Next Article in Journal
Association of Alleles of Human Leukocyte Antigen Class II Genes and Severity of COVID-19 in Patients of the ‘Red Zone’ of the Endocrinology Research Center, Moscow, Russia
Previous Article in Journal
A Multimodal Approach in the Treatment of Persistent Post-COVID
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

A Systems Biology Approach on the Regulatory Footprint of Human Endogenous Retroviruses (HERVs)

Haematology Laboratory, Unit of Molecular Biology, University Hospital of Ioannina, 45500 Ioannina, Greece
Neurosurgical Institute, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece
Diseases 2022, 10(4), 98;
Original submission received: 8 August 2022 / Revised: 22 September 2022 / Accepted: 7 October 2022 / Published: 2 November 2022


Human endogenous retroviruses (HERVs) are a family of endogenous retroviruses that comprise the ~8.93% of the human genome sequence, with a high proportion being human specific. The recent expansion of repeated HERV sequences has offered a framework for genetic and epigenetic innovation. In the current report, a systematic approach is implemented to catalogue regulatory elements within HERVs, as a roadmap to potential functions of HERV sequences in gene networks. ENCODE Project has offered a wealth of epigenetic data based on omics technologies. I analyzed the presence of HERV sequences on consensus cis-regulatory elements (cCREs) from ENCODE data. On the one side, HERVs are in 1 out of 9 cCREs (>100.000 cCREs in total), dispersed within the genome and present in cis-regulatory regions of ~81% of human genes, as calculated following gene enrichment analysis. On the other side, promoter-associated HERV cCREs are present adjacent to (in a 200 bp window) the transcription start sites of 256 human genes. Regulatory network production, followed by centrality analysis led to the discovery of 90 core genes containing HERV-associated promoters. Pathway analysis on the core network genes and their immediate neighbors revealed a regulatory footprint that, among others, is associated with inflammation, chemokine signaling and response to viral infection. Collectively, these results support the concept that the expansion of regulatory sequences derived from HERVs is critical for epigenetic innovation that may have wired together genes into novel transcriptional networks with critical roles in cellular physiology and pathology.

1. Introduction

A major breakthrough of the Human genome project was that a large proportion of genomic sequence was repeat-associated [1,2]. Based on Repeatmasker algorithm, more than half of the human genome has been calculated to be repeat-associated [3], while other approximations consider that this is an underestimation and over two thirds of the human genome are derived from repetitive elements [4]. Retrotransposons are a major category of repetitive DNA, expanded through evolution mainly through an RNA intermediate, a mechanism called retrotransposition [5]. The mainstream opinion is that expansion of retrotransposons may be deleterious to genomic integrity and lead to disease, that is why the cell has several restraint mechanisms, while in the long term the presence of repetitive DNA can be exploited as a source for genetic novelty [5]. Transposons contain a number of regulatory elements and can regulate the expression of genes within the same regulatory domain [6]. In addition, transposon sequences can affect several physiological processes, such as imprinting, by acting as sites for methylation [7], and recombination, by providing an abundant source of Holliday Junctions sequences [8].
Among repetitive DNA elements, Human Endogenous Retroviruses (HERVs) comprise almost 9% of the human genome [3], having a pan-primate evolutionary distribution [9]. HERVs share the same structural features with exogenous retroviruses and contain Long Terminal Repeats (LTRs), a structural feature for expression and retrotransposition into a novel genomic site. Regulatory sequences derived from HERVs, mostly those present in their LTRs are an abundant source for epigenetic innovation during evolution [10].
HERVs contain in their LTRs a rich repertoire of transcription factor binding sites that may be common in different elements or unique [11,12]. Some common transcription factors in many HERV LTRs include among others YY1 [13], Sp1 [14], Myb [15] and NF-κB [16,17]. This polymorphic binding landscape in HERV LTRs may be a reason why different members of HERV family exhibit distinct transcriptional footprints, with a noteworthy upregulation of several members in male and female reproductive system and in early development [18,19,20,21,22,23]. This noteworthy variation of expression is considered that is partly due to the unique features of their LTRs, a source of epigenetic innovation [24]. A well-established paradigm is the role of HERV-H, a primate-specific member of the HERV family that is expressed in naïve-like stem cells [25]. Importantly, sequences derived from HERV-H seem to have a dual role in the epigenetic determination of pluripotency, either by chromatin reorganization during human pluripotent stem cell (hPSC) differentiation, by creating topologically associating domains (TADs) [26], or by their involvement in the evolution of novel enhancers combined with their action as long non-coding RNAs [27].
The results from the ENCODE project have indisputably rejected the idea of junk DNA, since at least one biochemical function has been assigned to the majority of non-coding genomic sequence, including repetitive DNA [28]. On the contrary, this unprecedented wealth of experimental evidence provides a roadmap for discovering novel functions of our genome. Following this conceptual framework, I present a post-genomic view on the regulatory functions of HERV-derived sequences. The results of the current study support a genome-wide function of HERVs in the vicinity of genes as well as a promoter-specific involvement in regulatory networks that may be affecting physiology and pathology.

2. Materials and Methods

2.1. Sequence Analysis and Annotation

The data retrieval workflow is based in the concepts implemented in previous studies [29,30,31] and included the following steps: A repository on HERV sequences was generated based on repeatmasker tool [3,32] and retrieved using the UCSC Table Browser [33,34,35]. The presence of candidate cis-regulatory elements (cCREs) extracted from data available from the ENCODE (Encyclopedia Of DNA Elements) Project Consortium [36,37] in HERV sequences was determined using the UCSC Table Browser, in the extracted data and further analyzed using bioinformatic tools available in the UCSC Genome Browser and the UCSC Table Browser ( (accessed on 4 August 2022)) [38,39,40,41]. Table Browser was used to extract the genomic features of HERVs and cCREs, including total number, individual and total size, as well as their relative distributions. The latest GENCODE reference [42] was used to annotate promoter-specific HERV-CRE sequences to their respective gene, in UCSC Table Browser.

2.2. Analysis of Genomic Distribution Related to Human Genes

Gene-centered genomic annotation and enrichment analysis was performed using Peak Annotation and Visualization (PAVIS) tool [43], available in National Institute of Environmental Sciences web server ( (accessed on 4 August 2022)). PAVIS offers a gene specific annotation to the following sub-categories: upstream or downstream of genes (up to 1000 bp), 5′ UTRs, exons, introns and 3′ UTRs.
Total cCREs that contain HERV sequences were annotated in relation to transcription start sites (TSSs) of Human genes using the Genomic Regions Enrichment of Annotations Tool (GREAT) ( [44]. The data derived from GREAT were inspected and analyzed further using the UCSC Genome Browser. All the aforementioned servers were last accessed on 4 August 2022.

2.3. Network Analysis

Regulatory networks were constructed with the GENEMANIA prediction server tool ( (accessed on 4 August 2022)), using the default settings and provide 20 nearest-neighbor genes in the final network [45]. The initial regulatory network derived from Genemania server was further processed in Cytoscape application, version 3.8.1 [46,47,48]. Centrality analysis to assess the presence of the core genes in the network was performed in Cytoscape, using Centiscape plugin [49,50]. Briefly, the degree of interaction was used as a weighting method to calculate centrality. Central genes with significant interactions were considered the ones that contained a degree of interactions above the threshold of mean interactions.
The participation of the core regulatory network from the aforementioned network in cellular pathways was calculated by enrichment analysis in the Reactome pathway knowledgebase, version 81 ( (accessed on 4 August 2022)) [51]. The Enrichr tool ( (accessed on 4 August 2022)) [52,53] was implemented to evaluate for enrichment of Gene Ontologies, Cellular Pathways, and Disease. Appyter was used for data visualization [54]. The servers were last accessed on 4 August 2022.

3. Results

3.1. Overview on HERV-Associated cis-Regulatory Elements

The purpose of the current study was to establish an insight on the regulatory roles of HERV-derived sequences. An overview of the study design is presented in Figure 1. An initial goal was to establish the genome-wide landscape of regulatory regions that are associated with HERVs. First, using the available data from repeatmasker, I filtered for the presence of masked sequences derived from the family of endogenous retroviruses. In total, based on the latest repeatmasker integration of ENCODE data, Human Genome Contains 735.887 HERV-derived sequences that account for the 8.93% of the human genome. Next, I intersected the cCREs extracted from available ENCODE project consortium data (in total 926.535 elements, accounting for 8.14% of the genome), with that of HERVs [55]. As a result, 1 out of 9 cCREs elements (105.875 elements and 0.96% of the genome) contain HERV-derived sequences, later on referred as HERV-CREs. The genomic distribution of HERV-CREs is presented in Table S1. Among the total HERV-CREs, 920 are in promoter-like signatures (PLS), 8.538 are in proximal enhancer-like signatures (pELS), 82.821 are in distal enhancer-like signatures (dELS), while 4.063 are in DNase-H3K4me3 enriched regions and 9.533 are in CTCF-binding sites.

