key: cord-0895469-2h3fqcs0
authors: Sorek, Matan; Meshorer, Eran; Schlesinger, Sharon
title: Impaired activation of Transposable Elements in SARS-CoV-2 infection
date: 2022-03-01
journal: bioRxiv
DOI: 10.1101/2021.02.25.432821
sha: 193f3ddb6c90e1916881daeedb47c979685514b8
doc_id: 895469
cord_uid: 2h3fqcs0

Transposable element (TE) transcription is induced in response to viral infections. TE induction triggers a robust and durable interferon (IFN) response, providing a host defense mechanism. Still, the connection between SARS-CoV-2 IFN response and TEs remains largely unknown. Here, we analyzed TE expression changes in response to SARS-CoV-2 infection in different human cellular models. We find that compared to other viruses, which cause global upregulation of TEs, SARS-CoV-2 infection results in a significantly milder TE response in both primary lung epithelial cells and in iPSC-derived lung alveolar type 2 cells. TE activation precedes, and correlates with, the induction of IFN-related genes, suggesting that the limited activation of TEs following SARS-CoV-2 infection may be the reason for the weak IFN response. Diminished TE activation was not observed in lung cancer cell lines with very high viral load. Moreover, we identify two variables which explain most of the observed diverseness in immune responses: basal expression levels of TEs in the pre-infected cells, and the viral load. Finally, analyzing the SARS-CoV-2 interactome, as well as the epigenetic landscape around the TEs that are activated following infection, we identify SARS-CoV-2 interacting proteins, which may regulate chromatin structure and TE transcription in response to a high viral load. This work provides a functional explanation for SARS-CoV-2’s success in its fight against the host immune system, and suggests that TEs could be used as sensors and serve as potential drug targets for COVID-19. Key points Unlike other viruses, SARS-CoV-2 invokes a weak and inefficient transposable element (TE) response TE induction precedes and predicts IFN response Basal TE expression and viral load explain immune responses Distinct chromatin and enhancer binding factors occupancy on TEs induced by SARS-CoV-2

Coronaviruses are a diverse group of single-stranded positive-strand RNA viruses infecting a wide range of vertebrate hosts. These viruses are thought to generally cause mild upper respiratory tract illnesses in humans such as the common cold. However, infection with severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2), which causes Coronavirus Disease-2019 (COVID- 19) , can result in a cytokine storm, which develops into acute respiratory distress syndrome and acute lung injury, often leading to reduction of lung function and even death 1 . The fatality of SARS-CoV-2 increases substantially with age 2 . Unlike other airborne viruses, SARS-CoV-2 is unusually effective at evading the early innate immune responses, such as type I and type III interferons (IFN-I; IFN-III). This is partially achieved by viral proteins that antagonize various steps of dsRNA-activated early host responses 3, 4 . However, the understanding of the kinetics of IFN response in mild and severe COVID-19 patients is lacking. 5 . The authors found that SARS-CoV-2 infection in these cells results in an inflammatory phenotype with an activation of the NF-κB pathway, and a delayed IFN signaling response. Other recently published data support these conclusions 6, 7 , although the outcome of IFN response on the virus and its host are not clear 8 .

Transposable elements (TEs) are abundant sequences in the mammalian genome that contain multiple regulatory elements and can amplify in a short evolutionary timescale 9 . Lately, it was found that TEs induction stimulate antiviral response, via both cis and trans mechanisms 10 . First, TEs have coopted to 4 shape the transcriptional network underlying the IFN response, and some TEs serve as enhancers of antiviral genes in diverse mammalian genomes 11, 12 . TEs are also enriched in enhancers of CD8+ T lymphocytes-specific genes, suggesting that their upregulation might influence not only the innate but also the adaptive, immune response 13 . What's more, due to the similarities between TEs and viral transcripts, cells sometimes misidentify them as invading viruses and trigger the innate immune nucleic acid sensors (e.g. RIG-I, MDA-5 and cGAS) controlling IFN response. Consequently, genome-wide global induction of TEs is common during some viral infections in humans, and transposon induction is part of the first wave response to viral infection 14, 15 . For example, infection by highly pathogenic avian influenza viruses elicits TEs induction 16 . As a result, TE dsRNA is formed, recognized by the host sensors and activates both the NFkB and the IFN pathways, thus enhancing immune response 17 .

Collectively, these findings suggest a causative link between TE induction and the intensity of the IFN response 18 .

