia, inflammatory response and EVT cell types. Notably, metabolic pathway genes, including glycolysis, are enriched (Extended Data Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ee and \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ef) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eNext, we aimed to map genome-wide changes in histone acetylations in PE; we confirmed the overall data quality and similarity among CUT\u0026amp;Tag data from independent placental samples and replicates (Extended Data Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). H3K27ac showed enrichment at genes upregulated in PE. However, the H4K16ac level was unaltered and showed no enrichment at upregulated genes in preeclampsia (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ee, Extended data Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb and c). Meta-analysis revealed the expected increase of H3K27ac at gene promoters or transcription start sites. However, H4K16ac is increased at gene bodies and enhancer features in PE\u0026thinsp;+\u0026thinsp;IUGR compared to control (Extended Data Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). Next, we analysed H4K16ac and H3K27ac levels at the protein-coding genes. Of the three clusters obtained from k-means clustering, cluster 1 showed genes having higher H3K27ac levels and higher expression in PE\u0026thinsp;+\u0026thinsp;IUGR compared to healthy controls (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ee-g). The functional annotation of genes in cluster 1 recapitulated the terms associated with changes in PE, such as hypoxia, estrogen signalling, and glycolysis, as represented in MSigDb Hallmark gene sets, alongside diseases such as preeclampsia and hypertension (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ef).\u003c/p\u003e\n\u003cp\u003eOverall, we conclude that H3K27ac levels are associated with upregulated genes in PE, while H4K16ac levels are not associated with gene expression levels. This is consistent with recent reports suggesting that H4K16ac is dispensable for gene expression in human cell line models \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4K16ac acetylated domains are enriched at L1s, LTRs and\u003c/strong\u003e \u003cstrong\u003eAlu\u003c/strong\u003e\u003cstrong\u003es in Preeclampsia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurthermore, to evaluate the changes in acetylation levels across genomic features, we analysed the coverage of CUT\u0026amp;Tag reads within genomic bins of 1 kb using 0.5 kb sliding windows for H3K27ac. For H4K16ac, we used 10 kb bins with 5 kb sliding windows due to its broad enrichment pattern. We observed more genomic bins (domains) gained with H3K27ac (H3K27ac+) and H4K16ac (H4K16ac+) in the PE\u0026thinsp;+\u0026thinsp;IUGR placentas compared to the control samples (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea and b, Extended Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). Notably, H4K16ac\u0026thinsp;+\u0026thinsp;domains specific to PE\u0026thinsp;+\u0026thinsp;IUGR overlapped with higher number of full-length L1s (\u0026gt;\u0026thinsp;5kb), \u003cem\u003eAlu\u003c/em\u003e, and LTR elements compared to genes. Meanwhile, H3K27ac\u0026thinsp;+\u0026thinsp;domains specific to PE\u0026thinsp;+\u0026thinsp;IUGR overlapped with a higher number of genes, \u003cem\u003eAlu\u003c/em\u003e, and LTR elements but not at full-length L1s (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb-d). Individual \u003cem\u003eAlu\u003c/em\u003e, LTR, and full-length L1s that overlapped with H4K16ac\u0026thinsp;+\u0026thinsp;domains (from Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb) displayed an overall increase in H4K16ac in PE\u0026thinsp;+\u0026thinsp;IUGR compared to controls (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH4K16ac\u0026thinsp;+\u0026thinsp;TEs regulate the expression of genes in the cis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverlap-analysis of public histone modification ChIPseq datasets across different cell lines showed H4K16ac\u0026thinsp;+\u0026thinsp;domains in PE\u0026thinsp;+\u0026thinsp;IUGR overlap at enhancer and promoter marks such as H3K4me1 and H3K4me3 (Extended Data Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). We next compared H3K4me3 and H3K4me1 ChIPseq signal from the chorionic villi (ENCODE datasets) at the H4K16ac-rich TEs (Alu, L1s (\u0026gt;\u0026thinsp;5kb) and LTRs) in control and PE\u0026thinsp;+\u0026thinsp;IUGR respectively (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). These H4K16ac\u0026thinsp;+\u0026thinsp;TEs displayed higher levels of histone modifications associated with regulatory elements such as H3K4me1 and H3K4me3 and lower repressive marks, H3K9me3 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). To determine the cis-regulatory effect of these H4K16ac\u0026thinsp;+\u0026thinsp;TEs, we analysed expression level of genes at different distances from H4K16ac\u0026thinsp;+\u0026thinsp;L1s, LTRs and \u003cem\u003eAlu\u003c/em\u003es. Genes closer to H4K16ac\u0026thinsp;+\u0026thinsp;TEs in PE\u0026thinsp;+\u0026thinsp;IUGR placenta had significantly higher expression than those distantly located genes (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). In contrast, genes close to TEs (LTRs) that lacked H4K16ac in Control and PE\u0026thinsp;+\u0026thinsp;IUGR are expressed at lower levels in the Control and PE\u0026thinsp;+\u0026thinsp;IUGR placenta respectively (Extended Data Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec). Higher levels of active enhancer-associated histone modifications and low levels of repressive histone modifications at H4K16ac\u0026thinsp;+\u0026thinsp;TEs (Extended Data Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb \u0026amp; \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed), suggests their cis-regulatory role.\u003c/p\u003e\n\u003cp\u003eWe found PE\u0026thinsp;+\u0026thinsp;IUGR-specific increase in H4K16ac but not H3K27ac at TEs such as MER52A, L1M2, L1PA2, HERVL-E, \u003cem\u003eAluJb\u003c/em\u003e and \u003cem\u003eAluSx\u003c/em\u003e located near the \u003cem\u003eIFIT\u003c/em\u003e gene cluster (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec). Notably, these H4K16ac\u0026thinsp;+\u0026thinsp;TEs loop to \u003cem\u003eIFIT1, IFIT2\u003c/em\u003e and \u003cem\u003eIFIT3\u003c/em\u003e genes. This suggests that H4K16ac\u0026thinsp;+\u0026thinsp;TEs could function as enhancers to maintain the expression level of these \u003cem\u003eIFIT\u003c/em\u003e genes. Since we found a higher level of TE transcripts in the preeclamptic placenta, we asked whether transcribed TE loci could function as enhancers to regulate genes in cis. Differential expression analysis of individual TEs using the TElocal tool revealed 6118 up-regulated compared to 4168 down-regulated TEs in PE\u0026thinsp;+\u0026thinsp;IUGR compared to healthy controls (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed). Notably, more L1 and LTRs are upregulated than downregulated in PE\u0026thinsp;+\u0026thinsp;IUGR, whereas a similar number of \u003cem\u003eAlu\u003c/em\u003es are up and downregulated. Analysis of the expression profile of genes near these TEs revealed that genes closer to upregulated TEs have higher expression levels in PE\u0026thinsp;+\u0026thinsp;IUGR than genes located away from upregulated TEs (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ee). In contrast, genes closer to the downregulated TEs are expressed at lower levels in PE\u0026thinsp;+\u0026thinsp;IUGR. Notably, genes close to upregulated TEs showing higher expression in PE\u0026thinsp;+\u0026thinsp;IUGR are associated with EVTs, eclampsia and pregnancy complications (Extended Data Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ee).