3.2. HERV-CREs Are Present in the Vicinity of the Majority of Human Genes

Two types of analyses were performed in HERV-CREs to provide an overview on the genome-wide regulatory landscape of HERV-CREs in relation to human genes. In the first, HERV-CREs were used for enrichment analysis in relation to genes, based on PAVIS tool (Figure 2, panel A). Approximately, 1 out of 11 HERV-CREs reside adjacent to Transcription Start Sites (TSS) of human genes, while the majority is intronic or intergenic, accounting for 31.8% and 56.7% of total HERV-CREs, respectively. In addition, a second analysis was performed to reveal the distribution of HERV-CREs in respect to gene TSS, based on GREAT tool (Figure 2, panel B). The genomic regions of HERV-CREs have been found near the TSS of 15,173 (81%) of all 18,777 genes (Table S2). The majority of HERV-CREs was in a distance of 5–500 kilobases (kb) to the nearest gene TSS. Together, PAVIS and GREAT analysis reveal an interspersed and gene-specific genomic distribution.

3.3. The Regulatory Footprint of Promoter-Specific HERV-CREs

Among HERV-CREs, a number of 920 sequences were present in promoter-like signatures (PLS). These elements were further analyzed for their involvement in gene-regulatory networks, since they possess a genomic position in a window of 200 bp near gene TSS, making them the ideal candidates to act as core promoter gene-regulatory elements [56,57]. The UCSC table browser was queried using the latest GENCODE reference to allocate promoter-specific HERV-CREs into their respective genes, using the ENCODE annotation of PLS cCREs, which is regions that fall within a window of 200 bp of an annotated GENCODE TSS. In total, 256 genes were found to contain at least one HERV-CRE in their promoter regions (Table S3).
In a latter step, the gene-set containing HERV-PLS CREs, was analyzed in the GENEMania server to generate a regulatory network containing interactions between these genes and a generated dataset of 20 nearest-neighbor interacting genes. The resultant network (containing the genes with at least one interaction and the 20 resultant genes) was transferred into Cytoscape environment for visualization and further analysis with the Centiscape plugin, to find the central/core-network genes, based on the number of interactions. The final network is depicted in Figure 3, indicating the central genes in yellow.
Additionally, to provide a comprehensive synopsis of the functions on genes in the final network, we added the gene dataset in Reactome server (Table S4). The top-10 enriched cellular pathways, according to enrichment p-value, contain: Chemokine receptors bind chemokines; Ca2+ pathway; G beta:gamma signaling through PLC beta; Presynaptic function of kainate receptors; ADP signaling through P2Y purinoceptor; Activation of kainate receptors upon glutamate binding; Interleukin-10 signaling; Peptide ligand-binding receptors; G-protein beta:gamma signaling.
The calculated network contains 90 central genes, that were further analyzed with GENEMania, to provide the core regulatory network of genes with HERV-PLS CREs (Figure 4). The final, core network contains different types of interactions (Co-expression, Physical Interactions, Predicted, Pathway, Co-localization, Shared protein domains and Genetic Interactions) as well as inferred functions, based on enrichment analysis. Among others, core HERV-associated genes may share these functions: cellular response to chemokine; leukocyte migration; G protein-coupled receptor binding; ERK1 and ERK2 cascade; cellular calcium ion homeostasis; response to lipopolysaccharide; tumor necrosis factor production. In concert, the results from the above analysis provide novel data that suggest that a regulatory network which contains HERV-PLS CREs might exist.

3.4. Involvement of Central Promoter-Specific HERV-CREs in Cellular Pathways

The associated genes of the central promoter-specific HERV-CREs were further analyzed using Enrichr meta-analysis enrichment tool to assess their association with specific ontologies, pathways and diseases. The results of Enrichr analysis are presented in Figure 5 and Figure 6 and Table S5. Based on enrichment in TTRUST (transcriptional regulatory relationships unraveled by sentence-based text-mining) database, the majority of enriched transcription factors are members of NF-κΒ (including human RELA, and NFKB1) STAT (including STAT1, STAT3 and STAT6) and IRF (including IRF3 and IRF8) families. The top-10 enriched factors also include SPI1 and JUN factors (Figure 5).
Based on the most enriched Ontologies (GO Biological Process 2021), HERV-associated genes are involved in inflammatory response, cytokine and chemokine signaling and the chemotaxis/migration of several types of white blood cells (Figure 5). According to ClinVar 2019 database, central HERV-associated genes are linked to neoplastic disease (such as ovary, hepatocellular, prostate cancer and lynch syndrome), chronic granulomatous disease, susceptibility to HIV-1, myocardial infarction, disorder in organic acid metabolism, neural tube defect and microvascular complications of diabetes (Figure 5).
Importantly, the top enriched cellular pathways for HERV-associated genes according to MSigDB Hallmark 2020 database, contain several common genes. Common genes mostly include members of chemokines, chemokine ligands and their receptors (CCL2, CCL4, CCL5, CCL7, CCL17, CXCL6, CXCL9 CXCL10 CXCL11 CCR1, CCR2 and CCR4), interleukins and their receptors (IL1B, IL1R1 and IL1R2), apoptosis regulators (CASP3 and CFLAR) G-protein GNAI2, matrix metalloproteinase MMP2 and matrix metalloproteinase inhibitor TIMP2. These proteins take part in the enriched pathways: IL-6/JAK/STAT3 Signaling; Allograft Rejection; Inflammatory Response; Interferon Gamma Response; Apoptosis; TNF-alpha Signaling via NF-kB; IL-2/STAT5 Signaling; Complement; Reactive Oxygen Species Pathway (Figure 6).