This link is also evident in aging as TE basal expression increases with age 19, 20 . Consequently, aging is associated with sterile inflammation which include erroneous IFN response, or the 'Inflammaging' phenomena 21 . On the other hand, at the molecular level, aging is associated with decreased heterochromatin-associated marks, e.g. H3K9me3 22 and DNA methylation 23 Although some studies have addressed TEs expression following SARS-CoV-2 infection 27,28 , none has considered the cell type and basal TE expression levels as important characteristics of their analysis.

Here, we suggest that SARS-CoV-2 infected cells that fail to activate an immediate and effective TE response will be more likely to demonstrate a late immune response. Notably, in primary cells, the IFN 5 response to SARS-CoV-2 is observed only 96 hours after infection, unlike that observed in cell lines 29 . This correlation between the IFN response and TE expression levels strengthens our model and gives rise to a hypothesis that link between mild TE levels and the ineffective innate response to SARS-CoV-2 infection in some individuals 29 . Given the high correlation between TEs activation and higher IFN response, we suggest a possible use for TEs in COVID-19 prognosis

Since TEs are induced in response to many viral infections 14 , we first analyzed recently published datasets of primary human lung epithelial cells (NHBE) infected with influenza (IAV) 30 or SARS-CoV-2 1 . NHBE cells mimic infected human lung cells, showing cytopathic effects after SARS-CoV-2 infection 31 . In these cells IAV infection caused, as expected, a global increase in TE subfamilies expression across all TE families, but SARS-CoV-2 did not ( Figure 1A ). As expected, IFNβ treated cells also activated TE expression levels ( Figure 1A ) because many TEs have IFN-responsive sequences and are upregulated following the induction of IFN response 25, 32 . This raised an intriguing hypothesis that SARS-CoV-2 may avoid the robust TE expression response that often follows a viral infection.

To examine this hypothesis, we expanded our data analysis to more cell types infected with SARS-CoV-2 and other viruses. For SARS-CoV-2, we added lung cancer cell lines A549 and Calu3, and iAT2 primary cells, which represent a young state of normal lung cells, as well as primary human airway epithelial (HAE) cells and peripheral blood mononuclear cells (PBMCs) 5 . As expected, we found that other viruses induced a marked activation of TE ( Figure S1A ). Importantly, we observed that the viral load affects the strength of the TE response to SARS-CoV-2 infection: the higher the viral 6 load the stronger the TE upregulation ( Figure 1B , note green heat map at the bottom denoting the viral load of each sample).

Remarkably, although viral load explains much of the observed TE response ( Figure 1C , R^2=0.66, p=0.0078. See Methods for details), incorporating the TE basal levels into the model improved the accuracy of the prediction of the TE response ( Figure 1D , R^2=0.78, p-value=0.0095). This is because for both low viral load and high viral load, the TE induction levels in the primary cells, which have a higher TE basal level, were mild compared to the transformed cell lines ( Figure 1B 

Because IFN expression had previously been associated with TE expression 11, 17, 33 , we next tested the relationship between IFN and TEs expression during SARS-CoV-2 infection. In agreement with the more robust TE induction, we found a significantly larger number of upregulated IFN genes (Table S1) in the cancer cell lines one day after infection with SARS-CoV-2 compared with primary cells, including NHBE cells ( Figure 2A) . Importantly, NHBE cells did respond to IAV infection and to IFNβ treatment by a significant induction of IFN genes ( Figure 2B and Figure S1B ) and TE expression levels ( Figure 1A ) already after 4-12 hours, excluding the possibility that these cells simply fail to activate TEs or induce IFN response. While infection of iAT2 cells with SARS-CoV-2 resulted in a mild but detectable TE response ( Figure 1B , larger TE log fold change [LFC] compared to NHBE cells, p< 1e-6

Kolmogorov-Smirnov test) one day post infection, it showed almost no IFN response, even weaker than infected NHBE cells (smaller LFC of IFN genes, p<0.0001 Kolmogorov-Smirnov test, Figure   2A ). This mild TE response slightly increased after 4 days and was accompanied by a significant IFN response ( Figure 2A ). This suggests that the early TE response precedes the late IFN response in these cells.

The idea that TE activation is inducing IFN response following SARS-CoV-2 infection was also supported by A549 cells over-expressing the ACE2 receptor that were infected with SARS-CoV-2.