\u003c/p\u003e\n\u003cp\u003eOverall, our data support the model that higher levels of H4K16ac at TEs and TE transcription contribute to the expression of genes close to these TEs, contributing to an altered gene expression programme in preeclampsia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTransposable elements are derepressed in the preeclamptic placentas.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of TE expression in our cohort at the individual loci revealed 1611 upregulated and 915 downregulated L1s; similarly, 898 upregulated and 432 downregulated ERV internal elements and/or LTRs. A similar number of \u003cem\u003eAlu\u003c/em\u003e elements were up and downregulated in PE\u0026thinsp;+\u0026thinsp;IUGR (n\u0026thinsp;=\u0026thinsp;4 PE\u0026thinsp;+\u0026thinsp;IUGR) samples compared to controls (n\u0026thinsp;=\u0026thinsp;5) with two replicates per sample (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed, Extended Data Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea and b). Next, we analysed RNAseq data from five independent cohorts, including two with gestational age-matched placental biopsies. We used the TEtranscript tool\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, to analyse TE expression level at the subfamily level, which avoids bias against mapping to evolutionarily younger TEs, revealed significant (padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05) upregulation of 40 and downregulation of 15 TE subfamilies. Unsupervised clustering based on differentially expressed TE subfamilies from five independent cohorts led to a clear separation of preeclampsia from healthy control samples, irrespective of the disease severity and PE classification (PE, severe PE, PE with IUGR and superimposed PE) and timing of the onset of PE (early or late) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). Moreover, a separate analysis of gestational age-matched samples (GSE114691 and GSE186257) and our cohort also showed similar pattern of TE expression changes in preeclampsia (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb and Extended Data Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec-e). This analysis confirms that preeclampsia-specific differences in TE levels are not due to differences in the gestational age of the placenta.\u003c/p\u003e\n\u003cp\u003eWe next validated preeclampsia-specific upregulation of L1s and HERVs by performing RT-qPCR using primers that detect multiple HERVH loci and the 5\u0026rsquo; UTRs of evolutionarily young L1 elements (L1HS\u0026ndash;L1PA5) as well as L1 ORF1 (Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eg). TEs were not significantly upregulated in pregnancy-induced hypertension without proteinuria (PIH, gestational hypertension), suggesting that the upregulation of TEs is specific to preeclampsia (Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eg). Differentially expressed L1s were not due to pervasive transcription, as intergenic (for protein-coding genes) full-length L1s (\u0026gt;\u0026thinsp;5kb) showed significantly higher expression in PE\u0026thinsp;+\u0026thinsp;IUGR compared to healthy controls (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ed).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eL1s exhibit higher H4K16ac and reduced DNA methylation levels in preeclamptic placentas.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDNA methylation plays a vital role in transcriptional repression of TEs in somatic tissues. Reduced heterochromatin and DNA methylation contribute to ageing and senescence-associated increases in TE transcription \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The reactivation of HERVs and L1s associated with reduced DNA methylation increases with age \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Thus, we asked whether higher H4K16ac levels at L1s (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ee) are associated with low DNA methylation levels. Using an ELISA-based global methylation assay, we measured 5-methyl Cytosine (5-mC) across L1 repeats in placental biopsies. PE and PE\u0026thinsp;+\u0026thinsp;IUGR placentas showed significant hypomethylation at L1s in preeclampsia compared to the control placenta (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ef). L1s constitute\u0026thinsp;~\u0026thinsp;18% of the human genome, suggesting a global hypomethylation in PE, consistent with the previous findings demonstrating hypomethylation signatures associated with preeclampsia \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. We conclude that low DNA hypomethylation and higher H4K16ac contribute to the derepression of TEs in the preeclamptic placenta.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eElevated type-I interferon pathway in Preeclamptic placenta.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePRRs, including RIG-I receptor-like receptors (RLRs), MDA5 recognise TE-derived dsRNA, whilst cyclic GMP-AMP synthase (c-GAS) recognise reverse transcribed TE cDNA as invading viruses, triggering the antiviral IFN-I innate immune pathway \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. We next asked whether the derepressed TE transcripts could activate the IFN-I pathway. Differential expression analysis of IFN-pathway genes (n\u0026thinsp;=\u0026thinsp;250) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e revealed upregulation of 22 and downregulation of 9 IFN-associated genes in PE (all PE combined for cohorts) placenta compared to controls (padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05, log2FC\u0026thinsp;\u0026gt;\u0026thinsp;0.5). Upregulated genes in PE samples included IFNA1, ISG15, MX1, MX2, and STAT2 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea), which are IFN-I responsive genes, which are also associated with senescence phenotype. Pathway enrichment analysis of all upregulated IFN genes showed enrichment for the IFN-I pathway. In contrast, downregulated genes did not show enrichment for the IFN-I pathway (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb). Upregulated IFN genes did not show significant changes in H4K16ac and H3K27ac levels (Extended Data Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea) between control and PE (PE and PE\u0026thinsp;+\u0026thinsp;IUGR) samples. This suggests that IFN-I upregulation is not due to a general increase in histone acetylation levels at these genes. Quantifying the expression level of IFN genes in another independent gestational age-matched cohort confirmed significant upregulation of IFN-I genes in PE and PE\u0026thinsp;+\u0026thinsp;IUGR samples compared to controls (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed and Extended data Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). RT-qPCR validation in our placental biopsies further confirmed the upregulation of IFN-I genes in preeclamptic placentas (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eg). Although there were no overall changes in histone acetylations at upregulated IFN-I genes. Interestingly, however, TEs located close to and loops with upregulated IFIT genes (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb) gain H4K16ac but not H3K27ac level (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec). This suggests that H4K16ac\u0026thinsp;+\u0026thinsp;TEs could maintain the expression level of some of the IFN-I pathway genes through their enhancer activity. Overall, we conclude that higher levels of L1s, ERV internal regions, LTRs and \u003cem\u003eAlu\u003c/em\u003es in the preeclamptic placenta correlate with elevated levels of the antiviral IFN-I pathway in preeclampsia.