4. Discussion

In this paper a framework on the regulatory roles of Human Endogenous Retroviruses was developed. My approach provides a novel view on gene regulation based on the wealth of data from ENCODE project. The presented conclusions are based on two different approaches. First, a general approach provides an overview of HERV-associated regulatory roles and signifies that 4 out of 5 human genes contain at least one HERV-associated cis-regulatory element. Second, based on promoter-specific HERV elements, I reveal the presence of a distinct regulatory footprint. Genes that form a core regulatory network are involved in significant pathways that impact cellular homeostasis and pathology.
The reference for characterizing a HERV sequence has been the repeatmasker tool [32], a golden-standard approach for transposon characterization. In addition, the cCREs library of regulatory elements from ENCODE [28,37], provided genome-wide experimental data that make possible a post-genomic view of HERV-associated gene regulation with high confidence. An interesting feature of both cCREs and HERVs is their diversity in size and genomic distribution. This diversity is more obvious in cCREs, since there are more cCREs sequences than those of HERVs origin (926.535 elements compared to 735.887, respectively), but cCREs account for a smaller fraction of the genome than HERVs (8.14% of the human genome compared to 8.93%, respectively). Importantly, there is a significant number of cCREs that contain HERV sequences, with the majority belonging to distal enhancer elements. Having that in mind, GREAT analysis revealed that the dispersed nature of HERV sequences allowed for their presence on the regulatory domains of the 80% of human genes (Figure 2). The distribution of HERVs has been verified as genome-wide, as can be seen in elements in the HERV-K and HERV-H groups that some are also polymorphic in human populations [58,59,60]. PAVIS analysis results also suggest an intron- and intergenic region-based distribution (Figure 2). GREAT tool considers a gene regulatory domain definition that takes into account a basal regulatory domain in a minimum distance upstream and downstream of a gene TSS, which is extended up to 1 Mb [44]. This definition comes in agreement with the latest genome-wide experimental and computational data [61,62,63,64,65]. Collectively, the presence of HERVs in cCREs that are based in experimental data, suggest that HERV sequences may impact, in different degree, the epigenetic regulation of 4 out 5 human genes. The fact that HERVs contain an abundance of regulatory elements [66,67,68] and are actively transcribed in humans [69] strengthen the possible impact of the above finding. It should be noted that the regulatory potential of HERVs sequences may also involve viral transcription factors. Interplay between HERVs and several types of viruses, such as tumor viruses [70,71] and HIV-1 [72,73,74], may impact cellular physiology. Since apart from CREs, the presence of trans-regulatory elements may impact the expression of genes and genomic structure [75], future studies are needed to provide a more accurate view on the role of HERVs in the three-dimensional genome landscape.
Promoter-specific HERVs have a potential to act as core-promoter regulatory elements. Promoter specific CREs were interrogated for the presence of HERV sequences and in a later step they were assigned to their adjacent genes. In total, 256 genes have HERV sequences in their promoters. Based on the concept that repetitive elements from HERVs may act in concert, HERV-related genes have the potential to take part in the same signaling pathways and to form a regulatory network. The inferred network from GENEMania (Figure 4) interconnects HERV-related genes with more than 3.000 interactions, a notion that supports the action of these genes in concert. Several lines of evidence corroborate this notion. First, The action of HERV-H in naïve-like stem cells, where gene are expressed in concert, based on repetitive HERV-H regulatory elements [25]. Second, HERV-K elements can regulate stem cell function and neuronal differentiation [76]. Third, HERV-W have been inferred to regulate inflammatory groups of patients with schizophrenia and bipolar disorder [77]. Among the most significant pathways affected by HERV-related genes, based on Reactome analysis were presynaptic functions as well as GABA B receptor pathway (Table S4). This result is in accordance with pathway analysis where a significant pathway from ClinVar 2019 database is neural tube defect (Figure 5), the functions of HERV-W as well as the fact that brain diseases can be associated with inflammation and HERVs expression [78]. It should be noted that the interactions presented in the regulatory network are based on computational imputations and should be corroborated in future studies.
Inflammatory response has been surfaced as one among the most significant pathways that are associated with HERV-related genes, based on both Gene Ontology (Figure 5) and MSigDB Hallmark 2020 database (Figure 6). There have been evidence that HERVs are implicated in inflammation [79]. The current study substantiates this concept and reveals a set of genes that have a direct involvement in inflammatory response and immunity (Figure 6), such as chemokines, chemokine ligands and their receptors and interleukins and their receptors [80,81,82,83]. Significantly, the regulatory footprint of HERV-associated promoters contains binding sites for several members of the NF-κB family of transcription factors (Figure 5). TNF-alpha signaling via NF-κB is enriched, while NF-κB family also takes part in Interferon Gamma Response (Figure 6) [84]. NF-κB pathways are among the master regulators of inflammation and immunity [85,86,87,88]. It is known that NF-κB binding sites reside in Alu elements [89] that may act in concert and aid in stochastic gene expression [90]. As regards HERVs, they contain several binding sites for NF-κB factors and their LTRs can be modulated by NF-κB [16,17]. This study suggests the possibility of dispersed sequences from HERVs to act in a similar manner to wire genes into NF-κB-related regulatory networks. NF-κB takes part in regulatory loops and networks with several genes as well as non-coding RNAs and can impact, among others, the interplay between inflammation and cancer [91,92,93,94,95]. This potential supports the notion that such regulatory loops can be also HERV-associated. The core HERV network also include binding sites for transcription factors of the STAT family and enriched pathways of IL-6/JAK/STAT3 Signaling and IL-2/STAT5 Signaling (Figure 6). JAK/STAT pathways are involved in inflammatory response in physiological and pathological conditions [96,97,98]. Based on this study results, I suggest that STAT binding sites may be spread in parallel to that of NF-κB during HERV expansion in the human genome and take part in concerted gene regulation.
Among the significant pathological conditions in ClinVar, neoplastic disease is present in 4 out of 10 total diseases. HERV genes are associated with ovary, hepatocellular and prostate cancer, as well as lynch syndrome, a hereditary type of nonpolyposis colorectal cancer (Figure 5). The probable involvement of HERVs in cancer is a long-enduring concept [68,99,100]. Specifically, HERV transcripts have been found upregulated in prostate [101], kidney [102] breast [102,103,104,105], hepatocellular [106], ovarian [107] and colorectal cancer [108,109], as well as in soft tissue sarcoma [110] and leukemia [111]. Recent data suggest a connection between HERVs and cancer stemness [112]. In particular, HERV retrotranscriptome has been found to accurately characterize leukemia stem cells [111] and to show a widespread expression that may be tissue- and developmental-stage specific [113]. The aforementioned data agree with the notion that HERV retrotranscriptome can characterize normal and cancer cells, while the question remains whether thay act as the main regulatory elements in such condition or as passive transcribed elements. Consequently, further experimental evidence is needed to provide a causative role for HERVs in carcinogenesis. Previous results from our research team suggest a similar role for VL30s, a mouse analogy to HERVs that can impact a number of cancer-associated pathways in the mouse [30]. Our previous results implicate VL30s in cellular stress and carcinogenesis [114,115,116,117,118], a feature that may be shared with HERVs, based on their similar structure and disseminated genomic distribution.
Viral infection can lead to transactivation of HERVs, leading to an interplay between exogenous and endogenous viral sequences [70,119]. In this study, HERV-related genes found take part in the susceptibility to HIV-1 pathway (Figure 5). HIV-1 infection activates endogenous retroviral promoters regulating antiviral gene expression [120], a phenomenon with various possible implications for HIV-1 infection outcome, from particle infectivity [121], to release efficiency [122] and activation of immune response [123]. This result is in accordance with the bibliography and supports previous findings. Importantly HIV-1 infection is also associated with NF-κB signaling activation and transposable elements activation [124], which may suggest that HIV-1 and HERVs might be interconnected through a positive-feedback mechanism that involves inflammation induction and NF-κB activation.

5. Conclusions

The intuition of Barbara McClintock led to experimental evidence that introduced the scientific community to the concept of a dynamic genome [125]. Moreover, the components of the dynamic genome have been characterized as controlling elements [126] that may shape organism physiology during evolutionary time, by participating in and forming gene regulatory networks [127]. The current study provides a systemic, post-genomic view on the regulatory footprint of HERV sequences that support the various roles of the dynamic, repetitive part of 9% of our genome.

Supplementary Materials

The following supporting information can be downloaded at:, Table S1: Genomic features of HERV-CREs; Table S2: Genes associated with HERV-CREs; Table S3: Genes with HERV-CREs in their promoter regions. Table S4: Most significant pathways associated with HERV-CREs; Table S5: Total Results following Enrichr Analysis.


This research received no external funding.

Data Availability Statement

The data analyzed are available as links provided in the main text and as supplementary data.