These cells, when treated with Ruxolitinib, a JAK1/2 inhibitor that is known to reduce inflammatory response 34 , showed reduced IFN response ( Figure 2C ), while the global TE response remain high ( Figure 2D ). This shows that although IFN response upregulate TE expression, TE overexpression is not solely dependent on the IFN response. Finally, although done on different tissues, genetic backgrounds and multiplicity of infection (M.O.Is), the magnitude of IFN changes strongly correlated with TE expression changes ( Figure 2E ). This correlation (p<0.05, permutation test on Spearman correlation) was specific to IFN related genes (and epifactors, see below), as other random groups of genes were not correlated with TE changes. These, and other 14, 32 results, indicate that while IFNβ treatment activate TEs expression, TEs upregulation is an early response to viral infection, which precede the IFN response and induce it in a positive feedback loop 14 .

Since TEs were shown to function as regulatory elements, or enhancers, for adjacent host genes encoding critical innate immune factors 11 , we also tested the relation between expression changes in individual TEs and their neighboring genes. In general, genes that were adjacent to upregulated TEs, were prone to be upregulated as well ( Figure 2F ). Focusing on IFN-related genes (Table S2 ) located near up-regulated TEs revealed an even stronger effect, suggesting that this IFN gene induction by an 8 adjacent TE occurs also following SARS-CoV-2 infection ( Figure S1C ). However, while ~60% of the upregulated IFN-related genes were located near upregulated TEs in response to IAV infection, only 10-30% of the upregulated IFN related genes were located near upregulated TEs in response to SARS-CoV-2 infection ( Figure 2G ). This suggests that although following SARS-CoV-2 infection TEs have the capacity to serve as cis-regulatory enhancers to nearby genes including IFN related genes, the TEs nearby IFN related genes remain mostly silent. In concordance, gene ontology (GO) analysis revealed that while the upregulated genes adjacent to upregulated TEs in response to IAV infection were enriched for cytokine receptor binding genes, no immune related function was enriched among those in response to SARS-CoV-2 infection.

Taken together, these data suggest that while TE induction following IAV infection precedes and contributes to the expression of IFN-related genes 30 , SARS-CoV-2 infection fails to upregulate the TEs coopted for immune activation. According to this hypothesis, TE induction is a crucial step in the activation of the anti-viral immune response against RNA viruses 35 . However, TE response to SARS-CoV-2 infection is limited: considerably less TEs are activated, the level of their upregulation is lower, and most importantly, their specificity is altered: those TEs that induce immune response are not the ones activated by SARS-CoV-2 infection.

Regulation of TEs transcription is largely achieved through epigenetic silencing 36 . To understand the nature of the distinctive TE regulation of those TEs that do go up following SARS-CoV-2 infection, we investigated which specific histone modifications are found on the upregulated TEs before SARS-CoV-2 infection (Table S2) .

To this end, we used a large-scale dataset including multiple ChIP-seq profiles for histone modifications (HMs) in uninfected A549 cells 37 . Since for A549 cells we also have pre-and post-SARS-CoV-2 infection data, it allowed us to examine the 'epigenetic signature' 38 Table S3 ).

We found that the TEs that were upregulated in response to SARS-CoV-2 infection in A549 cells of all different classes were enriched for active histone marks in uninfected cells, with a subset of TEs marked by H3K36me3 as well as the combination of H3K27ac, H3K4me3, H3K79me2 and H3K9ac

( Figure 3A -E and Figure S2 ). This was consistent among different ChIP-seq experiments for the same histone modification ( Figure S3 ). SINEs and DNA elements upregulated in response to SARS-CoV-2 infection were especially enriched for active marks compared to both random TEs and IAV-induced TEs. By contrast, LINEs that were upregulated in response to SARS-CoV-2 infection were highly enriched for a bivalent signature of the repressive H3K9me3 mark together with the active H3K36me3 mark ( Figure 3G and Figure 4) . Interestingly, all classes of SARS-CoV-2-induced TEs were depleted for H3K27me3 in the uninfected cells ( Figure 3F ). This is in stark contrast to IAV-induced TEs, which show no such depletion ( Figure 3F ).

Repeating the analysis at the TE family level, we found that the largest enrichment was in the specific L1 and L2 LINE families and in the MIR and Alu SINE families ( Figure 5) . These families showed a strong enrichment especially in H3K36me3 and H3K79me2, and were depleted for H3K27me3. The

H3K36me3 and H3K79me2 were also significantly enriched on the main ERV/LTR families. Overall, these results suggest that the TEs that are upregulated in response to SARS-CoV-2 in A549 cells have a distinct epigenetic profile, which differs from that of TEs upregulated by IAV, as well as from the general epigenetic profile of TEs in the human genome. Specifically, SARS-CoV-2-induced TEs are devoid of H3K27me3, and are comprised of two major subsets of TEs: i. SINEs and DNA elements marked by a highly active chromatin profile, and ii. a bivalent group of LINEs marked by both repressive and active marks, which keeps them in a poised state ready for infection-induced expression.