\u003c/p\u003e\n\u003ch3\u003eCytoplasmic dsRNA sensing by RIG-I in syncytiotrophoblasts in PE placenta\u003c/h3\u003e\n\u003cp\u003eQuantification of the expression level of PRRs in the RNAseq datasets showed preeclampsia-specific upregulation of cytoplasmic dsRNA sensors, RIG-I and MDA5 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed). In contrast, c-GAS and STING, a downstream sensor of c-GAS, showed downregulation in the PE and PE\u0026thinsp;+\u0026thinsp;IUGR placentas. We aimed to investigate whether a higher level of TE transcripts in PE placentas leads to the accumulation of dsRNA and whether RLRs recognise these transcripts. We performed a series of immunostaining using antibodies against RLRs (RIG-I and MDA5), along with antibodies that recognise dsRNA (J2) (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea and Extended Data Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb). J2 antibodies are widely used to detect TE-derived endogenous dsRNAs that accumulate upon ageing- or senescence-associated de-repression of HERV, LTRs and \u003cem\u003eAlu\u003c/em\u003es. Notably, endogenous dsRNAs are generally below the detection limit of J2 antibody in the absence of viral infection or epigenetic drug-mediated de-repression of TEs. Confocal immunofluorescence imaging of preeclamptic placental biopsy cryosections showed enriched dsRNA (J2) signal in the outer layer of the placental villi (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea, Extended Data Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb). Similarly, RIG-I and MDA5 levels were detected at a higher level in multiple independent preeclamptic sections compared to healthy controls, particularly at the outer layer of the placental villi. These results are consistent with the RNAseq data showing significant upregulation of RIG-I and MDA5 in PE placentas (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e\n\u003cp\u003eWe found clear colocalisation of RIG-I and MDA5 with J2 at the outer layer of the placental villi, suggesting these RLRs recognise accumulated cytoplasmic dsRNA in the preeclamptic placentas. Furthermore, we found RIG-I colocalisation with cytotrophoblast marker Keratin 7 (KRT7) and SDC-1, a syncytiotrophoblasts marker, but not with Vimentin, a known mesenchymal marker in the placenta, suggesting that dsRNA accumulation and RIG-I sensing occurs at the outer syncytial layer of the placental villi. Moreover, RNAse-III treatment reduced the dsRNA signal, demonstrating the specificity of the J2 antibodies to endogenous dsRNAs (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb). Altogether, these immunofluorescence data supports our conclusion that TE-derived endogenous dsRNAs are recognised by RLRs, leading to elevated IFN-I signalling in preeclampsia.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMost pregnancy complications, such as preeclampsia, IUGR, premature birth, and stillbirth, are linked to elevated levels of inflammation; the exact causes of inflammation in the placenta remain unclear. We demonstrate that TE upregulation linked to H4K16ac hyperacetylation activates the antiviral IFN-I pathway by recognising TE-derived nucleic acids by viral nucleic acid sensor proteins. Particularly, dsRNA sensors PRRs such as RIG-I and MDA5 are upregulated, while cytoplasmic DNA sensors c-GAS and STING are downregulated. Furthermore, we found a higher signal and colocalisation of dsRNA and RIG-I signal in trophoblasts in preeclamptic placenta, demonstrating activation of specific IFN-I pathways in preeclampsia.\u003c/p\u003e \u003cp\u003eHaemochorial placentas in humans increase the risk of vertical viral transmission, thereby exerting selection pressure to develop robust antiviral mechanisms for foetal protection. Recent evidence indicates that TEs are implicated in elevated levels of sterile inflammation in placental villi due to the TE-derived IFN pathway \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, which is beneficial and confers antiviral protection to the foetus during pregnancy \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. However, TE-derived nucleic acids are associated with sterile inflammation in ageing, cancer, senescence and neurological disorders. Elevated inflammation is also associated with senescence and accelerated ageing in the preeclamptic placenta \u003csup\u003e\u003cspan additionalcitationids=\"CR58 CR59\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. These findings suggest that although a low level of TE expression could benefit pregnancy through stimulation of antiviral pathways \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e, deregulation of TEs could also cause elevated sterile inflammation associated with premature ageing and senescence in the placenta. Elevated IFN-I could also contribute to syncytial knot-mediated sprouting and apoptosis, a common phenotype in preeclamptic placenta \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIFIT cluster genes (\u003cem\u003eIFIT1, IFIT2, IFIT3\u003c/em\u003e and \u003cem\u003eIFIT5\u003c/em\u003e) are generally not expressed in most cell types; they are the early responders to IFN-I activation upon viral infection and are involved in dsRNA signalling. Consistent RNAseq data showing upregulation of RIG-I, MDA5, and many IFN-I genes downstream to PRRs, including IFITs, are also upregulated in the preeclamptic placenta. Immunofluorescence data shows a higher level of dsRNA and RIG-I in the outer layer of the placental villi, which comprises CTBs and STBs. Upregulation of dsRNA sensing PRRs and clear colocalisation of dsRNA with RIG-I supports the model of TE-derived nucleic acids triggering the IFN-I pathway. We did not see a general increase in histone acetylation level at the upregulated IFN-I genes (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), suggesting that higher IFN-I upregulation is not due to global increase in histone hyperacetylation mediated derepression. However, enhancers near the \u003cem\u003eIFIT\u003c/em\u003e gene cluster harbour many TE elements that gain H4K16ac in PE, suggesting H4K16ac\u0026thinsp;+\u0026thinsp;TEs could contribute to maintaining higher expression levels of \u003cem\u003eIFIT\u003c/em\u003e genes. This is similar to the finding that shows SINE elements acting as enhancers to regulate IFN in mouse model \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. A higher level of IFITM (an IFN-1-regulated gene) is shown to inhibit cell fusion in STBs, which can contribute to pregnancy complications \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, consistent with the smaller birth and placental weight in early-onset PE \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Our findings demonstrate that TE derepression leads to elevated IFN-I in PE and PE\u0026thinsp;+\u0026thinsp;IUGR, which provides a possible molecular explanation for the preeclampsia phenotype, including reduced syncytiotrophoblasts in PE. Further in vitro experimentation is needed to establish the direct impact of TEs and upregulation of IFN-I pathway, including IFITs and ISGs, on TSC proliferation, differentiation of CTBs to STB and EVTs and invasion properties of EVTs. Further understanding of the function of individual IFN-I proteins will facilitate therapeutics development and identification of potential clinical biomarkers.