The author would like to thank Stefania Mantziou for the valuable discussions and the continuous support.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Lander, E.S.; Linton, L.M.; Birren, B.; Nusbaum, C.; Zody, M.C.; Baldwin, J.; Devon, K.; Dewar, K.; Doyle, M.; FitzHugh, W.; et al. Initial sequencing and analysis of the human genome. Nature 2001, 409, 860–921, Correction in Nature 2001, 412, 565–566; Erratum in Nature 2001, 7, 720. [Google Scholar] [CrossRef][Green Version]
  2. Venter, J.C.; Adams, M.D.; Myers, E.W.; Li, P.W.; Mural, R.J.; Sutton, G.G.; Smith, H.O.; Yandell, M.; Evans, C.A.; Holt, R.A. The sequence of the human genome. Science 2001, 291, 1304–1351. [Google Scholar] [CrossRef] [PubMed][Green Version]
  3. Kojima, K.K. Human transposable elements in Repbase: Genomic footprints from fish to humans. Mob. DNA 2018, 9, 2. [Google Scholar] [CrossRef] [PubMed][Green Version]
  4. de Koning, A.P.J.; Gu, W.; Castoe, T.A.; Batzer, M.A.; Pollock, D.D. Repetitive Elements May Comprise Over Two-Thirds of the Human Genome. PLoS Genet. 2011, 7, e1002384. [Google Scholar] [CrossRef] [PubMed][Green Version]
  5. Goodier, J.L.; Kazazian, H.H., Jr. Retrotransposons Revisited: The Restraint and Rehabilitation of Parasites. Cell 2008, 135, 23–35. [Google Scholar] [CrossRef][Green Version]
  6. Elbarbary, R.A.; Lucas, B.A.; Maquat, L.E. Retrotransposons as regulators of gene expression. Science 2016, 351, aac7247. [Google Scholar] [CrossRef][Green Version]
  7. Dimitriadou, E.; Noutsopoulos, D.; Markopoulos, G.; Vlaikou, A.-M.; Mantziou, S.; Traeger-Synodinos, J.; Kanavakis, E.; Chrousos, G.P.; Tzavaras, T.; Syrrou, M. Abnormal DLK1/MEG3 imprinting correlates with decreased HERV-K methylation after assisted reproduction and preimplantation genetic diagnosis. Stress 2013, 16, 689–697. [Google Scholar] [CrossRef]
  8. Ladias, P.; Markopoulos, G.; Lazaros, L.; Markoula, S.; Tzavaras, T.; Georgiou, I. Holliday Junctions Are Associated with Transposable Element Sequences in the Human Genome. J. Mol. Biol. 2016, 428, 658–667. [Google Scholar] [CrossRef]
  9. Li, Y.; Zhang, G.; Cui, J. Origin and Deep Evolution of Human Endogenous Retroviruses in Pan-Primates. Viruses 2022, 14, 1370. [Google Scholar] [CrossRef]
  10. Sverdlov, E.D. Retroviruses and Primate Genome Evolution; CRC Press: Boca Raton, FL, USA, 2005. [Google Scholar]
  11. Montesion, M.; Williams, Z.H.; Subramanian, R.P.; Kuperwasser, C.; Coffin, J.M. Promoter expression of HERV-K (HML-2) provirus-derived sequences is related to LTR sequence variation and polymorphic transcription factor binding sites. Retrovirology 2018, 15, 57. [Google Scholar] [CrossRef]
  12. Jia, L.; Liu, M.; Yang, C.; Li, H.; Liu, Y.; Han, J.; Zhai, X.; Wang, X.; Li, T.; Li, J.; et al. Comprehensive identification and characterization of the HERV-K (HML-9) group in the human genome. Retrovirology 2022, 19, 11. [Google Scholar] [CrossRef] [PubMed]
  13. Knössl, M.; Löwer, R.; Löwer, J. Expression of the Human Endogenous Retrovirus HTDV/HERV-K Is Enhanced by Cellular Transcription Factor YY1. J. Virol. 1999, 73, 1254–1261. [Google Scholar] [CrossRef] [PubMed][Green Version]
  14. Fuchs, N.V.; Kraft, M.; Tondera, C.; Hanschmann, K.-M.; Löwer, J.; Löwer, R. Expression of the human endogenous retrovirus (HERV) group HML-2/HERV-K does not depend on canonical promoter elements but is regulated by transcription factors Sp1 and Sp3. J. Virol. 2011, 85, 3436–3448. [Google Scholar] [CrossRef][Green Version]
  15. De Parseval, N.; Alkabbani, H.; Heidmann, T. The long terminal repeats of the HERV-H human endogenous retrovirus contain binding sites for transcriptional regulation by the Myb protein. J. Gen. Virol. 1999, 80, 841–845. [Google Scholar] [CrossRef][Green Version]
  16. Matteucci, C.; Balestrieri, E.; Argaw-Denboba, A.; Sinibaldi-Vallebona, P. Human endogenous retroviruses role in cancer cell stemness. Semin. Cancer Biol. 2018, 53, 17–30. [Google Scholar] [CrossRef] [PubMed]
  17. Mommert, M.; Tabone, O.; Oriol, G.; Cerrato, E.; Guichard, A.; Naville, M.; Fournier, P.; Volff, J.-N.; Pachot, A.; Monneret, G.; et al. LTR-retrotransposon transcriptome modulation in response to endotoxin-induced stress in PBMCs. BMC Genom. 2018, 19, 522. [Google Scholar] [CrossRef] [PubMed][Green Version]
  18. Seifarth, W.; Frank, O.; Zeilfelder, U.; Spiess, B.; Greenwood, A.D.; Hehlmann, R.; Leib-Mösch, C. Comprehensive Analysis of Human Endogenous Retrovirus Transcriptional Activity in Human Tissues with a Retrovirus-Specific Microarray. J. Virol. 2005, 79, 341–352. [Google Scholar] [CrossRef][Green Version]
  19. Georgiou, I.; Noutsopoulos, D.; Dimitriadou, E.; Markopoulos, G.; Apergi, A.; Lazaros, L.; Vaxevanoglou, T.; Pantos, K.; Syrrou, M.; Tzavaras, T. Retrotransposon RNA expression and evidence for retrotransposition events in human oocytes. Hum. Mol. Genet. 2009, 18, 1221–1228. [Google Scholar] [CrossRef][Green Version]
  20. Lazaros, L.; Kitsou, C.; Kostoulas, C.; Bellou, S.; Hatzi, E.; Ladias, P.; Stefos, T.; Markoula, S.; Galani, V.; Vartholomatos, G.; et al. Retrotransposon expression and incorporation of cloned human and mouse retroelements in human spermatozoa. Fertil. Steril. 2017, 107, 821–830. [Google Scholar] [CrossRef][Green Version]
  21. Smallwood, A.; Papageorghiou, A.; Nicolaides, K.; Alley, M.; Jim, A.; Nargund, G.; Ojha, K.; Campbell, S.; Banerjee, S. Temporal Regulation of the Expression of Syncytin (HERV-W), Maternally Imprinted PEG10, and SGCE in Human Placenta1. Biol. Reprod. 2003, 69, 286–293. [Google Scholar] [CrossRef]
  22. Sun, M.-A.; Wolf, G.; Wang, Y.; Senft, A.D.; Ralls, S.; Jin, J.; Dunn-Fletcher, C.E.; Muglia, L.J.; Macfarlan, T.S. Endogenous Retroviruses Drive Lineage-Specific Regulatory Evolution across Primate and Rodent Placentae. Mol. Biol. Evol. 2021, 38, 4992–5004. [Google Scholar] [CrossRef] [PubMed]
  23. Karamitros, T.; Paraskevis, D.; Hatzakis, A.; Psichogiou, M.; Elefsiniotis, I.; Hurst, T.; Geretti, A.-M.; Beloukas, A.; Frater, J.; Klenerman, P.; et al. A contaminant-free assessment of Endogenous Retroviral RNA in human plasma. Sci. Rep. 2016, 6, 33598. [Google Scholar] [CrossRef] [PubMed]
  24. Hurst, T.P.; Magiorkinis, G. Epigenetic Control of Human Endogenous Retrovirus Expression: Focus on Regulation of Long-Terminal Repeats (LTRs). Viruses 2017, 9, 130. [Google Scholar] [CrossRef][Green Version]
  25. Wang, J.; Xie, G.; Singh, M.; Ghanbarian, A.T.; Raskó, T.; Szvetnik, A.; Cai, H.; Besser, D.; Prigione, A.; Fuchs, N.V.; et al. Primate-specific endogenous retrovirus-driven transcription defines naive-like stem cells. Nature 2014, 516, 405–409. [Google Scholar] [CrossRef][Green Version]
  26. Zhang, Y.; Li, T.; Preissl, S.; Amaral, M.L.; Grinstein, J.D.; Farah, E.N.; Destici, E.; Qiu, Y.; Hu, R.; Lee, A.Y.; et al. Transcriptionally active HERV-H retrotransposons demarcate topologically associating domains in human pluripotent stem cells. Nat. Genet. 2019, 51, 1380–1388. [Google Scholar] [CrossRef] [PubMed]
  27. Lu, X.; Sachs, F.; Ramsay, L.; Jacques, P.; Göke, J.; Bourque, G.; Ng, H.-H. The retrovirus HERVH is a long noncoding RNA required for human embryonic stem cell identity. Nat. Struct. Mol. Biol. 2014, 21, 423–425. [Google Scholar] [CrossRef] [PubMed]
  28. Consortium, E.P. An integrated encyclopedia of DNA elements in the human genome. Nature 2012, 489, 57. [Google Scholar] [CrossRef][Green Version]