This attests at the relative failure of the SARS-CoV-2 infected cells to activate TEs in a repressive chromatin state as IAV does.

Seeking a possible mechanism for the activation of TEs with this distinctive chromatin modification pattern, we searched for genes, expression of which changes in correlation with TE response. We focused on genes that had a high correlation with TE response both in the SARS-CoV-2 infection from Blanco-Melo et al. and in the iAT2 infected cells from Huang et al. We found that the inversely correlated genes were enriched for mitochondrial-related genes and processes ( Figure 6A and Table S4 , and see Methods), consistent with previous reports 39 . By contrast, genes that were positively correlated with TE response among all samples were enriched, in addition to type I interferon production, for chromatin, DNA and enhancer binding, RNA Pol-II binding, transcription factor and cofactor binding as well as histone binding, demonstrating a clear epigenetic and chromatin-related signature ( Figure 6A and Table S4 ).

Indeed, intersecting the positively correlated genes with all genes that encode for chromatin binding proteins, or epifactors 40 , showed highly significant enrichment ( Figure 6B , green. 44 genes, fold-enrichment=2.33, p < 10 -7 , Fisher's exact test). Interestingly, SETD2, the human H3K36 lysine trimethylase, was among the epifactors that were positively correlated with TE response ( Figure 6C ).

H3K36me3 marks gene bodies of active genes. In addition, SETD2 methylation of STAT1 is crucial for interferon response 41 and its H3K36 methylation contributes to ISG activation, pointing at its role in the cellular response to viral infection. Finally, recent evidence shows that SETD2 is essential for microsatellite stability, implicating its role in non-genic transcriptional regulation 42, 43 . Interestingly, SETD2 is also among the interacting proteins of SARS-CoV-2, and compared with other interactomes of coronaviruses as well as of different IAV strains, we found that SETD2 is specific for SARS-CoV-2 (Table S5) .

We therefore searched for more epifactors in the SARS-CoV-2 interacting proteins. Among the ten epifactors that interact with SARS-CoV-2, we found the histone acetylation related proteins BRD2, BRD4, which were highly correlated ti the TE response ( Figure 6C) , and HDAC2, all of which are, once again, SARS-CoV-2-specific (Table S5) . We also found two TE-related epifactors: the SARS-CoV-2-specific interacting epifactor DDX21, a DNA damage and dsRNA sensing protein, and MOV10, an RNA helicase that also restricts LINE expression 44 , which interacts with SARS-CoV-2, as well as with IAV. These observations suggest that SARS-CoV-2 may affect TE expression through interaction with a subset of specific epifactors.

In this study, we re-analyzed published data to examine the link between SARS-CoV-2 infection and transcriptional activation of Transposable Elements (TEs). We find that in normal lung epithelial cells, Since TE expression rises with age 19, 20 , and age is the most significant risk factor for COVID19related death, we hypothesize that high basal level of TE expression desensitizes the TE induction response to viral infection, explaining the age-related decline in survival. In contrast, young people should benefit from higher SARS-CoV-2 induced TE overexpression that, in turn, prompts IFN response early in the disease course 6 . Our "TE desensitization" model makes several predictions. First, it anticipates that induction of TEs would precede the IFN response. Second, the cellular TE activation response to SARS-CoV-2 should be associated with basal levels of TE expression, where lower basal levels predict stronger cellular responses. As such, and thirdly, it anticipates that in older cells, which would have a higher basal level of TEs and would be "TE desensitized", the TE activation response will be significantly milder. This would lead, fourthly, to a less effective IFN induction response, allowing SARS-CoV-2 to operate "under the radar" at early disease stages when the viral load is still low. Finally, at later stages as the viral load accumulates, selected TEs are induced. Those TEs are characterized by histone modifications, regulators of which are found to interact with the SARS-CoV-2 proteome.

As our model predicts, we showed that SARS-CoV-2 elicits a weaker TE activation response compared with other viruses, and a weaker TE activation response in primary cells compared with cancer cells.

These primary cells have higher initial levels of TEs. We speculate that in old normal cells, low TE expression fails to induce viral mimicry and thus fails to initiate early innate immune reaction. This is in line with previously published data that link TE expression to immune reaction 17, 33 , as well as studies that show suppression of dsRNA-activated early host responses following SARS-CoV-2 infection 3, 4 . Our own analysis also shows a highly significant correlation between TE activation and the induction of IFN response.

Cells that showed a large TE response also had a strong IFN response to SARS-CoV-2 infection, but not through adjacent upregulated TEs. This is because the immune response can be induced by other mechanisms beside by TEs acting as enhancers for immune genes 36 . For example, the TE themselves can also act in trans, being sensed as dsDNA and dsRNA by the cells sensors for these species Therefore, the impaired TE activation following SARS-CoV-2 infection may have a double impact, yielding the delayed IFN response seen in COVID-19 patients. 13 In addition, SARS-CoV-2 viral load seems to be linked with TE expression level changes.

Accordingly, we were able to compare TEs induced following a rigorous SARS-CoV-2 infection to that of IAV infection in A549 cells. Interestingly, the TEs that are upregulated in response to SARS-CoV-2 in A549 cells showed a distinct epigenetic profile, which differed from that of TEs upregulated by IAV, as well as from the general epigenetic profile of TEs in the human genome. Specifically, SARS-CoV-2-induced TEs are devoid of H3K27me3 and enriched for H3K36me3, a subset of which is bivalently marked also with H3K9me3. This atypical pattern was identified as a mark for poised enhancers that control surrounding gene expression 46 . Here, the activation is potentially mediated by the H3K36 trimethylase SETD2. SETD2 expression is closely correlated with TE induction, and it specifically interacts with the SARS-CoV-2 NSP9 protein.

Together, these data suggest a model where SARS-CoV-2 entry modifies SETD2 deposition or activity, leading to aberrant H3K36me3 enrichment on a subset of TEs. Consequently, these TEs are and --winAnchorMultimapNmax 200 to allow for large numbers of multi-mapped reads for downstream analysis of TEs. For ChIP-Seq data, alignment with STAR was followed by filtering out of non-uniquely mapped reads.

Gene expression was analyzed separately. Genes were considered significantly up-(down-) regulated if they had at least 1.5 fold difference and FDR corrected p-value < 0.05. We quantified TE expression changes both at the level of TE subfamilies and at the level of individual repeat loci. For TE subfamily quantification we used TEtranscripts from the TEToolKit 50 . The TEtranscript algorithm quantifies TE subfamilies and genes simultaneously by assigning together multi-mapped reads which are associated with the same TE subfamily. Human repeat annotations for hg38 were downloaded from TEtranscripts site. TEtranscripts was run using --mode multi and -n TC. This was followed by differential expression analysis using DESeq2 51 . TE response was quantified using the 95-percentile of the log2 fold-change of all TE subfamilies. 16 To quantify gene expression and to determine the locations of individual TEs that change in expression we used featureCounts v2.0.0 50,52 from the Subread package which uses only uniquely mapped reads.

Simple repeat elements were removed prior to the analysis. The TE and gene count matrices were combined, followed by DE-Seq2 to compare between mock and infected cells. Individual TEs were considered significantly up-(down-) regulated if they had at least 1.5 fold difference and p-value < 0.05. In order to robustly calculate the IFN response, we used all genes associated with the following GO terms: GO_0035458, GO_0035457, GO 0035456, GO_0035455, GO_0034340 , as well as genes associated with the following pathways in pathcards (https://pathcards.genecards.org/Pathway):

Immune response IFN alpha/beta signaling super-pathway and pathways 2747 , 2388, 213 (Table S1) . 

To calculate the ChIP profile of different histone modification on TEs, we used the processed output files from the ENCODE project, which are filtered for the ENCODE blacklist regions (see Table 1 ).

For each TE we defined the flanking region as the TE location and its surrounding 500 bp up-and down-stream. If the flanking region intersection with ChIP peak locations was non-empty then this TE was considered as TE with peak. For histone modification clustering (Figure 3 and Figure S2 ) we used the Jaccard metric. Enrichment of peaks on up-regulated TEs was calculated using hypergeometric test.

Genome tracks were produced using the ggbio package in R 53 .

For TE response-gene correlation, we used Spearman rank correlation between the 95-percentile TE subfamily log fold-change and the log fold-change of genes in the SARS-CoV-2 infected cells. We Genes were defined as highly correlated and inversely-correlated if the correlated was larger than 0.97 and smaller than -0.97 among these samples, respectively, and if, in addition, the direction of change aligned with that in the iAT2 cell between d1 and d4 after infection.

These cutoffs corresponded to approximately the top and bottom 5%.

For interactome analysis, we downloaded publically available interactome lists (see Table 2 ). A gene was considered in SARS-CoV-2 interactome if it was included in at least 3 out of 5 sources, and as a SARS-CoV-1 interacting partner if it was included in both relevant sources. 

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