\u003c/p\u003e \u003cp\u003eRecent work shows an interesting link between pregnancy-specific upregulation of HERVs and elevated IFN-I response, and this TE-IFN-I axis is co-opted to activate haematopoietic stem cells and erythropoiesis \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Similarly, primate-specific \u003cem\u003eAlu\u003c/em\u003e and rodent-specific B1 SINE RNA drive type III interferon (IFN-III) expression and antiviral protection in the placenta\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Unlike IFN-III in the placenta, which can protect the foetus from viral infection, we speculate that elevated IFN-I can pose a high risk of pregnancy complications due to morphological changes in the placenta. \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTEs are known to be upregulated during early development, embryonic stem cells and trophectoderm-derived trophoblast stem cells in the placenta \u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Trophectoderm-gained repressive H3K9me3 domains are preferentially deposited at hominoid-specific TEs such as LTR12, MER11B, HERVH, and HERVK9-int that are differentially enriched in placenta \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Epigenetic remodelling of these TEs is essential for early development, placentation and embryo implantation \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Mechanisms through TEs deregulated in the preeclamptic placenta are unknown. Higher H4K16ac domains in PE, particularly at TEs, suggest that a higher level of H4K16ac contributes to TE transcription in preeclamptic placenta, possibly due to loss of heterochromatin, as we found a reduced level of DNA methylation at L1s. Altered histone acetylations, including H4K16ac, can influence trophoblast phenotype, as the altered acetylation pathway is also known to affect TSC proliferation, differentiation and invasive properties of TSCs \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. What causes altered H4K16ac levels in preeclampsia is unclear; we speculate that altered glycolysis and hypoxia pathways (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed) could contribute to altered nuclear acetyl-CoA levels in the preeclamptic placenta. Altered nuclear acetyl-CoA is demonstrated to alter H4K16ac in trophoblast stem cells; this can influence the differentiation of TSCs to STBs \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The contribution of diet-induced changes in acetyl-CoA in altered levels of histone acetylation at TEs also cannot be ruled out, as the higher levels of nucleocytoplasmic acetyl-CoA can serve as a substrate for histone acetylation in growth or fed conditions compared to starved conditions (reviewed in) \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. This agrees with the hypothesis that chromatin modifications enriched at TEs, constituting nearly 50% of the genome, can be a source or sink for metabolic by-products such as acetyl-CoA (discussed in) \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Alternatively, dsRNAs released from syncytial knots or necrotic trophoblast cells expressing TEs could also trigger IFN-I activation and an \"antiviral\" immune response in PE \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTE transcription is epigenetically repressed by DNA methylation and chromatin-modifying complexes that mediate H3K9me3, reviewed in \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Our data suggests that TE deregulation in preeclampsia is due to higher histone acetylation and reduced DNA methylation levels. We have previously demonstrated the H4K16ac role in TE transcription in stem cells \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e; this study provides novel disease-relevant context on how altered histone acetylation at TEs can contribute to placental phenotypes associated with PE.\u003c/p\u003e \u003cp\u003eLimitations of the study: Although upregulated \u003cem\u003eAlu\u003c/em\u003e, LTR, HERV and L1 can form dsRNAs\u003csup\u003e\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e, which of these TEs are the source of dsRNAs in preeclampsia is unclear. It is also unknown whether these preeclampsia-associated changes have a causal role in the PE phenotype or appear as a consequence of premature ageing and senescence phenotype in the preeclamptic placenta. In the absence of animal models of PE, further experimentation using trophoblast differentiation and organoid models will reveal the direct impact of TE deregulation on the inflammatory phenotype observed in preeclampsia.\u003c/p\u003e \u003cp\u003eIn summary, low sterile inflammation is important for antiviral protection during pregnancy. Elevated IFN-I signalling due to TE-derived nucleic acids contributes to sterile inflammation phenotype. Further experimentation using in-vitro TSC models is needed to establish the direct effect of deregulated TEs in a chronic inflammatory state associated with preeclampsia \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Further research into understanding the complex interplay between epigenetic deregulation of TEs and inflammation in preeclampsia will lead to interventions targeting epigenetic regulators and inhibiting TE activity or inflammatory pathways in inflammation and age-related diseases, including PE.\u003c/p\u003e"},{"header":"Materials and Methods ","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were enrolled at the Royal London Hospital, Barts Health Trust from May 2021 to March 2022 as a part of a PE Epigenetics study with written informed consent before participating and ethical committee approval (REC 21/SS/0010) from the UK Health Research Authority. Demographic and clinical details were obtained from the Clinical Record Service (CRS) Cerner Millennium and BadgerNet Clevermed database. Immediately after birth, we collected the samples from the fresh placenta. Four samples (1cm x 1cm) of villous tissue were accessed by trimming away the basal plate with scissors and a scalpel, then cutting out a piece of the exposed villous tissue and discarding the basal plate tissue. \u0026nbsp;Dissected villous tissue was immediately transferred to a dish containing PBS, and the tissue was cut into small pieces, then snap-frozen immediately and stored at -80\u0026deg;C. We performed two replicates of CUT\u0026amp;Tag for H3K27ac and H4K16ac and polyA-RNAseq from different parts of the placenta biopsies. Sequencing data that failed were excluded for further analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecruitment criteria\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBabies born to women with PE were defined as recommendations from the International Society for the Study of Hypertension in Pregnancy (\u003cem\u003eISSHP\u003c/em\u003e) \u003csup\u003e\u003cspan class=\"normaltextrun\"\u003e76\u003c/span\u003e\u003c/sup\u003e, PIH, and normotensive control who delivered at the Royal London Hospital and were willing to provide informed consent. Exclusion criteria: Infants who were critically ill and babies with significant congenital and genetic abnormalities. Three groups were recruited: normotensive control, PE with GA\u0026gt; 37 weeks without IUGR, and PE \u0026lt;37 weeks and IUGR. IUGR is defined as weight for GA \u0026lt; 5th centile. The last group contained four preterm pregnancies \u0026lt; 37 weeks and one pregnancy \u0026gt; 37 but with IUGR (Extended Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA isolation and RT-qPCR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA from placental biopsies RNA using TRI reagent solution (ThermoFisher Scientific, AM9738), genomic DNA was eliminated by treating RNA samples with Turbo RNAse free DNAse1 (ThermoFisher Scientific AM1907). For reverse transcriptase-polymerase chain reaction (RT-qPCR), cDNAs were prepared with LunaScript\u003csup\u003e\u0026reg;\u003c/sup\u003e RT SuperMix Kit (NEB, E3010). qPCR was performed using qPCRBIO SyGreen Mix Lo-ROX (PCRBio) in LightCycler 480 instrument (Roche). Primer pairs for human L1 5\u0026rsquo; UTR and L1 ORF1 to human L1s were designed to amplify elements of the human-specific L1HS preferentially and evolutionarily recent primate-specific L1PA (L1PA2\u0026ndash;L1PA6) subfamilies were taken from \u003csup\u003e33\u003c/sup\u003e. Primers to the human IFNA family against a consensus sequence of all human IFNA gene sequences (\u003cem\u003eIFNA1\u003c/em\u003e\u003cem\u003e, IFNA2, IFNA4, IFNA5\u003c/em\u003e, \u003cem\u003eIFNA6\u003c/em\u003e, \u003cem\u003eIFNA7\u003c/em\u003e, \u003cem\u003eIFNA8\u003c/em\u003e, \u003cem\u003eIFNA10\u003c/em\u003e, \u003cem\u003eIFNA13\u003c/em\u003e, \u003cem\u003eIFNA14\u003c/em\u003e, \u003cem\u003eIFNA16\u003c/em\u003e, \u003cem\u003eIFNA17\u003c/em\u003e and \u003cem\u003eIFNA21\u003c/em\u003e) and IFNB1 are taken from \u003csup\u003e33\u003c/sup\u003e. The list of all primers used for RTqPCR is in Extended Data Table 2. Data were normalised to \u0026beta;-actin or PSIP1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used separate parts of the biopsies for RNAseq and CUT\u0026amp;Tag, except for two PE+IUGR samples and two control samples, which had one replicate each for H4K16ac CUT\u0026amp;Tag, and one control sample that had one replicate for H3K27ac RNAseq due to experimental failure or sample limitations (Fig. 1a).\u0026nbsp;RNA was isolated from placental biopsies using TRI reagent solution (ThermoFisher Scientific, AM9738), and genomic DNA was eliminated by treating RNA samples with Turbo RNAse free DNAse1 (ThermoFisher Scientific AM1907). RNA sequencing library preparation using NEBNext\u003csup\u003e\u0026reg;\u003c/sup\u003e Ultra\u003csup\u003e\u0026trade;\u003c/sup\u003e II Directional RNA Library Prep Kit for Illumina\u003csup\u003e\u0026reg;\u0026nbsp;\u003c/sup\u003e(NEB #E7765), followed by libraries, were sequenced as 150 bp paired-end reads using Novaseq 6000. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLINE1 DNA methylation assay\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA from placental tissues was isolated using a Quick-DNA mini prep plus kit according to the manufacturer\u0026rsquo;s instructions (Zymo Research D4068). LINE-1 methylation levels were quantified using an ELISA-based Global DNA Methylation Assay LINE-1 kit; the assay was performed as described by the manufacturer (Active Motif cat. no. 55017). Briefly, genomic DNA from each sample was digested overnight with MseI enzyme (10 U/\u0026mu;L) at 37 \u0026deg;C. 100 ng of digested gDNA was hybridised with a LINE-1 probe in a thermal cycler (98 \u0026deg;C for 10 min, 68 \u0026deg;C for 1 hr, followed by a quick ramp to 25 \u0026deg;C). LINE-1 probe is a 5\u0026rsquo; biotinylated oligo designed to hybridise to a 290 bp region of the LINE-1 repeat element, containing 88 cytosine residues, of which 12 are in a CpG context. Reactions were performed in triplicate along with the methylated and non-methylated DNA standard samples, prepared in parallel with placental genomic DNA samples. Digested DNA was transferred to a streptavidin-coated plate and incubated for 1 h at room temperature with mild agitation. Then, a 1:100 dilution of 5-methylcytosine monoclonal antibody was incubated for 1 hr at room temperature, followed by 1 hr of HRP-conjugated secondary antibody. The developing solution was added and incubated for 3 min; the stop solution was added when the standard samples showed colour change. Finally, the plate was read at 450 nm and 655 nm.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCUT\u0026amp;Tag\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCUT\u0026amp;Tag from placental biopsies was performed according to the Steve Henikoff lab protocol \u003csup\u003e77\u003c/sup\u003e, with modifications to tissue processing as described below. Different parts of placental biopsies were processed to perform replicates of H3K27ac and H4K16ac CUT\u0026amp;Tag. To adapt CUT\u0026amp;Tag tissue sections, flash-frozen placental tissues (approximately 3\u0026ndash;4 mm size) were manually homogenised with tight homogenisers in wash buffer (20 mM HEPES pH 7.5, 150 mM NaCl, 0.1% BSA, 0.5 mM Spermidine and cOmplete EDTA-free protease inhibitor tablet) into a homogenous suspension of intact cells. Cells were transferred to 1.5-ml low DNA binding tubes (Eppendorf), and solutions were exchanged on a magnetic stand (DynaMag-2, Thermo Fisher Scientific). Cells were pelleted by centrifugation for 3 min 600\u0026times;\u003cem\u003eg\u003c/em\u003e at room temperature and resuspended in 500 \u0026mu;l of ice-cold NE1 buffer (20 mM HEPES-KOH pH 7.9, 10 mM KCl, 0.5 mM spermidine, 1% Triton X-100, and 20% glycerol and cOmplete EDTA-free protease inhibitor tablet) and let it sit for 10 min on ice. Nuclei were pelleted by centrifugation for 4 min 1300\u0026times;\u003cem\u003eg\u003c/em\u003e at 4 \u0026deg;C and resuspended in 500 \u0026mu;l of wash buffer, and the wash buffer by placing the tubes on a magnet stand to clear and withdraw the liquid, then resuspended in 1.0 ml wash buffer and held on ice until beads are ready. In total, 10 \u0026mu;l of BioMag Plus Concanavalin-A-conjugated magnetic beads (Polysciences, Inc) in binding buffer (20 mM HEPES-KOH pH 7.9, 10 mM KCl, 1 mM CaCl\u003csub\u003e2\u003c/sub\u003e, and 1 mM MnCl\u003csub\u003e2\u003c/sub\u003e) was added to each tube containing cells and rotated on an end-to-end rotator for 10 min. After a quick spin to remove liquid from the cap, tubes were placed on a magnet stand to clear and withdraw the liquid, and 800 \u0026mu;l of antibody buffer containing 1 \u0026mu;l of primary antibodies (normal rabbit IgG, Santa Cruz Cat no sc-2027, H3K27ac (Abcam, ab4729), H4K16ac (Abcam, ab109463) was added and incubated at 4 \u0026deg;C overnight in a nutator. Secondary antibodies (guinea pig \u0026alpha;-rabbit antibody, Antibodies online cat. no. ABIN101961) were added 1:100 in Dig-wash buffer (5% digitonin in wash buffer) and squirt in 100 \u0026mu;l per sample while gently vortexing to allow the solution to dislodge the beads from the sides and incubated for 60 min on a nutator. Unbound antibodies were washed in 1 ml of Dig-wash buffer for a total of three times. In total, 100 \u0026mu;l of (1:250 diluted) protein-A-Tn5 loaded with adapters in Dig-300 buffer (20 mM HEPES pH 7.5, 300 mM NaCl, 0.5 mM spermidine with Roche cOmplete EDTA-free protease inhibitor) was placed on a nutator for 1 hr and washed three times in 1 ml of Dig-300 buffer to remove unbound pA-Tn5. Then, 300 \u0026mu;l tagmentation buffer (Dig-300 buffer + 5 mM MgCl\u003csub\u003e2\u003c/sub\u003e) was added while gently vortexing and incubated at 37 \u0026deg;C for 1 hr on an incubator. Tagmentation was stopped by adding 10 \u0026mu;l 0.5 M EDTA, 3 \u0026mu;l 10% SDS, and 2.5 \u0026mu;l 20 mg/ml Proteinase K to each sample. All were mixed by full-speed vortexing for ~ 2 s and incubated for 1 h at 55 \u0026deg;C to digest. DNA was purified by phenol: chloroform extraction using phase lock tubes followed by ethanol precipitation. Libraries were prepared using NEBNext HiFi 2x PCR Master mix (Cat number M0541S) with a 72 \u0026deg;C gap filling step followed by 13 cycles of PCR with 10-s combined annealing and extension for enrichment of short DNA fragments. Pooled libraries were run on 1.5% ultrapure agarose gel, 200-700bp smear was excised, and DNA was extracted using Monarch gel extraction kit (NEB cat. No. T1020). Libraries were sequenced in Novaseq 6000 with 150bp paired-end reads at the Novogene sequencing service.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCryosectioning, Fixation, Immunofluorescence and Confocal Imaging\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrozen placenta tissues were embedded in OCT Mounting media and were cut on a cryostat (Leica CM1860) in 12\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026micro;m sections. Sections were fixed at -20 in methanol for 10\u0026nbsp;minutes and washed with PBS. Fixed sections were permeabilised with PBS-triton 0.15% for 15 minutes and blocked with 3% (w/v) bovine serum albumin for 30\u0026nbsp;min at room temperature. The sections were then probed with anti-dsRNA mouse monoclonal antibody, J2 (Nordic Cat.10010200, Lot 18439, 1:50) RIG-I (Cell Signalling Technology Cat# D14GG Lot D14G6, 1:200), KRT7 (Thermofisher Cat. MA1-06316, 1:200), MDA5 (Cell Signalling Technology Cat# 5321 Lot D74E4, 1:200), Vimentin (V9, Abcam ab8069, 1:500) at 4\u003csup\u003eo\u003c/sup\u003eC overnight. After washing with PBS, the sections were probed with Goat anti-Rabbit IgG AlexaFluor 488 (Abcam Cat. ab150077), Goat anti-Mouse IgG AlexaFluor 647 (Abcam ab150115) and DAPI at room temperature for 60 minutes. Sections were then mounted with Dako Mounting medium (Agilent Cat#S3023) and visualised with LSM880 inverted laser scanning confocal microscope (Zeiss). RNase-III treatment was performed in ShortCut RNase-III buffer following manufacturer protocol (NEB, Cat M0245S), followed by immunostaining with J2 antibody; Picogreen (Thermo Scientific Cat. P7589, 1:5000) was used to stain double-stranded DNA. Slides were imaged using a Zeiss multiphoton confocal microscope at 20x magnification. Images were processed using FIJI, and the Pearson colocalisation coefficient was calculated using FIJI using the Coloc 2 plugin with the following default settings in the plugin: Coste\u0026rsquo;s threshold regression and Coste\u0026rsquo;s randomisation threshold of 10 and PSF 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of CUT\u0026amp;Tag data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMapping\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e150bp paired-end reads for the CUT\u0026amp;Tag-seq were trimmed for adapters using the Trimmomatic tool and aligned locally to the hg38 genome through Bowtie2 (version 2.4.5) with these parameters for pair-end mapping: \u003cem\u003e--very-sensitive-local --no-unal --no-mixed --no-discordant --phred33 -I 10 -X 700\u0026nbsp;\u003c/em\u003e\u003csup\u003e78\u003c/sup\u003e. \u0026nbsp;The best alignment was retained using default bowtie2 options for multiple aligned reads. The bam files were sorted, indexed using samtools, and used to generate bigwigs for individual replicates of H3K27ac and H4K16ac. Merged bam files were obtained across control, PE and PE+IUGR using \u003cem\u003esamtools merge\u003c/em\u003e\u003csup\u003e\u0026nbsp;79\u003c/sup\u003e. These bam files were then sorted, followed by indexing and generating bed and bigwigs for individual histone modifications. Details of samples and replicates for CUT\u0026amp;Tag are listed in extended data table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHistone acetylation domain analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRead counts were obtained for the H4K16ac and H3K27ac for either 10kb with a sliding window of 5kb (for H4K16ac) or 1kb with 500bp sliding window (for H3K27ac) genomic bins on hg38 genome across control, PE and PE+IUGR groups using \u003cem\u003ebedtools multicov\u003c/em\u003e tool. Domains containing less than 50 reads sum across all samples were filtered out from analysis for H4K16ac, and non-zero counts were used for H3K27ac analysis. Differential analysis was performed on these counts by DESeq2, and results were plotted using the r-package \u003cem\u003eEnhanced Volcano,\u0026nbsp;\u003c/em\u003eshowing the differentially acetylated regions (DARs) for H4K16ac and H3K27ac, respectively (regions listed in the source data file). The threshold for the DARs was set at p-adj or FDR \u0026lt;0.05 for Benjamini-Hochberg correction. The number of genomic elements (protein-coding genes, LTRs, Alu and full-length L1 \u0026gt; 5kb) overlapping with the differentially acetylated regions were obtained for H4K16ac and H3K27ac, respectively, using the \u003cem\u003ebedtools intersect\u0026nbsp;\u003c/em\u003etool. Overlapping histone modification profiles across other histone modifications\u0026rsquo; ChIPseq datasets were generated by submitting the DARs to the Cistrome browser \u003csup\u003e80\u003c/sup\u003e for H4K16ac and H3K27ac, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBigwig generation and plotting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSorted bam files were subjected to bigwig generation via deepTools (version 3.5.1) \u003csup\u003e81\u003c/sup\u003e bamCoverage tool with \u003cem\u003e--binSize 20 \u0026ndash;normalizeUsing CPM --scaleFactor=1.0 --smoothLength 60 --extendReads 150 --centerReads\u0026nbsp;\u003c/em\u003eoptions. The signal was normalised to IgG or PE+IUGR vs Control through bigwigCompare. The bigwig files were used for plotting signals or visualisation in the genome browser. The genome-browser views were obtained by viewing the signal tracks in the UCSC genome browser or IGV. Normalised bigwigs (log2 PE+IUGR/control) for histone modifications and RNAseq were generated by using bigwigComapre function in deeptools.\u003c/p\u003e\n\u003cp\u003eSignal plotting at various genomic landmarks and bed coordinates was done using \u003cem\u003edeepTools\u003c/em\u003e. Matrices were generated using deepTools \u003cem\u003ecomputeMatrix reference-point\u003c/em\u003e or \u003cem\u003escale-regions\u003c/em\u003e option. These matrices were used to plot heatmaps or average summary plots using the \u003cem\u003eplotHeatmap\u003c/em\u003e or \u003cem\u003eplotProfile\u003c/em\u003e function in deepTools.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNAseq data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe reads obtained from public placental RNAseq datasets (Fig. 1b) and our cohorts (two biological replicates per sample) were subjected to quality check using FastQC followed by mapping to the human genome, hg38, using STAR\u003csup\u003e82\u0026nbsp;\u003c/sup\u003eby defining parameters specific for single or pair-end and default settings. The bam files generated were merged for the replicates, followed by indexing and bigwig generation using the tools described for CUT\u0026amp;Tag samples earlier, with \u003cem\u003enormalisation using\u0026nbsp;\u003c/em\u003eRPKM. The counts for the genes were obtained using the \u003cem\u003efeaturecounts\u0026nbsp;\u003c/em\u003etool from the SubRead package, and TE counts at the subfamily were obtained using the TEtranscript tool with default options. The count matrices (genes or TEs) were subjected to DESeq2 for the differential analysis with default options. The PCA plot was generated using \u003cem\u003eplotPCA\u0026nbsp;\u003c/em\u003efunction on rlog transformed data for RNAseq data generated in this study. For differential expression analysis of TEs (at subfamily and loci-level) and Genes, we removed the outlier sample S20 (control) from downstream analysis.\u003c/p\u003e\n\u003cp\u003eWhile doing an integrative analysis of the public and our cohorts, different PE classes (across different cohorts) were controlled by taking into the design for DESeq2, along with each dataset serving as a batch. The batch effect removal was performed for the differences in cohorts (each cohort serving as a batch) using \u003cem\u003elimma::removeBatchEffect\u0026nbsp;\u003c/em\u003eon the variance stabilised counts (VST) transformed count matrix. Differentially expressed genes (DEGs) or differentially expressed TE subfamilies were counted as those having Benjamini-Hochberg corrected FDR (padj) \u0026lt;0.05. The DEGs were functionally annotated using \u003cem\u003eEnrichR\u003c/em\u003e and \u003cem\u003eMetascape\u0026nbsp;\u003c/em\u003efor combined analysis. For our cohort, the functional enrichment was performed using \u003cem\u003eclusterProfiler\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u003csup\u003e83\u003c/sup\u003e.\u003c/em\u003e The differential expression of DEGs and TE-subfamilies was visualised as volcano plots or heat maps using EnhancedVolcano or pheatmap and complexHeatmap packages, respectively. For the heatmap, z-scores obtained on variance stabilised (VST) counts were used to compare the control and PE cases. Independent analysis for differential expression in GSE114691 and GSE186257 datasets was done using the online tool iDEP 2.0 \u003csup\u003e84\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFor differential enrichment analysis at individual TE element (loci) levels, counts were obtained by using TElocal tool for control (n=5) and PE+IUGR (n=4) samples. These counts were used for the differential enrichment analyses using the \u003cem\u003eDESeq2\u003c/em\u003e package in R. The differential expression of these TEs was visualised as a heatmap using \u003cem\u003eComplexHeatmap\u0026nbsp;\u003c/em\u003epackage in R. Similarly, we also obtained differentially acetylated TE loci in PE+IUGR vs control for H4K16ac and H3K27ac. For LINEs, the signal was plotted as a heatmap for H4K16ac, H3K27ac and RNAseq. The RNA signal across the intergenic (for protein-coding genes) full-length L1s (\u0026gt;5kb) was calculated as RPKM from the read counts obtained across the control and PE+IUGR samples. This RPKM signal was then plotted as a scattered dot plot using GraphPad Prism 10. The signal was plotted as the \u003cem\u003elog10\u003c/em\u003e value of the RPKM on the Y-axis. Paired violin-plot comparisons were compared using the ANOVA Friedman test for multiple paired data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCis\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-regulatory effect of hyper-acetylated and over-expressed TEs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther comparisons on the hyper- or hypo-acetylated (H4K16ac) TEs for their potential as enhancers were confirmed by comparing the H3K4me1 (ENCFF710ASO), H3K4me3 (ENCFF169MHR) and H3K9me3 (ENCFF541CWH) available from the ENCODE datasets for Chorion villi. Further, TEs (Alus, LTRs and full-length L1s) were filtered for H4K16ac-rich (read coverage \u0026gt; 150 sum total across group either Control (n=5) and PE+IUGR (n=4) respectively) or H4K16ac-poor (read coverage \u0026lt;10 sum total across group either Control (n=5) and PE+IUGR (n=4) respectively). These TEs were used to fetch genes at varying genomic distance bins using bedtools closest. For studying the cis-effect of the H4K16ac+ or H4K16ac\u0026ndash; TEs in control and PE+IUGR, genes were grouped into different bins of genomic intervals of 10kb to 200kb from the TEs that intersected with the H4K16ac+ or H4K16ac\u0026ndash; domain. We compared the RNAseq read density (as FPKM) of those genes (for control and PE+IUGR, respectively) for genomic distances as box plots with the ANOVA Kruskal-Wallis test for multiple comparisons using GraphPad Prism10.\u003c/p\u003e\n\u003cp\u003eA similar comparison was also made for differentially expressed TEs (LINEs, SINEs, and LTRs). Here, we compared the log2 fold-change (PE+IUGR vs control) values as boxed-violin plots using \u003cem\u003etidyplots\u003c/em\u003e package in R for the genes at varying distances from up- or down-regulated TE elements.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIFN Pathway Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor analysis of interferon-regulated genes, IFN-regulated gene lists were downloaded from the Interferome database \u003csup\u003e85\u003c/sup\u003e. For comparison as a heatmap or functional annotation, only genes which are significantly dysregulated (p-adj \u0026lt;0.05) for PE vs control (for all cohort combined analysis) comparison were used. Functional annotation was done using \u003cem\u003eMetascape.\u003c/em\u003e Comparison of histone acetylations (H4K16ac and H3K27ac as CPM) and RNAseq (as log2 Fold change PE vs control) at differentially expressed IFN-regulated genes was done as box plots. For the GSE114691 cohort, a similar comparison of read counts was computed as boxed-violin plots for IFN-regulated genes and cytoplasmic dsRNA sensing and DNA-sensor proteins.\u003c/p\u003e\n\u003cp\u003eGene enrichment pathway analysis for interferon signaling was carried out utilising the SBGNview R package according to the vignette. Briefly, gene counts were extracted from the output of TElocal, and the Gage R package was used to determine enriched pathways. The subsequent output was filtered for the pathway of interest: \u003cem\u003eInterferon alpha beta signalling (reactome::\u003c/em\u003e\u003cem\u003eR-HSA-909733)\u003c/em\u003e and the log fold changes generated by DESeq2 were imposed upon them utilising SBGNview (https://doi.org/10.18129/B9.bioc.SBGNview).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical tests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor box-plots, and comparison of differentially expressed TE-subfamilies, the \u003cem\u003eDunn test\u003c/em\u003e function in the R tool \u003cem\u003erstatix\u003c/em\u003e with Bonferroni correction was used for multiple-group comparisons between the groups. All DESeq2 output was filtered for either FDR/ Benjamini-Hochberg adjusted p-value (padj \u0026lt; 0.05) or Wald test p-value (p \u0026lt; 0.05), as mentioned in the figure legends. P-values for RT-qPCR assays and gene-distance box plots were calculated using the ANOVA Kruskal-Wallis test or Dunn\u0026rsquo;s test with Bonferroni corrections for multiple comparisons using GraphPad Prism10 or R respiectively. Paired violin-plot comparisons were compared using the ANOVA Friedman test for multiple paired data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe data discussed in this publication have been deposited in NCBI\u0026rsquo;s Gene Expression Omnibus (GEO) and are accessible through the GEO Series accession numbers GSE261306 and GSE261276.\u003c/p\u003e\n\u003cp\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE261306\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReviewer access code Ydwbiewuvdonjqz\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?\u0026amp;acc=GSE261276\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReviewer access code ivudgookzdgnbap\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCUT\u0026amp;Tag raw data and processed data files (bigwigs) can be accessed at NCBI with an accession ID, and RNAseq raw data files can be accessed with an accession ID. All the datasets generated, and public datasets used in this study are detailed in (Source Table). CUT\u0026amp;Tag and RNAseq were performed using two different parts of the placental biopsies; replicates that failed QC were not used for the analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u0026nbsp;\u003c/strong\u003eAll the analyses in this manuscript have been carried out using publicly available tools. No custom code was generated for this purpose. The methodology contains the details of the steps involved in the analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe thank patient families for participating in the preeclampsia epigenetics study and donating placental biopsies. We thank QMUL Epigenetics Centre, Sarah Teichmann and Ioannis Sarropoulos (Sanger Institute), Miguel Branco, Helen Rowe, Hemanth Tummala and Pierre Maillard groups (QMUL) for reagents and discussion. This research used the BALM facility at Blizard Institute, Apocrita HPC, supported by QMUL Research-IT.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eMedical Research Council UKRI/MRC grant (MR/T000783/1) (MMP, MP, FB)\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eBarts charity small grant (MGU0475) (MMP). Newton Mosharafa scholarship from the British Council, Egypt, and the Central Department of Missions, Egypt (AS, ASA).\u003c/p\u003e\n\u003cp\u003eFor Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e QMUL has filed a patent application related to the findings of this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMMP, MP, AS and ASA acquired the funding, conceived and designed the study, and supervised the work. AS wrote the preeclampsia epigenetic study protocol and obtained ethical approval. ASA recruited the subjects and collected placental tissue samples and clinical data. MP and ASA performed the CUT\u0026amp;Tag, RNAseq experiments. FB and AD performed RTqPCR with contributions from MMP. MP analysed CUT\u0026amp;Tag, ChIPseq, and RNAseq data with contributions from CI. RK and AD performed cryosectioning, immunofluorescence and imaging. MMP and MP wrote the manuscript. All the authors have read and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSay L et al (2014) Global causes of maternal death: A WHO systematic analysis. Lancet Glob Health 2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldenberg RL, Culhane JF, Iams JD, Romero R (2008) Epidemiology and causes of preterm birth. 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Nucleic Acids Res 41\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6121510/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6121510/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePreeclampsia is a pregnancy-associated hypertension disorder that affects 5\u0026ndash;10% of pregnant women each year, resulting in adverse outcomes for both mother and child. Although the pathophysiology of preeclampsia remains somewhat unclear, it is linked to inflammation, senescence, and accelerated ageing phenotypes. Here, we aimed to investigate the altered epigenetic and transcriptomic changes in preeclampsia by performing genome-wide enrichment analysis of histone acetylation at histone H4 lysine 16 (H4K16ac) and H3 lysine 27 (H3K27ac) along with RNA sequencing analysis in preeclamptic and control placentas. We discovered transposable element (TE) families, including long terminal repeats (LTRs), endogenous retroviruses (ERVs), long interspersed nuclear elements (LINE), and short interspersed nuclear elements (SINE), are upregulated in preeclampsia. TEs upregulated in preeclampsia showed higher levels of H4K16ac, suggesting the contribution of this epigenetic modification in the regulation of TE transcription in the preeclamptic placenta. Genes closer to H4K16ac marked and upregulated TEs are expressed at higher levels in preeclampsia, suggesting that these TEs regulate transcription of nearby genes through their enhancer activity. Furthermore, we demonstrate that the upregulation of TEs results in double-stranded RNA (dsRNA) accumulation in trophoblast cells in the preeclamptic placenta. These TE-derived dsRNAs are detected by antiviral nucleic acid sensors, such as retinoic acid-inducible gene I (RIG-I) like receptors (RLRs), resulting in sterile inflammation due to the activation of the antiviral innate immune system. Our findings indicate that the epigenetic de-repression of TEs in the human placenta activates the type-I interferon response, leading to sterile inflammation in the preeclamptic placenta.\u003c/p\u003e","manuscriptTitle":"De-repression of Transposable Elements by Histone Hyperacetylation Leads to Sterile Inflammation in Preeclampsia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-26 09:22:27","doi":"10.21203/rs.3.rs-6121510/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a387d4dc-bcf5-4626-88f0-c2e1e7d97a8d","owner":[],"postedDate":"March 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":45187411,"name":"Biological sciences/Molecular biology/Chromatin/Histone post-translational modifications"},{"id":45187412,"name":"Health sciences/Diseases/Cardiovascular diseases"},{"id":45187413,"name":"Biological sciences/Molecular biology/Transcriptomics"}],"tags":[],"updatedAt":"2025-03-26T09:22:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-26 09:22:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6121510","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6121510","identity":"rs-6121510","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}