  29. Markopoulos, G.; Noutsopoulos, D.; Mantziou, S.; Gerogiannis, D.; Thrasyvoulou, S.; Vartholomatos, G.; Kolettas, E.; Tzavaras, T. Genomic analysis of mouse VL30 retrotransposons. Mob. DNA 2016, 7, 10. [Google Scholar] [CrossRef][Green Version]
  30. Mantziou, S.; Markopoulos, G.S. Origins and Function of VL30 lncRNA Packaging in Small Extracellular Vesicles: Implications for Cellular Physiology and Pathology. Biomedicines 2021, 9, 1742. [Google Scholar] [CrossRef]
  31. Vartholomatos, E.; Mantziou, S.; Alexiou, G.A.; Lazari, D.; Sioka, C.; Kyritsis, A.; Markopoulos, G.S. An NF-κB-and Therapy-Related Regulatory Network in Glioma: A Potential Mechanism of Action for Natural Antiglioma Agents. Biomedicines 2022, 10, 935. [Google Scholar] [CrossRef]
  32. Chen, N. Using Repeat Masker to Identify Repetitive Elements in Genomic Sequences. Curr. Protoc. Bioinform. 2004, 5, 4.10.1–4.10.14. [Google Scholar] [CrossRef] [PubMed]
  33. Gerstein, M.B.; Kundaje, A.; Hariharan, M.; Landt, S.G.; Yan, K.-K.; Cheng, C.; Mu, X.J.; Khurana, E.; Rozowsky, J.; Alexander, R.; et al. Architecture of the human regulatory network derived from ENCODE data. Nature 2012, 489, 91–100. [Google Scholar] [CrossRef] [PubMed][Green Version]
  34. Wang, J.; Zhuang, J.; Iyer, S.; Lin, X.; Whitfield, T.W.; Greven, M.C.; Pierce, B.G.; Dong, X.; Kundaje, A.; Cheng, Y.; et al. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Res. 2012, 22, 1798–1812. [Google Scholar] [CrossRef][Green Version]
  35. Wang, J.; Zhuang, J.; Iyer, S.; Lin, X.-Y.; Greven, M.C.; Kim, B.-H.; Moore, J.; Pierce, B.; Dong, X.; Virgil, D.; et al. A Wiki-based database for transcription factor-binding data generated by the ENCODE consortium. Nucleic Acids Res. 2012, 41, D171–D176. [Google Scholar] [CrossRef][Green Version]
  36. Landt, S.G.; Marinov, G.K.; Kundaje, A.; Kheradpour, P.; Pauli, F.; Batzoglou, S.; Bernstein, B.E.; Bickel, P.; Brown, J.B.; Cayting, P. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 2012, 22, 1813–1831. [Google Scholar] [CrossRef][Green Version]
  37. The ENCODE Project Consortium; Moore, J.E.; Purcaro, M.J.; Pratt, H.E.; Epstein, C.B.; Shoresh, N.; Adrian, J.; Kawli, T.; Davis, C.A.; Dobin, A. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 2020, 583, 699–710. [Google Scholar] [CrossRef]
  38. Kent, W.J.; Sugnet, C.W.; Furey, T.S.; Roskin, K.M.; Pringle, T.H.; Zahler, A.M.; Haussler, D. The Human Genome Browser at UCSC. Genome Res. 2002, 12, 996–1006. [Google Scholar] [CrossRef][Green Version]
  39. Fujita, P.A.; Rhead, B.; Zweig, A.S.; Hinrichs, A.S.; Karolchik, D.; Cline, M.S.; Goldman, M.; Barber, G.P.; Clawson, H.; Coelho, A.; et al. The UCSC Genome Browser database: Update 2011. Nucleic Acids Res. 2010, 39, D876–D882. [Google Scholar] [CrossRef][Green Version]
  40. Haeussler, M.; Zweig, A.S.; Tyner, C.; Speir, M.L.; Rosenbloom, K.R.; Raney, B.J.; Lee, C.M.; Lee, B.T.; Hinrichs, A.; Gonzalez, J.N.; et al. The UCSC Genome Browser database: 2019 update. Nucleic Acids Res. 2018, 47, D853–D858. [Google Scholar] [CrossRef][Green Version]
  41. Karolchik, D.; Hinrichs, A.S.; Furey, T.S.; Roskin, K.M.; Sugnet, C.W.; Haussler, D.; Kent, W.J. The UCSC Table Browser data retrieval tool. Nucleic Acids Res. 2004, 32, D493–D496. [Google Scholar] [CrossRef]
  42. Frankish, A.; Diekhans, M.; Jungreis, I.; Lagarde, J.; Loveland, J.E.; Mudge, J.M.; Sisu, C.; Wright, J.C.; Armstrong, J.; Barnes, I. GENCODE 2021. Nucleic Acids Res. 2021, 49, D916–D923. [Google Scholar] [CrossRef] [PubMed]
  43. Huang, W.; Loganantharaj, R.; Schroeder, B.; Fargo, D.; Li, L. Pavis: A tool for p eak a nnotation and vis ualization. Bioinformatics 2013, 29, 3097–3099. [Google Scholar] [CrossRef] [PubMed][Green Version]
  44. McLean, C.Y.; Bristor, D.; Hiller, M.; Clarke, S.L.; Schaar, B.T.; Lowe, C.B.; Wenger, A.M.; Bejerano, G. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 2010, 28, 495–501. [Google Scholar] [CrossRef] [PubMed][Green Version]
  45. Warde-Farley, D.; Donaldson, S.L.; Comes, O.; Zuberi, K.; Badrawi, R.; Chao, P.; Franz, M.; Grouios, C.; Kazi, F.; Lopes, C.T.; et al. The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010, 38, W214–W220. [Google Scholar] [CrossRef][Green Version]
  46. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of Biomolecular Interaction Networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef]
  47. Saito, R.; Smoot, M.E.; Ono, K.; Ruscheinski, J.; Wang, P.-L.; Lotia, S.; Pico, A.R.; Bader, G.D.; Ideker, T. A travel guide to Cytoscape plugins. Nat. Methods 2012, 9, 1069–1076. [Google Scholar] [CrossRef][Green Version]
  48. Su, G.; Morris, J.H.; Demchak, B.; Bader, G.D. Biological network exploration with Cytoscape 3. Curr. Protoc. Bioinform. 2014, 47, 8.13.1–8.13.24. [Google Scholar] [CrossRef][Green Version]
  49. Scardoni, G.; Tosadori, G.; Faizan, M.; Spoto, F.; Fabbri, F.; Laudanna, C. Biological network analysis with CentiScaPe: Centralities and experimental dataset integration. F1000Research 2014, 3, 139. [Google Scholar] [CrossRef][Green Version]
  50. Scardoni, G.; Petterlini, M.; Laudanna, C. Analyzing biological network parameters with CentiScaPe. Bioinformatics 2009, 25, 2857–2859. [Google Scholar] [CrossRef][Green Version]
  51. Fabregat, A.; Jupe, S.; Matthews, L.; Sidiropoulos, K.; Gillespie, M.; Garapati, P.; Haw, R.; Jassal, B.; Korninger, F.; May, B.; et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018, 46, D649–D655. [Google Scholar] [CrossRef]
  52. Chen, E.Y.; Tan, C.M.; Kou, Y.; Duan, Q.; Wang, Z.; Meirelles, G.V.; Clark, N.R.; Ma’Ayan, A. Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinform. 2013, 14, 128. [Google Scholar] [CrossRef] [PubMed][Green Version]
  53. Kuleshov, M.V.; Jones, M.R.; Rouillard, A.D.; Fernandez, N.F.; Duan, Q.; Wang, Z.; Koplev, S.; Jenkins, S.L.; Jagodnik, K.M.; Lachmann, A.; et al. Enrichr: A comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016, 44, W90–W97. [Google Scholar] [CrossRef] [PubMed][Green Version]
  54. Clarke, D.J.; Jeon, M.; Stein, D.J.; Moiseyev, N.; Kropiwnicki, E.; Dai, C.; Xie, Z.; Wojciechowicz, M.L.; Litz, S.; Hom, J.; et al. Appyters: Turning Jupyter Notebooks into data-driven web apps. Gene Expr. Patterns 2021, 2, 100213. [Google Scholar] [CrossRef]
  55. Pratt, H.E.; Andrews, G.R.; Phalke, N.; Huey, J.D.; Purcaro, M.J.; van der Velde, A.; Moore, J.E.; Weng, Z. Factorbook: An updated catalog of transcription factor motifs and candidate regulatory motif sites. Nucleic Acids Res. 2021, 50, D141–D149. [Google Scholar] [CrossRef] [PubMed]
  56. Goodrich, J.A.; Tjian, R. Unexpected roles for core promoter recognition factors in cell-type-specific transcription and gene regulation. Nat. Rev. Genet. 2010, 11, 549–558. [Google Scholar] [CrossRef] [PubMed][Green Version]
  57. Haberle, V.; Stark, A. Eukaryotic core promoters and the functional basis of transcription initiation. Nat. Rev. Mol. Cell Biol. 2018, 19, 621–637. [Google Scholar] [CrossRef]
  58. Grandi, N.; Pisano, M.; Pessiu, E.; Scognamiglio, S.; Tramontano, E. HERV-K(HML7) Integrations in the Human Genome: Comprehensive Characterization and Comparative Analysis in Non-Human Primates. Biology 2021, 10, 439. [Google Scholar] [CrossRef]
  59. Li, W.; Lin, L.; Malhotra, R.; Yang, L.; Acharya, R.; Poss, M. A computational framework to assess genome-wide distribution of polymorphic human endogenous retrovirus-K In human populations. PLoS Comput. Biol. 2019, 15, e1006564. [Google Scholar] [CrossRef][Green Version]
  60. Thomas, J.; Perron, H.; Feschotte, C. Variation in proviral content among human genomes mediated by LTR recombination. Mob. DNA 2018, 9, 1–15. [Google Scholar] [CrossRef][Green Version]
  61. Heidari, N.; Phanstiel, D.H.; He, C.; Grubert, F.; Jahanbani, F.; Kasowski, M.; Zhang, M.Q.; Snyder, M.P. Genome-wide map of regulatory interactions in the human genome. Genome Res. 2014, 24, 1905–1917. [Google Scholar] [CrossRef]
  62. Chua, E.H.Z.; Yasar, S.; Harmston, N. The importance of considering regulatory domains in genome-wide analyses–the nearest gene is often wrong! Biol. Open 2022, 11, bio059091. [Google Scholar] [CrossRef] [PubMed]
  63. Nasser, J.; Bergman, D.T.; Fulco, C.P.; Guckelberger, P.; Doughty, B.R.; Patwardhan, T.A.; Jones, T.R.; Nguyen, T.H.; Ulirsch, J.C.; Lekschas, F.; et al. Genome-wide enhancer maps link risk variants to disease genes. Nature 2021, 593, 238–243. [Google Scholar] [CrossRef] [PubMed]
  64. Lappalainen, T.; Scott, A.J.; Brandt, M.; Hall, I.M. Genomic Analysis in the Age of Human Genome Sequencing. Cell 2019, 177, 70–84. [Google Scholar] [CrossRef][Green Version]
  65. Jung, I.; Schmitt, A.; Diao, Y.; Lee, A.J.; Liu, T.; Yang, D.; Tan, C.; Eom, J.; Chan, M.; Chee, S.; et al. A compendium of promoter-centered long-range chromatin interactions in the human genome. Nat. Genet. 2019, 51, 1442–1449. [Google Scholar] [CrossRef]
  66. Fu, B.; Ma, H.; Liu, D. Endogenous Retroviruses Function as Gene Expression Regulatory Elements During Mammalian Pre-implantation Embryo Development. Int. J. Mol. Sci. 2019, 20, 790. [Google Scholar] [CrossRef][Green Version]
  67. Buttler, C.A.; Chuong, E.B. Emerging roles for endogenous retroviruses in immune epigenetic regulation*. Immunol. Rev. 2021, 305, 165–178. [Google Scholar] [CrossRef]
  68. Jansz, N.; Faulkner, G.J. Endogenous retroviruses in the origins and treatment of cancer. Genome Biol. 2021, 22, 147. [Google Scholar] [CrossRef] [PubMed]
  69. Tokuyama, M.; Kong, Y.; Song, E.; Jayewickreme, T.; Kang, I.; Iwasaki, A. ERVmap analysis reveals genome-wide transcription of human endogenous retro-viruses. Proc. Natl. Acad. Sci. USA 2018, 115, 12565–12572. [Google Scholar] [CrossRef] [PubMed][Green Version]
  70. Chen, J.; Foroozesh, M.; Qin, Z. Transactivation of human endogenous retroviruses by tumor viruses and their functions in virus-associated malignancies. Oncogenesis 2019, 8, 6. [Google Scholar] [CrossRef][Green Version]
  71. Dai, L.; Del Valle, L.; Miley, W.; Whitby, D.; Ochoa, A.C.; Flemington, E.K.; Qin, Z. Transactivation of human endogenous retrovirus K (HERV-K) by KSHV promotes Kaposi’s sarcoma development. Oncogene 2018, 37, 4534–4545. [Google Scholar] [CrossRef]
  72. Vincendeau, M.; Göttesdorfer, I.; Schreml, J.M.H.; Wetie, A.G.N.; Mayer, J.; Greenwood, A.D.; Helfer, M.; Kramer, S.; Seifarth, W.; Hadian, K.; et al. Modulation of human endogenous retrovirus (HERV) transcription during persistent and de novo HIV-1 infection. Retrovirology 2015, 12, 27. [Google Scholar] [CrossRef] [PubMed][Green Version]
  73. van der Kuyl, A.C. HIV infection and HERV expression: A review. Retrovirology 2012, 9, 6. [Google Scholar] [CrossRef] [PubMed][Green Version]
  74. Gonzalez-Hernandez, M.J.; Swanson, M.D.; Contreras-Galindo, R.; Cookinham, S.; King, S.R.; Noel, R.J.; Kaplan, M.H.; Markovitz, D.M. Expression of Human Endogenous Retrovirus Type K (HML-2) Is Activated by the Tat Protein of HIV-1. J. Virol. 2012, 86, 7790–7805. [Google Scholar] [CrossRef][Green Version]
  75. Delaneau, O.; Zazhytska, M.; Borel, C.; Giannuzzi, G.; Rey, G.; Howald, C.; Kumar, S.; Ongen, H.; Popadin, K.; Marbach, D. Chromatin three-dimensional interactions mediate genetic effects on gene ex-pression. Science 2019, 364, eaat8266. [Google Scholar] [CrossRef]
  76. Wang, T.; Medynets, M.; Johnson, K.R.; Doucet-O’Hare, T.T.; DiSanza, B.; Li, W.; Xu, Y.; Bagnell, A.; Tyagi, R.; Sampson, K.; et al. Regulation of stem cell function and neuronal differentiation by HERV-K via mTOR pathway. Proc. Natl. Acad. Sci. USA 2020, 117, 17842–17853. [Google Scholar] [CrossRef] [PubMed]
  77. Tamouza, R.; Meyer, U.; Foiselle, M.; Richard, J.-R.; Wu, C.-L.; Boukouaci, W.; Le Corvoisier, P.; Barrau, C.; Lucas, A.; Perron, H. Identification of inflammatory subgroups of schizophrenia and bipolar disorder patients with HERV-W ENV antigenemia by unsupervised cluster analysis. Transl. Psychiatry 2021, 11, 377. [Google Scholar] [CrossRef] [PubMed]
  78. Johnston, J.B.; Silva, C.; Holden, J.; Warren, K.G.; Clark, A.W.; Power, C. Monocyte activation and differentiation augment human endogenous retrovirus expression: Implications for inflammatory brain diseases. Ann. Neurol. 2001, 50, 434–442. [Google Scholar] [CrossRef] [PubMed]
  79. Ko, E.-J.; Cha, H.-J. The Roles of Human Endogenous Retroviruses (HERVs) in Inflammation. Kosin Med. J. 2021, 36, 69–78. [Google Scholar] [CrossRef]
  80. Mantovani, A.; Dinarello, C.A.; Molgora, M.; Garlanda, C. Interleukin-1 and related cytokines in the regulation of inflam-mation and immunity. Immunity 2019, 50, 778–795. [Google Scholar] [CrossRef][Green Version]
  81. Kany, S.; Vollrath, J.T.; Relja, B. Cytokines in Inflammatory Disease. Int. J. Mol. Sci. 2019, 20, 6008. [Google Scholar] [CrossRef]
  82. Chen, K.; Bao, Z.; Tang, P.; Gong, W.; Yoshimura, T.; Wang, J.M. Chemokines in homeostasis and diseases. Cell. Mol. Immunol. 2018, 15, 324–334. [Google Scholar] [CrossRef] [PubMed]
  83. Eckardt, V.; Miller, M.C.; Blanchet, X.; Duan, R.; Leberzammer, J.; Duchene, J.; Soehnlein, O.; Megens, R.T.; Ludwig, A.; Dregni, A.; et al. Chemokines and galectins form heterodimers to modulate inflammation. EMBO Rep. 2020, 21, e47852. [Google Scholar] [CrossRef] [PubMed]
  84. Pfeffer, L.M. The Role of Nuclear Factor κB in the Interferon Response. J. Interf. Cytokine Res. 2011, 31, 553–559. [Google Scholar] [CrossRef][Green Version]
  85. Pires, B.R.; Silva, R.C.; Ferreira, G.M.; Abdelhay, E. NF-kappaB: Two sides of the same coin. Genes 2018, 9, 24. [Google Scholar] [CrossRef] [PubMed][Green Version]
  86. Sehnert, B.; Burkhardt, H.; Dübel, S.; Voll, R.E. Cell-type targeted NF-kappaB inhibition for the treatment of inflammatory diseases. Cells 2020, 9, 1627. [Google Scholar] [CrossRef] [PubMed]
  87. Schuliga, M. NF-kappaB signaling in chronic inflammatory airway disease. Biomolecules 2015, 5, 1266–1283. [Google Scholar] [CrossRef][Green Version]
  88. Dev, A.; Iyer, S.; Razani, B.; Cheng, G. NF-κB and innate immunity. In NF-Kb Health and Disease; Springer: Berlin/Heidelberg, Germany, 2010; pp. 115–143. [Google Scholar]
  89. Antonaki, A.; Demetriades, C.; Polyzos, A.; Banos, A.; Vatsellas, G.; Lavigne, M.D.; Apostolou, E.; Mantouvalou, E.; Papadopoulou, D.; Mosialos, G. Genomic analysis reveals a novel nuclear factor-κB (NF-κB)-binding site in Alu-repetitive elements. J. Biol. Chem. 2011, 286, 38768–38782. [Google Scholar] [CrossRef][Green Version]
  90. Apostolou, E.; Thanos, D. Virus infection induces NF-κB-dependent interchromosomal associations mediating monoallelic IFN-β gene expression. Cell 2008, 134, 85–96. [Google Scholar] [CrossRef][Green Version]
  91. Markopoulos, G.S.; Roupakia, E.; Marcu, K.B.; Kolettas, E. Epigenetic regulation of inflammatory cytokine-induced epithe-lial-to-mesenchymal cell transition and cancer stem cell generation. Cells 2019, 8, 1143. [Google Scholar] [CrossRef][Green Version]
  92. Markopoulos, G.S.; Roupakia, E.; Tokamani, M.; Chavdoula, E.; Hatziapostolou, M.; Polytarchou, C.; Marcu, K.B.; Papavassiliou, A.G.; Sandaltzopoulos, R.; Kolettas, E. A step-by-step microRNA guide to cancer development and metastasis. Cell. Oncol. 2017, 40, 303–339. [Google Scholar] [CrossRef]
  93. Markopoulos, G.S.; Roupakia, E.; Tokamani, M.; Alabasi, G.; Sandaltzopoulos, R.; Marcu, K.B.; Kolettas, E. Roles of NF-κB signaling in the regulation of miRNAs impacting on inflammation in cancer. Biomedicines 2018, 6, 40. [Google Scholar] [CrossRef] [PubMed][Green Version]
  94. Hirsch, H.A.; Iliopoulos, D.; Joshi, A.; Zhang, Y.; Jaeger, S.A.; Bulyk, M.; Tsichlis, P.N.; Liu, X.S.; Struhl, K. A Transcriptional Signature and Common Gene Networks Link Cancer with Lipid Metabolism and Diverse Human Diseases. Cancer Cell 2010, 17, 348–361. [Google Scholar] [CrossRef][Green Version]
  95. Ditsworth, D.; Zong, W.-X. NF-kappaB: Key mediator of inflammation-associated cancer. Cancer Biol. Ther. 2004, 3, 1214–1216. [Google Scholar] [CrossRef] [PubMed][Green Version]
  96. Salas, A.; Hernandez-Rocha, C.; Duijvestein, M.; Faubion, W.; McGovern, D.; Vermeire, S.; Vetrano, S.; Casteele, N.V. JAK–STAT pathway targeting for the treatment of inflammatory bowel disease. Nat. Rev. Gastroenterol. Hepatol. 2020, 17, 323–337. [Google Scholar] [CrossRef] [PubMed]
  97. Pfitzner, E.; Kliem, S.; Baus, D.; Litterst, M.C. The Role of STATs in Inflammation and Inflammatory Diseases. Curr. Pharm. Des. 2004, 10, 2839–2850. [Google Scholar] [CrossRef] [PubMed]
  98. Stat, C.J.K.; Egf, P. The Jak-STAT pathway in rheumatoid arthritis. J. Rheumatol. 2005, 32, 1650–1653. [Google Scholar]
  99. Gonzalez-Cao, M.; Iduma, P.; Karachaliou, N.; Santarpia, M.; Blanco, J.; Rosell, R. Human endogenous retroviruses and cancer. Cancer Biol. Med. 2016, 13, 483. [Google Scholar]
  100. Kitsou, K.; Iliopoulou, M.; Spoulou, V.; Lagiou, P.; Magiorkinis, G. Viral Causality of Human Cancer and Potential Roles of Human Endogenous Retroviruses in the Multi-Omics Era: An Evolutionary Epidemiology Review. Front. Oncol. 2021, 11. [Google Scholar] [CrossRef]
  101. Agoni, L.; Guha, C.; Lenz, J. Detection of Human Endogenous Retrovirus K (HERV-K) Transcripts in Human Prostate Cancer Cell Lines. Front. Oncol. 2013, 3, 180. [Google Scholar] [CrossRef][Green Version]
  102. Bonaventura, P.; Alcazer, V.; Mutez, V.; Tonon, L.; Martin, J.; Chuvin, N.; Michel, E.; Boulos, R.E.; Estornes, Y.; Valladeau-Guilemond, J.; et al. Identification of shared tumor epitopes from endogenous retroviruses inducing high-avidity cytotoxic T cells for cancer immunotherapy. Sci. Adv. 2022, 8, eabj3671. [Google Scholar] [CrossRef]
  103. Wang-Johanning, F.; Frost, A.R.; Jian, B.; Epp, L.; Lu, D.W.; Johanning, G.L. Quantitation of HERV-K env gene expression and splicing in human breast cancer. Oncogene 2003, 22, 1528–1535. [Google Scholar] [CrossRef][Green Version]
  104. Golan, M.; Hizi, A.; Resau, J.H.; Yaal-Hahoshen, N.; Reichman, H.; Keydar, I.; Tsarfaty, I. Human Endogenous Retrovirus (HERV-K) Reverse Transcriptase as a Breast Cancer Prognostic Marker. Neoplasia 2008, 10, 521–533, IN1–IN2. [Google Scholar] [CrossRef] [PubMed]
  105. Zhou, F.; Li, M.; Wei, Y.; Lin, K.; Lu, Y.; Shen, J.; Johanning, G.L.; Wang-Johanning, F. Activation of HERV-K Env protein is essential for tumorigenesis and metastasis of breast cancer cells. Oncotarget 2016, 7, 84093–84117. [Google Scholar] [CrossRef] [PubMed][Green Version]
  106. Ma, W.; Hong, Z.; Liu, H.; Chen, X.; Ding, L.; Liu, Z.; Zhou, F.; Yuan, Y. Human Endogenous Retroviruses-K (HML-2) Expression Is Correlated with Prognosis and Progress of Hepatocellular Carcinoma. BioMed Res. Int. 2016, 2016, 8201642. [Google Scholar] [CrossRef] [PubMed]
  107. Wang-Johanning, F.; Liu, J.; Rycaj, K.; Huang, M.; Tsai, K.; Rosen, D.G.; Chen, D.-T.; Lu, D.W.; Barnhart, K.F.; Johanning, G.L. Expression of multiple human endogenous retrovirus surface envelope proteins in ovarian cancer. Int. J. Cancer 2006, 120, 81–90. [Google Scholar] [CrossRef]
  108. Golkaram, M.; Salmans, M.L.; Kaplan, S.; Vijayaraghavan, R.; Martins, M.; Khan, N.; Garbutt, C.; Wise, A.; Yao, J.; Casimiro, S.; et al. HERVs establish a distinct molecular subtype in stage II/III colorectal cancer with poor outcome. npj Genom. Med. 2021, 6, 13. [Google Scholar] [CrossRef]
  109. Ko, E.-J.; Ock, M.-S.; Choi, Y.-H.; Iovanna, J.; Mun, S.; Han, K.; Kim, H.-S.; Cha, H.-J. Human Endogenous Retrovirus (HERV)-K env Gene Knockout Affects Tumorigenic Characteristics of nupr1 Gene in DLD-1 Colorectal Cancer Cells. Int. J. Mol. Sci. 2021, 22, 3941. [Google Scholar] [CrossRef]
  110. Giebler, M.; Staege, M.S.; Blauschmidt, S.; Ohm, L.I.; Kraus, M.; Würl, P.; Taubert, H.; Greither, T. Elevated HERV-K Expression in Soft Tissue Sarcoma Is Associated with Worsened Relapse-Free Survival. Front. Microbiol. 2018, 9, 211. [Google Scholar] [CrossRef]
  111. Alcazer, V.; Bonaventura, P.; Tonon, L.; Michel, E.; Mutez, V.; Fabres, C.; Chuvin, N.; Boulos, R.; Estornes, Y.; Maguer-Satta, V.; et al. HERVs characterize normal and leukemia stem cells and represent a source of shared epitopes for cancer immunotherapy. Am. J. Hematol. 2022, 97, 1200–1214. [Google Scholar] [CrossRef]
  112. Rivas, S.R.; Valdez, M.J.M.; Govindarajan, V.; Seetharam, D.; Doucet-O’Hare, T.T.; Heiss, J.D.; Shah, A.H. The Role of HERV-K in Cancer Stemness. Viruses 2022, 14, 2019. [Google Scholar] [CrossRef]
  113. Larouche, J.-D.; Trofimov, A.; Hesnard, L.; Ehx, G.; Zhao, Q.; Vincent, K.; Durette, C.; Gendron, P.; Laverdure, J.-P.; Bonneil, É.; et al. Widespread and tissue-specific expression of endogenous retroelements in human somatic tissues. Genome Med. 2020, 12, 40. [Google Scholar] [CrossRef] [PubMed]
  114. Mantziou, S.; Markopoulos, G.; Thrasyvoulou, S.; Noutsopoulos, D.; Gkartziou, F.; Artholomatos, G.; Tzavaras, T. Tinzaparin inhibits VL30 retrotransposition induced by oxidative stress and/or VEGF in HC11 mouse progenitor mammary cells: Association between inhibition of cancer stem cell proliferation and mammosphere disaggregation. Oncol. Rep. 2021, 46, 241. [Google Scholar] [CrossRef] [PubMed]
  115. Noutsopoulos, D.; Markopoulos, G.; Vartholomatos, G.; Kolettas, E.; Kolaitis, N.; Tzavaras, T. VL30 retrotransposition signals activation of a caspa-se-independent and p53-dependent death pathway associated with mitochondrial and lysosomal damage. Cell Res. 2010, 20, 553–562. [Google Scholar] [CrossRef] [PubMed]
  116. Noutsopoulos, D.; Markopoulos, G.; Koliou, M.; Dova, L.; Vartholomatos, G.; Kolettas, E.; Tzavaras, T. Vanadium Induces VL30 Retrotransposition at an Unusually High Level: A Possible Carcinogenesis Mechanism. J. Mol. Biol. 2007, 374, 80–90. [Google Scholar] [CrossRef]
  117. Markopoulos, G.; Noutsopoulos, D.; Mantziou, S.; Vartholomatos, G.; Monokrousos, N.; Angelidis, C.; Tzavaras, T. Arsenic Induces VL30 Retrotransposition: The Involvement of Oxidative Stress and Heat-Shock Protein 70. Toxicol. Sci. 2013, 134, 312–322. [Google Scholar] [CrossRef][Green Version]
  118. Thrasyvoulou, S.; Vartholomatos, G.; Markopoulos, G.; Noutsopoulos, D.; Mantziou, S.; Gkartziou, F.; Papageorgis, P.; Charchanti, A.; Kouklis, P.; Constantinou, A.I.; et al. VL30 retrotransposition is associated with induced EMT, CSC generation and tumorigenesis in HC11 mouse mammary stem-like epithelial cells. Oncol. Rep. 2020, 44, 126–138. [Google Scholar] [CrossRef]
  119. Nellåker, C.; Yao, Y.; Jones-Brando, L.; Mallet, F.; Yolken, R.H.; Karlsson, H. Transactivation of elements in the human endogenous retrovirus W family by viral infection. Retrovirology 2006, 3, 44. [Google Scholar] [CrossRef]
  120. Badarinarayan, S.S.; Shcherbakova, I.; Langer, S.; Koepke, L.; Preising, A.; Hotter, D.; Kirchhoff, F.; Sparrer, K.M.J.; Schotta, G.; Sauter, D. HIV-1 infection activates endogenous retroviral promoters regulating antiviral gene expression. Nucleic Acids Res. 2020, 48, 10890–10908. [Google Scholar] [CrossRef]
  121. Monde, K.; Terasawa, H.; Nakano, Y.; Soheilian, F.; Nagashima, K.; Maeda, Y.; Ono, A. Molecular mechanisms by which HERV-K Gag interferes with HIV-1 Gag assembly and particle infectivity. Retrovirology 2017, 14, 27. [Google Scholar] [CrossRef][Green Version]
  122. Monde, K.; Contreras-Galindo, R.; Kaplan, M.H.; Markovitz, D.M.; Ono, A. Human Endogenous Retrovirus K Gag Coassembles with HIV-1 Gag and Reduces the Release Efficiency and Infectivity of HIV-1. J. Virol. 2012, 86, 11194–11208. [Google Scholar] [CrossRef][Green Version]
  123. Hurst, T.P.; Magiorkinis, G. Activat ion of the innate immune response by endogenous retroviruses. J. Gen. Virol. 2015, 96, 1207–1218. [Google Scholar] [CrossRef] [PubMed]
  124. Curty, G.; Iniguez, L.P.; Soares, M.A.; Nixon, D.F.; de Mulder Rougvie, M. Off-Target Effect of Activation of NF-κB by HIV Latency Reversal Agents on Transposable Elements Expression. Viruses 2022, 14, 1571. [Google Scholar] [CrossRef] [PubMed]
  125. McClintock, B. The origin and behavior of mutable loci in maize. Proc. Natl. Acad. Sci. USA 1950, 36, 344–355. [Google Scholar] [CrossRef][Green Version]
  126. McClintock, B. Controlling elements and the gene. Proc. Cold Spring Harb. Symp. Quant. Biol. 1956, 21, 197–216. [Google Scholar] [CrossRef]
  127. Feschotte, C. Transposable elements and the evolution of regulatory networks. Nat. Rev. Genet. 2008, 9, 397–405. [Google Scholar] [CrossRef]
Figure 1. Study overview.
Figure 1. Study overview.
Diseases 10 00098 g001
Figure 2. Genomic distribution of HERV-CREs. (A) Genomic location distribution in relation to human genes, following PAVIS analysis. The relative abundance of sites upstream, in 5′ UTR, Exons, introns, 3′ UTR, downstream and in intergenic regions is shown in distinct color bars. (B) Genomic location distribution in relation to Transcription Start Sites (TSS) of human genes, following GREAT analysis. The number of HERV-CREs in different distances to TSS of mRNA genes is shown. The relative distance to TSS is divided into 0–5, 5–50, 50–500 and >500 kb.
Figure 2. Genomic distribution of HERV-CREs. (A) Genomic location distribution in relation to human genes, following PAVIS analysis. The relative abundance of sites upstream, in 5′ UTR, Exons, introns, 3′ UTR, downstream and in intergenic regions is shown in distinct color bars. (B) Genomic location distribution in relation to Transcription Start Sites (TSS) of human genes, following GREAT analysis. The number of HERV-CREs in different distances to TSS of mRNA genes is shown. The relative distance to TSS is divided into 0–5, 5–50, 50–500 and >500 kb.
Diseases 10 00098 g002
Figure 3. Regulatory network of total genes with HERV-associated regulatory elements in their promoters. Central genes, that contain a significant degree of interactions are denoted in yellow color.
Figure 3. Regulatory network of total genes with HERV-associated regulatory elements in their promoters. Central genes, that contain a significant degree of interactions are denoted in yellow color.
Diseases 10 00098 g003
Figure 4. Network analysis of central HERV-PLS CREs genes. The final network includes 90 input genes (indicated with gray stripes) and top 20 related genes, based on GENEMania analysis. The presented, final network, contains 110 genes and 3117 total interactions. Types of interaction are presented as lines with different colors while functions are presented as circles with different colors, representing individual genes/gene products (see the color-coding legend below the network for details).
Figure 4. Network analysis of central HERV-PLS CREs genes. The final network includes 90 input genes (indicated with gray stripes) and top 20 related genes, based on GENEMania analysis. The presented, final network, contains 110 genes and 3117 total interactions. Types of interaction are presented as lines with different colors while functions are presented as circles with different colors, representing individual genes/gene products (see the color-coding legend below the network for details).
Diseases 10 00098 g004
Figure 5. Enriched gene datasets containing HERV-PLS CREs. The dataset of 110 genes, based on GENEMania analysis were used as an input in the Enrichr meta-analysis tool, for enrichment in gene databases. Representative results from KEGG2021 Pathway Human, KEGG2019 Pathway Mouse, Clinvar2019 and MirTarbase2017 are presented. The results were visualized in Appyter tool. * denotes the level of statistical significance.
Figure 5. Enriched gene datasets containing HERV-PLS CREs. The dataset of 110 genes, based on GENEMania analysis were used as an input in the Enrichr meta-analysis tool, for enrichment in gene databases. Representative results from KEGG2021 Pathway Human, KEGG2019 Pathway Mouse, Clinvar2019 and MirTarbase2017 are presented. The results were visualized in Appyter tool. * denotes the level of statistical significance.
Diseases 10 00098 g005
Figure 6. Top enriched HERV-associated genes in cellular pathways, according to MSigDB Hallmark 2020 database. Following Enrichr analysis, the top10 enriched pathways in MSigDB Hallmark 2020 database that contain HERV-associated genes were further analyzed for the existence of common genes. The top20 genes (left panel) were plotted, based on their occurrence in the respective top10 related pathways (right panel).
Figure 6. Top enriched HERV-associated genes in cellular pathways, according to MSigDB Hallmark 2020 database. Following Enrichr analysis, the top10 enriched pathways in MSigDB Hallmark 2020 database that contain HERV-associated genes were further analyzed for the existence of common genes. The top20 genes (left panel) were plotted, based on their occurrence in the respective top10 related pathways (right panel).
Diseases 10 00098 g006
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Markopoulos, G.S. A Systems Biology Approach on the Regulatory Footprint of Human Endogenous Retroviruses (HERVs). Diseases 2022, 10, 98.

AMA Style

Markopoulos GS. A Systems Biology Approach on the Regulatory Footprint of Human Endogenous Retroviruses (HERVs). Diseases. 2022; 10(4):98.

Chicago/Turabian Style

Markopoulos, Georgios S. 2022. "A Systems Biology Approach on the Regulatory Footprint of Human Endogenous Retroviruses (HERVs)" Diseases 10, no. 4: 98.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop