Mos1 transposase open reading frame (ORF) which functions in vertebrate and arthropod cells. Functional silencers are also found at similar locations within three other animal mariner elements, i.e. IS630-Tc1-mariner (ITm) DD34D elements, Himar1, Hsmar1 and Mcmar1. These silencers are able to impact eukaryotic promoters monitoring strong, moderate or low expression as well as those of mariner elements located upstream of the transposase ORF. We report that the silencing involves at least two transcription factors (TFs) that are conserved within animal species, NFAT-5 and Alx1. These cooperatively act with YY1 to trigger the silencing activity. Four other housekeeping transcription factors (TFs), neuron restrictive silencer factor (NRSF), GAGA factor (GAF) and GTGT factor (GTF), were also found to have binding sites within mariner silencers but their impact in modulating the silencer activity remains to be further specified. Interestingly, an NRSF binding site was found to overlap a 30 bp motif coding a highly conserved PHxxYSPDLAPxD peptide in mariner transposases. We also present experimental evidence that silencing is mainly achieved by co-opting the host Polycomb Repressive Complex 2 pathway. However, we observe that when PRC2 is impaired another host silencing pathway potentially takes over to maintain weak silencer activity. Mariner silencers harbour features of Polycomb Response Elements, which are probably a way for mariner elements to self-repress their transcription and mobility in somatic and germinal cells when the required TFs are expressed. At the evolutionary scale, mariner elements, through their exaptation, might have been a source of silencers playing a role in the chromatin configuration in eukaryotic genomes.Transposable elements are driving forces for establishing genetic innovations such as transcriptional regulatory networks in eukaryotic genomes. Here, we describe a silencer situated in the last 300 bp of the  mariner transposons can self-regulate their activity by using a silencer element located in their DNA sequence. The silencer element interferes with host housekeeping protein transcription factors involved in the polycomb silencing pathways. As the regulation of chromatin configuration by polycomb is an important regulator of animal development, our findings open the possibility that mariner silencers might have been exapted during animal evolution to participate in certain regulation pathways of their hosts. Since some of the TFs involved in mariner silencer activity play a role at different stages of nervous system development and neuron differentiation, it might be possible that mariner transposons can be active during some steps of cell differentiation. Interestingly, mariner transposons (i.e. IS630-Tc1-mariner (ITm) DD34D transposons) have so far only been found in genomes of animals having a nervous system.Transposons are mobile DNA sequences that have long co-evolved with the genome of their hosts. Consequently, they are involved in the generation of mutations, as well as the creation of genes and regulatory networks. Controlling the transposon activity, and consequently its negative effects on both the host soma and germ line, is a challenge for the survival of both the host and the transposon. To silence transposons, hosts often use defence mechanisms involving DNA methylation and RNA interference pathways. Here we show that  In multicellular eukaryotes TE insertions must occur within the germinal lineage or during early development in order to be transmitted to the following generations. This leads to the suggestion that transposition into somatic cells had no value for the TEs or their host. However, in the early 1980’s evidence began to accumulate showing that somatic TE activity (i.e. single excision or excision followed by re-insertion) occurred at high frequency in animal taxa. This was first shown for a DNA transposon, Tc1 in the worm Caenorhabditis elegans  .Mos1, ii) the optimal activity of the silencer element was position-dependent and had an effect only when located downstream of the marker. The first explanation was supported by observations based on the [NeoR]Δ6 and [NeoR]Δ7 constructs which gave results similar to those obtained with the Δ1[NeoR]Δ2, indicating that the silencer was located within the 3’ half of the MOS1 open reading frame (ORF) corresponding to the Δ7 DNA segment  or negative orientations  than the GFP control  the silencer element was not in the non-coding terminal regions of  segment . The rolntations . Only th control . These dMOS1 segment contains a real silencer element, we used a transient luciferase expression assay that was previously validated to characterize silencer elements [NeoR]Δ7+ and HS2_P_Luc_Δ7+ constructs are of the same order of magnitude with respect to controls, [NeoR] MOS1 segm] 14.3x;  and HS2_] 14.3x; , respecty 5-fold . It is iy 5-fold  and wereMos1 silencer element was tested using [NeoR]Δ7+ and [NeoR]Δ7-, our stable expression assay and two other cellular lineages originating from distantly related species: Speedy cells , contained the 3’ half plus the NRSF binding site overlapping the DNA motif encoding the PHxxYSPDLAPxD peptide (i.e. two YY1 binding sites plus one NRSF binding site). The third, Δ8-MOS1-[116–311] was similar to the second with the exception that its NRSF binding site was removed. The fourth, Δ8-MOS1-[66–311]-ΔNRSF, was similar to Δ8-MOS1-[47–311]-ΔNRSF but its 19 residues on the 5’ end were deleted.In addition to the variant Δ8-ere made . The firMOS1-[1–152], Δ8-MOS1-[86–311] and Δ8-MOS1-[116–311], the luciferase expression is higher than the P_Luc control but, significantly, it was 2.5 to 3.5-fold lower than that of HS2_P_Luc . Our results revealed that there were two NFAT-5 and one Alx1 binding sites in the 50 bp MOS1 segment  ) or the 3’ half of the Mos1 or Hsmar1 silencers (HS2_P_Luc_Δ8-MOS1-[116–311] and HS2_P_Luc_Δ8-HSMAR1-[115–310]) were not affected by the DZNep treatment. This suggested that the weak silencer effect resulting from the presence of these DNA segments might result from another silencing mechanism and is detected only when PRC2 is disrupted. Such a duality between silencing pathways was previously described for ITm TEs contained in the genome of murine ES cells. Indeed, these TEs can switch from heterochromatinization mediated by the HP1 (Heterochromatic Protein 1) dependent pathway to a PRC2-dependent silencing when the Histone-lysine N-methyltransferase Su(var)39/HP1 is disrupted  . Nevertheless, it should be noted that in the original manuscript describing the discovery of mariner in D. mauritiana  marker cassette corresponding to a neomycin resistance gene coding a neomycin phosphotransferase 2 was cloned between the EcoRI and BamHI sites of pBS SK+. This gene was flanked by an early SV40 promoter (a moderate promoter) and an SV40 terminator except for the plasmids used in Sf21 cells, where the SV40 promoter was replaced by the immediate early protein 1 promoter . Each assayed DNA segment was cloned upstream (EcoRI site) or downstream (NotI site) of the marker in positive (+) or negative (-) orientation. DNA spacers of 1.2 kbp or 2.7 kbp were cloned between the 3’ end of the marker and the 5’end of the assayed DNA segment at the XbaI site as described . Two days after transfection, 1/3 of the transfected cells were evaluated for luciferase activity with the Luciferase Assay System Kit (Promega). The remaining 2/3 of the cells were transferred in 100 mm Petri dishes followed by G418 sulfate selection  for 15 days. Cells were then fixed and stained with 70% EtOH-0.5% methylene blue for 3 h. Only colonies with a diameter > 0.25 mm were counted.Cells were co-transfected with approximately 150 ng of a two plasmids mix using jetPEIMOS1-ΔNRSF, Δ8-MOS1-mutNRSF, Δ8-HSMAR1-ΔNRSF, and Δ8-HSMAR1-mutNRSF were synthesized by ATG:Biosynthetics. To use the plasmids containing promoter pMos1 or pHsmar1 in transient luciferase expression assay, the NcoI-BamHI DNA fragment containing the luciferase ORF and an SV40 late polyadenylation signal was purified from the P_Luc plasmid, then cloned into each of both plasmids between NcoI and BamHI sites. In pMos- and pHsmar1-Luc plasmids, the BamHI site at the 3’ end of the luciferase cassette was used to clone the DNA fragment to assay. For the transient luciferase expression assay in HeLa and H4 cells, 6 x 104 cells were seeded onto a 24-well plate one day prior to transfection. Transfection was performed using jetPEI, according to the manufacturer’s instructions, using 400 ng of test DNA and 50 ng of pRL-Tk Renilla. For DT40 cells, 5 x 105 cells were seeded onto a 24-well plate one day prior to transfection. jetPEI was also used to transfect about 400 ng of test DNA and 50 ng of pRL-Tk Renilla. For Co115, 4 x 105 cells were seeded onto a 24-well plate one day prior transfection. For each test plasmid, its amount (400 ng) was fitted to its size with respect to that of the smallest plasmid used as a control in each experiment, P_Luc. Transfection was performed with ICAFectin441 DNA transfection reagent, according to the manufacturer’s instructions (In Cell Art), using 400 ng of test DNA and 400 ng of pRL-Tk Renilla. Luciferase expression was measured in a 96-well plate format with detection of fluorescence using the Dual-Glo Luciferase Assay System (Promega). Measurements were recorded on a Berthold plate-reader luminometer. Similar assays were used to investigate whether PRC2 was involved in the silencing effect observed with our constructs. However, a PRC2 inhibition was achieved by adding DZNEp  in the cell culture medium from the seeding until measuring Firefly and Renilla luciferase activities.Plasmid constructs are presented in NFAT-5 whereas the RNA-seq analyses done on another HeLa cell batch led to the opposite conclusion.The expression profile of NRSF, YY1, TARBP2, Alx1, and NFAT-5 in HeLa, Co115 and H4 cells was determined by Western-blot analysis using commercial antibodies for NRSF , YY1 , TARBP2 , Alx1  and NFAT-5 . For DT40 and HeLa cells, expression profile was determined by RNA-seq analysis using data available in databases, GEO datasets SRX286375 and SRX083286, respectively. During the analysis, we observed only one discrepancy between the RNA-seq data and the Western-blot analyses. Indeed, our HeLa cells were found to express 4 cells were seeded onto a 24-well plate one-day prior transfection and then transfected with jetPEI, according to the manufacturer’s instructions using 0.5 μg of plasmid DNA. Cells recovered from the culture 24 h post-transfection were washed three times with 1X PBS. The cell pellet was finally suspended in 400 μL 1X PBS-2% paraformaldehyde (w/v), and stored at 4°C. The analyses were performed using a flow cytometer FACSORT and the Cell Quest program (Beckton Dickinson). A total of 20 000 cells were acquired for each sample. Dead cells and debris were excluded from the analysis based on forward angle and side scatter light gating. Analysis gates were determined from the green fluorescence intensity using transfection controls done with or without plasmids expressing GFP.8 x 10NRSF, a human NRSF fused at its N-terminal end with a FLAG antigen  cassette was separated at its 3’ end by a 3 kbp DNA fragment from a Δ7-MOS1 segment (in positive or negative orientation). The distance effect was analysed by transfecting these linear vectors into HeLa cells and screening for neomycin resistant colonies after 2 weeks of G418 selection. Each histogram bar corresponds to the median value obtained from three experiments performed in triplicate. Bars corresponded to quartiles 1 and 3. * indicates significant differences (p<0.05) compared to the [NeoR] control. Results indicate that a Δ7-MOS1 segment located 3 kbp downstream of the NeoR marker gene is able  to negatively interfere with its expression only when it is located in a positive orientation. Overall, these results support the conclusion that the Δ7-MOS1 segment has its optimal silencer effect when it is located downstream of the maker gene in a positive orientation. For these experiments the linear vectors were prepared as follows: first, pBS plasmids containing the [NeoR], [NeoR]Δ7+ or [NeoR]Δ7- were linearized by enzymatic cleavage using EcoRI. The linear vectors were purified by electrophoretic separation on agarose gels, eluted with a QIAquick PCR purification kit (Qiagen), and quantified with a BioSpec-nano (Shimadzu Biotech).We assayed the impact of plasmid configuration using linear vectors in which the [(DOCX)Click here for additional data file.S3 FigHsmar1 [NcoI, BclI, SalI, BamHI, EcoRI, and BglII. The NcoI and BamHI sites of this MCS were used to clone an NcoI-BamHI DNA fragment (1924 bp) containing an ORF coding the firefly luciferase, which was purified from a pGL3 plasmid (P_Luc). pMos1 and pHsmar1 DNA fragments were synthesized by ATG:biosynthetics GMBh  and each cloned in a pUC19 plasmid.The design of both promoter sequences took into account the definition previously proposed for Hsmar1 . The 5’ (DOCX)Click here for additional data file.S4 Figa) Location of the Δ81 to Δ85 MOS1 DNA segments within the sequence of the Δ8-MOS1 segment. Δ8, black; Δ81, red; Δ82, blue; Δ83, green; Δ84, purple; Δ85, orange. The region absent in the short version of Δ81 (short Δ81 probe in b) Comparison of effects of Δ7-MOS1 and Δ8-MOS1 on the expression of the Firefly and the Renilla luciferase marker genes using transient expression assays in HeLa cells. The assays were performed with Δ7 and Δ8 segments cloned in + orientation. Each histogram bar corresponds to the median value obtained from three experiments done in triplicate. Bars corresponded to quartiles 1 and 3. The median ratios RLU from Firefly/RLU from Renilla were calculated as indicated in Firefly/RLU from Renilla” were above 1 (i.e. where no strong silencer effect is observed) is coloured in grey. No significant effect was found between both plasmid constructs (p<0.05).((DOCX)Click here for additional data file.S5 Figmariner segments; black+grey: Δ7 mariner segments. Transcription factor binding sites frequently found within PREs in drosophila are shown in blue for YY1, in green for Zeste, in turquoise for the GAGA factor and in pink for the GTGT factors. Arrows above the nucleic acid sequences indicate the orientation of each motif. NRSF binding sites are highlighted in red and typed in white. With respect to NRSF binding sites that are conserved in position in all MLEs, the other motifs are arranged in different configurations, ordering and spacing in each element. This indicated that these motifs would not have been conserved in orthologous positions across MLEs during their evolution. Putative NFAT-5 binding sites are highlighted in boxes.Black: Δ8 (DOCX)Click here for additional data file.S6 Figmariner elements using the MEME facilities at http://meme.sdsc.edu/meme/cgi-bin/meme.cgi. A single conserved motif of 50 nucleotides was found and was located in the region encoding one of the two highly conserved peptide motifs in the mariner transposase, PHxxYSPDLAPxD [mariner elements are shown on the right side of the figure. Their names, accession numbers and host species for each element are: Tvmar1, AY282463 Trichomonas vaginalis; Ahmar1, AB056896 Adoxophyes honmai; Armar1, AB056894 Ascogaster reticulatus; Cpmar1, U11641 Chrysoperla plorabunda; Mpmar1, U11649 Mantispa pulchella; Damar1, U11656 Drosophila ananassae; Himar1, U11646 Haematobia irritans; Hsmar2, U49974 Homo sapiens; Bytmar1, AJ507226 Bythogrea thermydron; Acmar1, AB081476 Apis cerena; Ammar1, U19902 for Apis mellifera; Ccmar1, U40493 Ceratitis capitata; Demar1, U08094 Drosophila erecta; Famar1, AY226507 Forficula auricularia; Gpmar1, U18308 Glossina palpalis; Aamar1, AB006464 Attacus atlas; Dtmar1, X79719 Dugesia tigrina; Funmar1, AB055188 Fungia sp. Kusabiraishi; Hbmar1, U04455 Heterorhabditis bacteriophaga; Hcmar1, M63844 Hyalophora cecropia; Hsmar1, U52077 Homo sapiens; Cemar1, M98552 Caenorhabditis elegans; Cemar2, X77804 C. elegans; Mcmar1, H20772 Meloidogyne chitwoodi; Bcmar1, AF349133 Bactrocera tryoni; Botmar1, consensus sequence  Bombus terrestris; Dsecmar1, AF035569 Drosophila sechellia; Madmar1, U24436 Mayetiola destructor; Mbmar1, AF465247 Mamestra brassicae; Mlmar1, AC182003 (element from 70120 to 71051) Myotis lucifugus; Dmmar1 = Mos1, X78906 Drosophila mauritiana; Momar1, U12279 Metaseuilius occidentalis; Mudmar1, AF373028 Musca domestica; Sinvmar1, AF518173 Solenopsis invicta. Depending on the subfamily to which they belong, the element names are typed in brown (irritans), green (capitata/mellifera), blue (cecropia), purple (briggsae/elegans) and red (mauritiana). Tvmar1 is the only member of what might be the sixth mariner subfamily. The PHPPYSPDLAPXD motif is given above the sequence alignment. Consensus of NRSE [charlatan binding element) are indicated below the sequence alignment. In the consensus, the most conserved positions are shown in upper case and the variable positions in lower case. In the CBE consensus, IUPAC abbreviations are used to indicate the following: S = C or G; K = G or T; B = C, T or G; H = A, T or C; M = A or C; V = A, C or G and N = A, C, G or T. Taking into account the sequence degeneracy of the CBE motif, the positions that are conserved in the binding motif are highlighted in grey.Conserved motifs were searched within the nucleic acid sequences corresponding to the Δ7 DNA segment of 34 (DOCX)Click here for additional data file.S7 Fig(a) The RE1 probe corresponds to the RE1 binding site located upstream of the vesicular acethylcholine (VAChT) transporter and choline acethyltransferase (ChAT) genes in the human cholinergic gene locus. A 120 bp DNA segment was amplified by PCR from the pKL-7 plasmid [(b) Set-up of the EMSA for chn. Lane 1 corresponds to a probe control. Lanes 2 to 4 were obtained with a nuclear extract (NE) containing chn. Because chn was not tagged with a peptide and we did not have anti-chn antibodies, the specificity of the shifted complex observed in lane 2 was verified in lanes 3 and 4 by observing binding with a specific DNA binding competitor (CBE-1: 5’-GGCCGTTCAGCACCACCGCCATTGGTCGCGC-3’ [(c) Set-up of the EMSA for HsNRSF and fuguNRSF. Lanes 1 and 10 correspond to probe controls. Lanes 2 through 5 and 11 through 14 are negative controls obtained with an NE prepared from HeLa cells transfected with an empty expression plasmid: pCS2+. Lanes 6 through 9 were obtained with an NE containing HsNRSF fused to a FLAG tag. The specificity of the shifted complex observed in lane 6 was verified in lane 8 in which the anti-FLAG monoclonal antibody (Anti-FLAG MAb) creates a super-shifted complex (red stars). Lanes 15 to 18 were obtained with an NE containing fuguNRSF fused to a myc tag. The specificity of the shifted complex observed in lane 15 was verified in lane 16 in which the anti-myc monoclonal antibody (Anti-myc MAb) creates a super-shifted complex. Lanes 7, 9, 17 and 18 were negative controls that support the conclusion that the super-shifted complexes observed in lanes 8 and 16 were not due to a non-specific effect of the MAb. Complexes were separated on a 6% PAGE in 0.25X TBE gel. Together, these results support that the three NRSF systems can be used to detect NRSF binding sites with EMSA. plasmid . PrimersCGCGC-3’ ) and a nCGCGC-3’ –38. (c) (DOCX)Click here for additional data file.S8 FigMOS1 (a) and Δ8-HSMAR1 (b).Location of the variant segments within the sequences of the Δ8- Names of each variant are indicated at the 3’ end of its sequence. Dashes indicated positions that are present in each fragment. Conserved DNA binding motifs described in c) Expression of the Firefly and the Renilla luciferase marker genes using transient expression assays in HeLa cells. The assays were performed with Δ8-HSMAR1 and five variants cloned in + orientation. Each bar corresponds to the median value obtained from three experiments done in triplicate. Bars corresponded to quartiles 1 and 3. The median ratios RLU from Firefly/RLU from Renilla were calculated as indicated in Firefly/RLU from Renilla” were above 1 (i.e. where no strong silencer effect is observed) is coloured in grey. * indicates a significant difference (p<0.05) with the P_Luc controls. ** indicates a significant difference (p<0.05) with HS2_P_Luc_Δ8-HSMAR1.(DOCX)Click here for additional data file.S9 FigLanes 1 and 4 correspond to probe controls. Lanes 2 and 5 show shifted complexes with NE containing fuguNRSF. The grey arrow locates a complex resulting from the binding of YY1, as shown in (DOCX)Click here for additional data file.S10 FigFirefly luciferase gene from 4 different HS2_P_Luc plasmid constructs containing Δ8-HSMAR1 variants cloned downstream of the marker gene. Each histogram bar corresponds to the median value obtained from three experiments done in triplicate. Bars corresponded to quartiles 1 and 3. * indicates a significant difference (p<0.05) between cells treated or not with 5 μM DZNep. As indicated in Firefly/RLU from Renilla” for the HS2_P_Luc transfection done in absence or presence of 5 μM DZNep are used as a reference and fixed at 1 arbitrary unit. They were respectively used to calculate the ratios of each construct assayed in absence or in presence of 5 μM DZNep.Expression of the (DOCX)Click here for additional data file.S11 Figa) mariner elements and Δ8 mariner segments were counted using the RepeatMasker annotation in the Hg19 version. (*) number of elements with a maximum of 40 bp truncated at their 5’ and-or 3’ ends. Damaged Δ8 mariner segments are DNA regions containing one or several DNA insertions or deletions. (b) Conservation profiles in terms of sequence divergence and integrity of Hsmar1 and Hsmar2 elements in the human genome. The horizontal axes are calibrated to the size of the mariner elements. The vertical axis represents the number of stacked mariner elements. Each horizontal lane corresponds to one mariner element that consists in 1 to 6 high-scoring segment pairs (HSP). For Hsmar1, 469 elements are in 1 HSP, 106 in 2 HSPs, 16 in 3 HSPs, and 1 in 1 HSP. For Hsmar2, 838 elements are in 1 HSP, 280 in 2 HSPs, 6, in 3 HSPs, 28 in 1 HSP, 8 in 5 HSPs, and 3 in 6 HSPs. Mariner elements were ranked in the stacking from the best RepeatMasker scores to the worst. In the right margin a coloured scale from red to blue is shown, representing the sequence divergence of each element with its reference element . Black bars above the horizontal axis show the location of the Δ8 segments.((DOCX)Click here for additional data file.S12 FigHsmar2 inserted in positive orientation into an intragenic region and flanked by its 5' and 3' regions (each 2-kbp) were investigated in four cell lines  of ENCODE. Hsmar2 Sil+ were those located into LOC 389996  that overlaps with the otopetrin 1 pseudogene, and into intronic regions of the SLC22A16 , MYO5B , and PDS5A  genes. Graphics were calculated with Integrative Genomic Viewer (IGV2.3.63) and ENCODE data. On the top were supplied the locus name, a Refseq graphic of each locus, and the location of each Hsmar2 element. Below, for each locus and in each cell line, the five first lanes described the CHIP-seq signal (named criterion-S on the right hand) for H3K27me3, EZH2, H3K4me3, H3K27ac and H3K9me3, respectively. The area in grey located absences of CHIP-seq signal. The signal scales at each locus were 60, 25, 25 and 25 in Dnd41; 50, 10, 25 and 7 in GM12878; 100, 20, 100 and 8 in HepG2; and 30, 15, 10 and 15 in HMEC. Results highlighted that the CHIP-seq signals varied importantly depending on the locus and the cell line. The five last lanes described the location of the CHIP-seq peaks  for H3K27me3, EZH2, H3K4me3, H3K27ac and H3K9me3. The chromatin status of each locus in each cell type was indicated below between bracket with P, T, and H indicating a polycomb, trithorax or Su(var)39/HP1 status, respectively. “ucs”indicated an absence of co-localized peaks that was considered under our analysis conditions as an undetermined chromatin status. The 4 graphs at the bottom described the input signal for each locus in each of the four cell lines. The signal scales were 30 for LOC 389996, 15.36 for SLC22A16, 10 for MYO5B, and 15 for PDS5A. Results highlighted that the statistical significance of CHIP-seq signals through CHIP-seq peaks depended importantly on the input signal, locus and cell line. The blue band indicated the location of Sil+.Four loci containing a Sil+ (PDF)Click here for additional data file.S13 FigThe hierarchical clustering was performed analysing the chromatin status  of 187 loci. “+” in the right margin located the Sil+. Loci with a “ucs” chromatin phenotype were filled in grey. Loci mainly associated with a polycomb or a Su(var)39/HP1 status in the cell lines are gathered in the green and yellow boxes, respectively. Among loci with a polycomb status, those displaying a bivalent trithorax or Su(var)39/HP1 status were respectively in the blue box or in the orange boxes. 2A red-blue colour scale depicted normalized chromatin status . At the bottom of the heat map, the cell lines analysed for their polycomb, Trithorax or Su(var)39/HP1 status were respectively typed in green, blue or orange. The names referencing H1-ESC annotations were typed in purple.(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Electrophorus electricus) has played an important role in the history of science. Remarkably little is understood about the molecular nature of its electric organs.With its unique ability to produce high-voltage electric discharges in excess of 600 volts, the South American strong voltage electric eel  contains supplementary material, which is available to authorized users. Electrophorus electricus) is a freshwater teleost (order: Gymnotiformes) from South America, the only species identified to date within the genus Electrophorus [E. electricus is most famous for its ability to generate strong voltage discharges  from elnd pumps .E. electricus produces weak EODs  for navigation and communication. However, it is unique within Gymnotiformes in possessing three distinct EOs , and it is the only gymnotiform capable of a strong-voltage EOD. In E. electricus, strong EODs are produced from the main EO and the anterior two-thirds of the more ventrally-positioned Hunter’s EO; weak EODs are produced from the Sachs’ organ and the posterior one-third of the Hunter’s organ  expression in an electric fish and show that all three EOs in E. electricus express a unique repertoire of miRNAs, including a novel miRNA and three conserved miRNAs involved with muscle development inhibition in mammals. The results build a framework for comprehensively understanding the molecular nature of an electrocyte and provide a foundation for future work on electric organs in electric fish.Despite the importance of electric fishes in the history of science, genomic, proteomic, and metabolomic approaches towards understanding the molecular nature of electrocytes (the single cell of the electric organ) have only lately been undertaken -16. In aE. electricus and the transcriptome of the three EOs and five other tissues: brain, spinal cord, heart, skeletal muscle, and kidney, as described previously [E. electricus and Danio rerio, the nearest related sequenced fish genome, showed considerable local synteny  enrichment analysis on each of the 12 tissue-specific clusters. This analysis revealed enriched functions that were consistent with expectations based on the tissues in the tissue-specific clusters , the most enriched GO terms involved the general category of transcriptional regulation, including sequence-specific DNA binding, ligand-activated sequence-specific DNA binding, and sequence-specific DNA binding transcription factor activity, as well as GO terms involved with acetylcholine receptor activity. In cluster 7 , the enriched GO terms were involved in metabolism, such as NADP binding, NAD (P) + transhydrogenase activity, and phosphofructokinase activity, as well as insulin-like growth factor (IGF) I and II binding. In cluster 10 , the enriched GO terms interestingly included voltage-gated Ca2+ activity and Ca2+ binding. Additionally, cluster 10 shows enrichment in receptor binding, receptor activity, and hyaluronic acid binding.GO analysis of cluster 9  showed an enrichment of GO terms involved with transmembrane transporting Figure , while eTopGO was further used to generate GO graphs for each of the five primary clusters of interest  using enriched GO terms  as input . Cluster 9 also includes GO terms involved in hormone/androgen binding; this GO term is physiologically relevant as EOD has been shown to be regulated in part by the presence of sex hormones [Similar to the results from cluster 6, the GO graph generated for the enriched terms in cluster 7  had a highly represented, broad category of metabolism . Finally, the cluster 9  GO graph has a highly represented group characterized as transmembrane transport . This group includes GO terms such as voltage-gated sodium channel activity and inward rectifier Khormones .hoxc10a, hoxc11a, hoxc12a and hoxc13a genes in all three electric organs  from the hoxca cluster to be over-expressed in two other Gymnotiformes (Eigenmannia virescens and to a lesser extent Sternopygus macrurus) and one mormyrid (Brienomyrus brachyistius) as well . Jawed vertebrates have four paralogous hox cluster genes , among which only the hoxc cluster was shown to be dispensable for body plan development. The entire cluster was lost in Elasmobranch fishes and its deletion in mice caused only minor transformations of axial identity [hoxca genes in some species of electric fishes.One surprise arising from the 8-tissue profiling was the elevated expression of hat axis . We obseidentity -22. Whetegr3 (early growth response 3), six2a (sine oculis-related homeobox 2a), hoxc11a (homeo box C11a), foxj3 (forkhead box J3), ar (androgen receptor), pou3f1 (POU class 3 homeobox 1), lbx2 (ladybird homeobox homolog 2), and hoxc10a (homeo box C10a).A significant future goal is to understand the mechanisms underlying EO development and maintenance. As a step toward that goal, a plausible hypothesis is that transcription factors highly upregulated in EO regulate distinctive characteristics of EO. Based on this hypothesis, candidates for this set of important transcription factors were identified by their high expression ratios (>7-fold) in EO compared to skeletal muscle  and lbx2   and emx2 . We then compared the number of occurrences of each transcription factor binding site in the promoters of the 51 most highly expressed cluster 9 genes individually against our background control and found p-values smaller than 0.005 for emx2, lbx2, pou3f1 (POU class 3 homeobox 1), prrx1b, and sox12 (SRY-box 12) . Two teleost-specific members  of the inward rectifying K+ channel (Kir2) family are abundant in E. electricus electrocytes. A hallmark of Kir2 channels is a highly conserved aspartate residue at the inner mouth of the channel that binds Mg2+ and polyamines and plugs the channel at depolarizing voltages imparting rectification [kcnj2b and kcnj12b, both have an asparagine at the Mg2+ binding site, suggesting that the gymnotiform electrocyte has a unique intracellular environment. In addition, the α2 isoform of the sodium pump, which is highly over-expressed in the electrocyte, shows an amino acid substitution at a conserved site , and medium (green) but no short-wave sensitive (blue and violet) cone opsin genes . However, we cannot rule out the possibility that nerve contamination in EO tissue (which is highly innervated) contributed to this result, a possibility that would require additional experimentation to rule out.Within the cluster of genes co-expressed in EO, brain, and spinal cord (cluster 10) we found enrichment for terms pertaining to Cahoxc10a, hoxc11a, hoxc12a, hoxc13a, six2a, sox11b, and mef2b) in EO from four electric fishes  [A crucial step in our greater quest to elucidate the mechanisms by which electric organs have evolved is understanding the underlying principles of how genes are regulated in EO compared to muscle. Given that several transcription factors are highly upregulated in EO, we hypothesized that there may be enrichment of these transcription factor binding sites in the promoters of genes that are also highly expressed in EO. However, our attempts at identifying enriched binding motifs within the promoters of genes highly expressed in EO (cluster 9 genes) failed to identify a “smoking gun”; thus, it is reasonable to suspect that other mechanisms beyond enrichment of DNA binding sites for highly expressed transcription factors in EO might be responsible for regulation of cluster 9 genes. First, binding sites for transcription factors that are not upregulated might be enriched in cluster 9 genes and lead to transcriptional upregulation in EO. This mechanism, however, does not explain the restriction of cluster 9 gene upregulation to only EO. As an additional mechanism, factors regulating gene transcription, including chromatin state, might differentially affect availability of these binding sites in cluster 9 genes compared to availability in genes of other clusters. The upregulation of seven transcription factor genes  in both Gymnotiformes tested; these miRNA have known roles in inhibiting muscle differentiation [E. electricus EO but not identified in S. macrurus EO, implying that this novel miRNA arose for E. electricus-specific electrocyte development and function  and of mRNA and miRNA libraries from eight organs including the three electric organs. This study, which builds upon previous work from this group focusing on shared protein-coding gene expression patterns between EO and skeletal muscle in multiple independent lineages of electric fish [E. electricus in the Gymnotiformes lineage.We describe here an analysis of the first sequenced genome of an electric fish . GO terms were mapped back to all possible E. electricus genes using their D. rerio assignments. A total of 13,647 E. electricus genes remained in the analysis after these steps. In order to prevent the presence of a single GO term in one of our gene clusters from coming up as significant, we filtered out all GO terms from the tissue-specific clusters that were present less than two times. Enriched GO terms in each of the 12 tissue-specific clusters were identified by using the “elim” method of topGO [eviously  as beingof topGO , with a E. electricus.We measured miRNA expression in eight tissues  of One (1) μg total RNA from each tissue (isolated as described above for mRNA sequencing) was used to prepare small RNA sequencing libraries using an Illumina TruSeq Small RNA Library Preparation Kit according to the manufacturer’s instructions  with one modification: cDNA was size-selected on an agarose gel in the 140–300 bp range. Each of seven tissues  was labeled with a unique index using PCR indexing primers. The indexed small RNA libraries were pooled and sequenced on a single lane of HiSeq (1x100bp) at the same time as the corresponding mRNA was sequenced in our previous study . The kiE. electricus miRNA orthologs were identified by BLASTN comparison of expressed sequences from small RNA libraries to sequences from the Rfam database [Danio rerio, Tetraodon nigroviridis, and Takifugu rubripes sequences in the Ensembl database [. To identify genomic precursors for conserved and novel miRNAs, sequences were aligned to the E. electricus genome, and perfect matches together with 140 bp of flanking sequence were retrieved. Secondary RNA structure was predicted using RNAFold from the Vienna package [Conserved database . A set odatabase . To iden package , and pre package . MiRNAs  package .E. electricus, using a relaxed standard which allowed up to two base pair mismatches between sequences to account for known processes which add non-template nucleotides to the 3′ end of miRNAs [E. electricus tissue matched conserved miRNA families found in the E. electricus genome. Small miRNA sequences from S. macrurus were matched to precursors from E. electricus because of the lack of availability of a S. macrurus genome. MiRNA read counts for each tissue profiled in E. electricus and S. macrurus were first normalized by correction for the median number of total reads, followed by linear normalization  with default settings for core and matrix similarity identified potential binding sites for transcription factors in promoter regions. A promoter was defined as the region 2 kilobases upstream from the transcription start site of a gene as determined by AUGUSTUS gene modeling. For statistical analysis, the number of binding sites for a transcription factor in putative promoters of cluster 9 genes as a group or as single genes was compared to the expected number. The expected number was derived from the binding sites in putative promoters of a set of 2984 randomly selected genes from all clusters. The p-value of a comparison was defined as the cumulative probability from a binomial distribution of the expected number of binding sites greater than or equal to the number of binding sites identified with MatInspector in cluster 9 genes. The p-values reported were not corrected for the number of comparisons.E. electricus transcription factors selected for analysis with MatInspector were assumed to be identical to the binding properties of the homologous vertebrate transcription factors as described in MatBase (Genomatix). The relevance of DNA binding properties in MatBase to the properties of these E. electricus transcription factors has not been experimentally determined. Amino acid identities greater than 88% and averaging 96% for the DNA binding domains in these E. electricus transcription factors compared with mouse homologs supports the use of the MatInspector database for the analysis.The DNA binding properties of 21 highly upregulated E. electricus is available at http://efishgenomics.zoology.msu.edu together with BLAST and genome browsing capability. All raw sequencing reads are available in the National Center for Biotechnology Information Short Read Archive under BioProject IDs for the E. electricus genome (PRJNA249073) and transcriptome sequences for mRNA and miRNA analyses (PRJNA248545). All E. electricus miRNAs are available at miRBase. For gene and transcript expression analysis, see [Whole genome sequence and annotation of sis, see .E. electricus for this study was approved by the University of Wisconsin Animal Care and Use Committee (Protocol Number M01657). All procedures on S. macrurus followed the American Physiological Society Animal Care Guidelines, and were approved by the Institutional Animal Care and Use Committee at the New Mexico State University, NM (IACUC protocol 2014–044).The University of Wisconsin Madison is accredited by the American Association of Laboratory Animal Care. The protocol governing the animal care and usage of


Gene transcription mediated by RNA polymerase II (pol-II) is a key step in gene expression. The dynamics of pol-II moving along the transcribed region influence the rate and timing of gene expression. In this work, we present a probabilistic model of transcription dynamics which is fitted to pol-II occupancy time course data measured using ChIP-Seq. The model can be used to estimate transcription speed and to infer the temporal pol-II activity profile at the gene promoter. Model parameters are estimated using either maximum likelihood estimation or via Bayesian inference using Markov chain Monte Carlo sampling. The Bayesian approach provides confidence intervals for parameter estimates and allows the use of priors that capture domain knowledge, e.g. the expected range of transcription speeds, based on previous experiments. The model describes the movement of pol-II down the gene body and can be used to identify the time of induction for transcriptionally engaged genes. By clustering the inferred promoter activity time profiles, we are able to determine which genes respond quickly to stimuli and group genes that share activity profiles and may therefore be co-regulated. We apply our methodology to biological data obtained using ChIP-seq to measure pol-II occupancy genome-wide when MCF-7 human breast cancer cells are treated with estradiol (E2). The transcription speeds we obtain agree with those obtained previously for smaller numbers of genes with the advantage that our approach can be applied genome-wide. We validate the biological significance of the pol-II promoter activity clusters by investigating cluster-specific transcription factor binding patterns and determining canonical pathway enrichment. We find that rapidly induced genes are enriched for both estrogen receptor alpha . To understand changes in transcription arising due to stimuli it is useful to model the dynamics of transcription. We present a probabilistic model of pol-II transcription dynamics that can be used to compute RNA transcription speed and infer the temporal pol-II activity at the gene promoter. The inferred promoter activity profile is used to determine genes that are responding in a coordinated manner to stimuli and are therefore potentially co-regulated. Model parameters are inferred using data from high-throughput sequencing assays, such as ChIP-Seq and GRO-Seq, and can therefore be applied genome-wide in an unbiased manner. We apply the method to pol-II ChIP-Seq time course data from breast cancer cells stimulated by estradiol in order to uncover the dynamics of early response genes in this system. Transcription mediated by RNA polymerase II (pol-II) is an essential process in the expression of protein-coding genes in eukaryotes. Transcription is dependent upon a number of sequential and dynamic events, such as recruitment of pol-II to the transcriptional start site, activation of pol-II through phosphorylation of its C-terminal domain, elongation of the nascent transcript through the transcribed region and termination Chromatin immunoprecipitation, in conjunction with massively parallel sequencing (ChIP-seq) evaluates interactions between proteins and DNA, and, for example, can be used to monitor the presence of pol-II on DNA. Estimating the amount of pol-II associated with a transcribed gene provides a measure of transcriptional activity et al. fit a mechanistic model of pol-II transcription to nascent RNA data at a single locus and obtained a transcription speed of 4.3 kilobases per minute et al. activated transcription of genes greater than 100 kbp in length and estimated the transcription speeds using a model that measures an intronic RNA signal through taking advantage of co-transcriptional splicing. They obtain an average transcription rate of 3.1 kbp min−1−1A number of studies have attempted to determine the rate of transcription through modelling the dynamics of pol-II. Darzacq One way around the low temporal resolution of typical high-throughput time course data is to employ a non-parametric model of the biological signals of interest. In many cases we expect these signals to vary continuously and smoothly in time, when averaged over a cell population, and a Gaussian process model provides a convenient non-parametric model in such cases Here we present a Gaussian process model of transcription dynamics which can be fitted to genome-wide pol-II occupancy data measured using ChIP-Seq. The model describes the movement of pol-II through the gene body and combines a flexible model of promoter-proximal pol-II activity with a reliable estimate of transcription speed. By identifying genes which fit the model well, we provide a useful method to identify actively transcribed genes. The model does not assume a constant transcription speed and can therefore identify variable rates of transcription, for example due to transcriptional pausing. Model parameters are inferred using either maximum likelihood (ML) estimation or via Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. The Bayesian approach provides confidence intervals for parameter estimates and can incoporate priors that capture domain knowledge, e.g. the expected range of transcription speeds, based on previous experiments.We fit our model to a pol-II ChIP-Seq time course dataset from MCF7 breast cancer cells stimulated with estradiol. The model is used to identify the set of transcriptionally engaged genes and estimate their mean transcription rate and transcriptional activity near the promoter. By clustering promoter activity profiles, potential co-regulated groups of genes are identified, particularly those that respond rapidly to estrogen signalling. Subsequent characterisation of transcription factor (TF) binding sites in proximity to the promoters of genes within clusters provides a means of classifying groups of promoters that are responsive to the binding of specific combinations of TFs. Additionally, publically available ChIP-Seq datasets of TF profiles from the same system were used to identify cluster-specific patterns in TF-binding. The rates of transcription estimated by our model are consistent with the literature Our methodology has a number of advantages. We do not require data with high temporal resolution, making it feasible to model transcriptional dynamics genome-wide using ChIP-Seq or GRO-Seq time course data. We infer transcription rates for all genes in an unbiased manner and by using Bayesian parameter estimation we are able to associate our transcription rate estimates with confidence intervals. Our model is non-parametric and therefore does not make very strong assumptions about the temporal changes in transcriptional activity. Fitting the model genome-wide allows us to identify and filter out transcripts where pol-II does not travel down the gene body. This provides a principled method to identify responsive genes, in particular, early acting estrogen responsive genes in the specific application considered here. Since our model does not enforce a uniform transcription speed over the entire gene body, we can take into account phenomena such as pol-II pausing which would result in a non-uniform transcription speed. We also use this model to infer the promoter activity of transcriptionally engaged genes, to identify co-regulated gene modules downstream of estrogen signalling.Visualizing pol-II ChIP-seq reads mapped to transcriptional units at multiple time points following the addition of estradiol to MCF7 cells reveals the motion of pol-II through the gene body of estrogen responsive genes see . ComputiA Gaussian process (GP) is a distribution over the space of functions. This distribution is completely specified by a mean function Here we introduce a novel application of GPs to modelling the spatio-temporal dynamics of pol-II occupancy during transcription. Convolved GPs allow the modelling of correlations between multiple coupled data sources. In our case these data sources are the pol-II occupancy over time collected at different locations along the transcribed region of a gene. Modelling the data as a convolved process borrows information from these different data sources in estimating the model parameters and inferring the underlying signal in the data. Also, we find that convolved GPs are necessary to account for changes in the shapes of signals observed at different regions of the gene. In linear systems theory, the output In order to capture the movement of the transcription wave through transcriptional units, we divide each gene into The estimated delay  out see . This phUsing Similarly,Let For a fully Bayesian approach, we take advantage of the fact that the parameters are positive and bounded. We transform the parameters using a logit transform and work with unconstrained variables. We place a Gaussian prior over the parameters in the transformed domain and draw samples from the posterior using the Hamiltonian Monte Carlo (HMC) algorithm https://github.com/ciiram/PyPol_II.Code to implement the method is freely available as a Python package, PyPol-II, which can be downloaded from When fitting the model, we fix To compute the average transcription speed we plot the position along the gene in base pairs (bp) versus the delay in minutes and compute a linear regression through the origin. The slope of the regression line gives us the transcriptional speed. Each sample of the parameters provides a set of delay estimates from which we obtain a speed estimate.A key component of our method involves the estimation of delay between time series observed at different segments of the gene. The study of time delay between related time series has received attention from a number of researchers for a long time The delay is estimated by finding the value of In the kernel based approach of equation  is modelThe value of We used synthetic data and previously published experimental data to assess our novel method's performance. To generate the synthetic data, the underlying function equation  was giveN was fixed at 20 and the observation interval in vivo using 5,6-dichlorobenzimidazole 1-beta-D-ribofuranoside (DRB) and the pre-mRNA measured after the inhibitor was removed. As verified by the authors, the kinetics of pol-II and pre-mRNA are similar hence we expect good performance on this dataset to indicate applicability of our method to pol-II ChIP-seq data.To assess performance of our method on a well characterised real-world dataset we obtained a dataset from Singh and Padgett To demonstrate an application to pol-II ChIP-Seq data, we apply our model to investigate the transcriptional response to Estrogen Receptor signalling. ChIP-seq was used to measure pol-II occupancy genome-wide when MCF-7 breast cancer cells are treated with estradiol (E2). Cells were put in estradiol free media for three days. This is defined media devoid of phenol red (which is estrogenic) containing 2% charcoal stripped foetal calf serum. The charcoal absorbs estradiol but not other essential serum components, such as growth factors. This results in basal levels of transcription from E2 dependent genes. The cells are then incubated with E2 containing media, which results in the stimulation of estrogen responsive genes. The measurements were taken at logarithmically spaced time points 0, 5, 10, 20, …, 320 minutes after E2 stimulation.Raw reads were mapped onto the human genome reference sequence (NCBI_build37) using the Genomatix Mining Station (software version 3.2.1). The mapping software on the Mining Station is an index based mapper that uses a shortest unique subword index generated from the reference sequence to identify possible read positions. A subsequent alignment step is then used to get the highest-scoring match(es) according to the parameters used. We used a minimum alignment quality threshold of 92% for mapping and trimmed 2 basepairs from the ends of the reads to account for deterioration in read quality at the 3′ end. The software generates separate output files for uniquely mapped reads and reads that have multiple matches with equal score. We only used the uniquely mapped reads. On average about 66% of all reads could be mapped uniquely. The data are available from the NCBI Gene Expression Omnibus under accession number GSE44800.Time series of pol-II occupancy over various segments of genes were computed in reads per million (RPM) We first applied our methodology to synthetic data in order to compare its performance to other methods. We investigated the performance of five methods, namely cross-correlation (Corr), DCF, the kernel approach of SLC9A9 gene (the same data shown in We next applied the model to pre-mRNA data from Singh and Padgett We applied our method to a ChIP-Seq time-course dataset measuring pol-II occupancy genome-wide when MCF-7 cells are treated with estradiol (E2). For our initial experiment, we considered 3,064 genes which exhibit significant increase of pol-II occupancy between 0 and 40 minutes after E2 treatment. These genes were determined by counting the number of pol-II tags on the annotated genes in the RefSeq hg19 assembly at 0 and 40 minutes after E2 treatment and computing the TIPARP gene. MYH9 and RAB10. This motivates a closer look at long genes. The advantage of fitting each of the delay parameters independently instead of enforcing a linear relationship is that it allows us to take into account phenomena such as pol-II pausing and provides a means to filter genes where the values of estimated delay are not naturally ordered. Visual inspection of the inferred time series of the top ranked genes is consistent with a ‘transcription wave’ traversing the gene. The transcription wave is especially evident in the longer genes The inferred latent functions for each gene model the pol-II activity adjacent to the promoter. Clustering these profiles and examining the average profiles of each cluster allows us to visualise the general trends and also classify genes according to the immediacy and nature of the response. This provides an alternative to clustering based on mRNA abundance data (from microarray or RNA-Seq experiments) which is regulated both by mRNA production and degradation processes. The production of mRNA may be delayed relative to the actual activation of transcription at the promoter causing genes which are actually triggered at the same time to show different rates of mRNA production. Differences in degradation rate can also influence mRNA abundance profiles. It may therefore be difficult to distinguish early and delayed transcriptional regulation from mRNA abundance data.To classify the profiles we sample the mean of the latent function  in  and use JAK1 and JAK2 in cluster 10, which has a prominent early peak, suggests that the response of both genes to E2 is rapid and coordinated. Since these genes are known to act together in several biological pathways such as the IL-6 signaling pathway and the IFN gamma signaling pathway, their appearance in the same cluster suggests that the clustering is likely to reveal other biologically significant relationships. A closer look at the inferred pol-II promoter profiles of some examples in cluster 10, the earliest peaking cluster, and the corresponding inferred pol-II profiles over the last 20% of the genes reveals the possible influence of gene length on mRNA production and how clustering the inferred promoter profiles can account for this influence and uncover potential co-regulation. CLN8, BRI3BP and JAK2 in cluster 10. The 383 genes found to fit the model well were grouped into 12 clusters  with theet al.CASP7, FHL2, GREB1, ITPK1, NRIP1, WWC1 are found to fit our pol-II model well with ChIP-seq data. et al.  which are limited to the finite set of sampling times. We see that all mRNA peaks occur after WWC1 which is a long gene In Hah In the supplementary material, we compare the clustering obtained from the inferred promoter profiles to that obtained if the time series of the raw ChIP-seq reads are clustered and show that our model has the potential to uncover relationships which may be missed if we only consider the raw ChIP-seq reads.http://cistrome.org). In earlier work on the estrogen interactome, Fullwood et al.We investigated the TF peaks in a 40 kbp region around the gene transcription start site for all genes in each cluster using ChIP-seq data for a number of TFs measured under similar experimental conditions (i.e. MCF-7 breast cancer cells treated with E2) in the cistrome database Click here for additional data file.Text S1Supporting text. This file contains additional details of the mathematical model and results of biological validation via gene ontology analysis and transcription factor binding.(PDF)Click here for additional data file.


Insertional mutagenesis screens of retrovirus-induced mouse tumors have proven valuable in human cancer research and for understanding adverse effects of retroviral-based gene therapies. In previous studies, the assignment of mouse genes to individual retroviral integration sites has been based on close proximity and expression patterns of annotated genes at target positions in the genome. We here employed next-generation RNA sequencing to map retroviral-mouse chimeric junctions genome-wide, and to identify local patterns of transcription activation in T-lymphomas induced by the murine leukemia gamma-retrovirus SL3-3. Moreover, to determine epigenetic integration preferences underlying long-range gene activation by retroviruses, the colocalization propensity with common epigenetic enhancer markers (H3K4Me1 and H3K27Ac) of 6,117 integrations derived from end-stage tumors of more than 2,000 mice was examined.We detected several novel mechanisms of retroviral insertional mutagenesis: bidirectional activation of mouse transcripts on opposite sides of a provirus including transcription of unannotated mouse sequence; sense/antisense-type activation of genes located on opposite DNA strands; tandem-type activation of distal genes that are positioned adjacently on the same DNA strand; activation of genes that are not the direct integration targets; combination-type insertional mutagenesis, in which enhancer activation, alternative chimeric splicing and retroviral promoter insertion are induced by a single retrovirus. We also show that irrespective of the distance to transcription start sites, the far majority of retroviruses in end-stage tumors colocalize with H3K4Me1 and H3K27Ac-enriched regions in murine lymphoid tissues.We expose novel retrovirus-induced host transcription activation patterns that reach beyond a single and nearest annotated gene target. Awareness of this previously undescribed layer of complexity may prove important for elucidation of adverse effects in retroviral-based gene therapies. We also show that wild-type gamma-retroviruses are frequently positioned at enhancers, suggesting that integration into regulatory regions is specific and also subject to positive selection for sustaining long-range gene activation in end-stage tumors. Altogether, this study should prove useful for extrapolating adverse outcomes of retroviral vector therapies, and for understanding fundamental cellular regulatory principles and retroviral biology. A hallmark of retroviral replication is the stable integration of the ~9 kb genome into host chromosomes as a provirus which makes gamma-retroviral and lentiviral-based vectors attractive gene delivery vehicles for some therapeutic applications in human gene therapy ([The murine leukemia viruses (MLVs) are slowly-transforming gamma-retroviruses that induce tumors of hematopoietic origin upon injection into susceptible newborn mice ,2. MLVs therapy  with transition to acute myeloid leukemia (AML) and development of monosomy 7 [IM has received increased attention due to the occurrence of adverse events following gamma-retroviral vector-based gene therapy to correct the X-linked severe combined immunodeficiency disease (X-SCID) in which a lymphoproliferative disorder was induced in patients by activation of the oncogene -13. In anosomy 7 ,15. Therin vitro studies and studies of cultured primary cells have shown that gamma-retroviral vectors favor integration into nucleosomal DNA [While earlier studies suggested that MLVs preferentially target DNase I hypersensitive regions, transcription start sites (TSSs) and CpG islands ,17, seveomal DNA -24. Chroomal DNA -28, are omal DNA -24.Shh which is positioned 1 Mb from the Shh promoter [c-Myb upstream elements that coincide with enhancers in the EPU of this gene, establish physical contact with the c-Myb promoter through DNA looping [The bromodomain and extraterminal (BET) proteins mediate MLV integration at TSSs by tethering the integrase to acetylated H3 and H4 tails , and exppromoter ,34. The promoter , and it  looping . Taken tIn the past, MLV integrations were identified using anchored-type PCR methods and Sanger sequencing, including e.g. a special 5′-end primer called a splinkerette, or inverse PCR -38. Nextin vivo[In this study we subjected four NMRI mouse tumors induced upon infection with the rapid T-lymphomagenic MLV SL3-3 wild-type strain to strand-specific and paired-end RNA sequencing (RNA-seq) to determine retroviral integrations genome-wide, and to identify local patterns of retroviral IM. We then used a dataset of 6,117 SL3-3 integrations derived from lymphoid tumors of more than 2,000 mice to determine the colocalization propensity of proviruses with ENCODE immunoprecipitation with sequencing (ChIP-seq) enhancer and promoter markers in vivo. Our stuIn this study RNA-seq was used without specific enrichment to map integrations, and simultaneously determine transcript expression levels at sites of integration. We made whole-transcriptome libraries of four thymic tumors induced by wild-type SL3-3 in mice of an inbred NMRI strain. The four tumors are referred to as 324, 327, 359 and 410. We obtained ~290 million 101-base reads exceeding an average of ~70 million single reads per tumor (online available sequencing data). Integrations were mapped by analysis of 14 paired-end sequence signatures that expose genetic structural alterations in tumors , where chimeric transcription is enhanced by high levels of mouse transcription (described below).We observed a proportional increase in the number of integrations with increasing sequencing depth, and with the exception of tumor 359, the fraction of chimeric read pairs aligning in a direction from mouse to viral sequence, and vice versa, were nearly evenly distributed Figure A. This iP = 2.03E-30, hypergeometric probability)  with genes assigned from the conventional screens. However, the potential risk of misassignment is higher since the exact locations of proviruses are not known  integrations supported by chimeric fusions in DNA analyses using PCR (described in Methods). Since the majority of integrations that were not confirmed obtained a minimum coverage in sequencing Figure E, it is Arf6, Ccnd3, Chd9, Coro1a, Frat1, Gfi1/Evi5, Hivep1, Hsp90b1, Ikzf1, Kis2, Mef2c, Mir17, Myb, Mycn, Pvt1, Rasgrp1 and Thada  (the Gfi1/Evi5 locus), tumor 359 contained six integrations at this locus, four of which were positioned in sense in the 3′-UTR of Gfi1  showing that multiple CISs are targeted in tumors: eukemias  . The Klf7 locus contains two integration clusters one of which is positioned ~35 kb downstream of Klf7 while the other is located in the first intron of the gene. Klf7 was found consistently overexpressed in tumors harboring proviral insertions  and retSlc6a20a/Lztfl1/Ccr9 and Syn2/AK038749 (described below). Integration at Ccr9 induces tandem-activation of the distal genes Slc6a20a and Lztfl1 which are located adjacently in the same orientation .We observed retrovirus-induced activation of independent transcripts at other loci including  Slc6a20b. Slc6a20Ccr9 activates Slc6a20a and Lztfl1, exclusively, in three independent tumors analyzed. We speculate if the tandem-type activation pattern is induced by integration into a shared regulatory region through which proviruses can deregulate both Slc6a20a and Lztfl1. The regulation of genes through shared enhancers has been described previously and appears to be a regulatory principle that is applicable genome-wide [Therefore integration at ome-wide ,56,57.Syn2/AK038749 induces expression of the brain genes Syn2 and non-coding AK038749 located in opposite orientation . We confirmed expression of AK038749 by 5′-RACE  in T-cell lymphomas induced by SL3-3 integration in the Esam/Vsig2/Nrgn/Siae/Spa17 locus where the Nrgn expression level in lymphoid tumors corresponded to brain levels [We have previously reported on the activation of neuronal neurogranin , functions as a hotspot for retroviral integrations that induce expression of another distal gene (Irs4)  Figure D, withouta locus  is partiCelf2, whereby a non-chimeric transcript variant initiating from a far upstream position is activated, including also increased transcription in the provirus-containing intron (red arrow).The deregulation shown in Figure Celf2 reference sequence containing the integrated provirus made it possible to link upstream and downstream Celf2 sequence to the provirus  sites: the canonical SD located ~39 kb upstream from the insertion site; intronic iSD1 and iSD2, located ~38 and ~2 kb from the integration site, respectively. We confirmed splicing between the canonical SD site and the env splice site by RT-PCR and Sanger sequencing (data not shown). In the 3′-end of Celf2, splicing is mediated by iSD3 and iSD4, located 15 bp and 180 bp from the integration site, respectively. The coverage by sequencing reads at the 3′-LTR/mouse junction indicated that Celf2 is also activated by retroviral promoter insertion. Transcription initiation at the canonical U3/R position was confirmed by 5′- RACE as shown in Figure Remapping of chimeric sequencing reads against the s Figure D. RNA spCelf2 integration cluster , intermediate (~8 kb) and distal (>10 kb) positions relative to UCSC TSSs . The chromosomal distribution of integrations colocalizing directly in Figure We determined integration-enrichments of promoter-distal integrations at ChIP-seq peaks Figure , and obsWe observed consistently lowered enrichment of integrations at enhancers of brain indicating that retroviral integration targets enhancers tissue-specifically, and therefore the enrichments in most cases reflect integration into enhancers that are common to brain and the lymphoid tissues (data not shown). The distribution of promoter-distal integrations with respect to the midpoints of H3K4Me1 and H3K27Ac ChIP-seq peaks shows that the proviruses form a dense cluster peaking at ~800 bp in case of lymphoid enhancers (i.e. spleen and thymus), while the distribution is markedly distorted in case of brain enhancers (i.e. cortex and cerebellum) Figure .Considering overlapping integrations in Table In this study we used RNA-seq and ENCODE ChIP-seq data to determine positions of integration, local patterns of mouse sequence deregulation and integration-enhancer colocalization in end-stage tumors induced by the SL3-3 retrovirus. We mapped 136 integrations in only four tumors. 59 out of 92 (64%) integrations supported by chimeric fusions were confirmed by DNA analyses  using known gene annotations though infrequent activation of mouse (unannotated) sequence was observed at several positions as described throughout this manuscript. We have previously shown that integration into major CISs, including me cases . This come cases ,5. In adGfi-1/Evi-5 and Myc/Pvt1. The probability of finding multiple integrations in the same region in one tumor by chance is exceedingly small. Therefore, integrations into such positions are either pivotal for tumorigenesis or promoted in a context-dependent manner. An example is LMO2 and IL2RG cooperation in leukemia cases among X-SCID patients where insertional mutagenesis of LMO2 results in an increased growth advantage in the presence of IL2RG[Ccnd3, Frat1, Gfi1/Evi5, Myb and Myc/Pvt1 . Elevated levels of the transcription factor KLF7 is associated with minimal residual disease and relapse following chemotherapeutic treatment of childhood acute lymphoblastic leukemia (ALL) and KLF7 also promote early T cell survival [SYN2 encodes a neuronal phosphoprotein (Synapsin 2) and is deregulated in human glioblastoma multiforme and breast cancer [IRS4 is activated in T-ALL following COL4A5/IRS4 translocation involving the T-cell receptor beta locus [CELF2, which functions as a tumor suppressor in colon cancer [TMEM30B and PRKCH which are located adjacently on human chromosome 14 have been implicated in brain and breast cancer, respectively [SLC6A20, LZTFL1 and CCR9 are located on 3p21.3 in a region that is often found eliminated in tumors, referred to as CER1 (commonly eliminated region 1). Chromosome 3 abnormalities have been proposed to mediate tumor formation due to loss of putative tumor suppressor genes [WWOX also functions as a tumor suppressor in cervical cancer where it induces apoptosis and inhibits proliferation [The human orthologs of the genes described in this study are implicated in cancer . We have shown that unannotated RNAs are transcribed at sites of integration as well as in more distal regions , and that the provirus may activate transcription outside the actual gene in which it is integrated (the Col4a5/Irs4 and Wwox loci). We have also shown occurrences of combination-type activation patterns where retroviral promoter insertion and enhancer activation mutagenesis are employed by a provirus to alter the expression of a single gene (Celf2) as well as genomic regions outside genes (the Klf7 and Tmem30b/Prkch loci).We have shown several examples where more than a single gene is subject to activation from the same or opposite DNA strands including both proximal genes and genes whose initiation sites are positioned distantly from each other  transcription from the non-infected allele, and (2) unannotated transcripts. We believe that combination-type deregulation may represent a more general mechanism exposed by whole-transcriptome RNA-seq in our study.per se[AZU1 provides an example where expression of this non-LTR retrotransposon correlates with the expression of multiple surrounding genes in addition to AZU1[cis-linked genes thereby coordinating gene switching during hematopoiesis, and it also activates intergenic RNAs at low levels as a result of transient DNA looping with multiple intergenic sites [The promoters of many coding genes transcribe non-coding RNAs in the opposite direction, and are therefore bidirectional per se, and in per se,94. Moreper se,56. Enhaper se, and theper se. Noncodin to AZU1. Consideic sites . TherefoTaken together the deregulations described by us comply with emerging principles of complex higher-order genome regulation and show that MLVs have evolved to hijack such routes to activate multiple regions resulting in complex and long-ranging deregulations that are difficult to evaluate using conventional methods. Our analyses of integration-enhancer colocalizations in tumors also strongly support such models. Therefore, we anticipate that retrovirus-induced deregulations of equivalent or similar complexity are broadly applicable to other loci throughout the genome.The analysis of MLV-induced mouse tumors using RNA-seq has revealed novel mechanisms of retroviral insertional mutagenesis resulting in deregulations that reach beyond a single and nearest annotated gene target. Awareness of this previously undescribed layer of complexity regarding host sequence activations may prove important for elucidating adverse effects in retroviral-based gene therapies. We have also shown that wild-type gamma-retroviruses are positioned at enhancers of lymphoid tumors irrespective of their distance to TSSs, showing that insertion into regulatory regions is highly specific and also subject to positive selection during tumorigenesis. This suggests a mechanism whereby the provirus exploits the higher-order genome regulatome for sustaining long-range deregulations in tumors. This study should prove useful for extrapolating adverse outcomes of retroviral vector therapies, and for understanding fundamental cellular regulatory principles.Sequences are available from the NCBI short read archive (accession no. SRP041565). Primer sequences not provided in Additional file Inbred BALB/c and NMRI mice were infected with the rapid lymphomagenic MLV SL3-3 strain as described previously -50. UponTotal RNA was extracted from thymus and spleen by the guanidinium thiocyanate-phenol-chloroform method  and purified on spin columns using the RNeasy Mini Kit (Qiagen), including on-column DNase I treatment (Qiagen), and stored at −80°C. The concentration and purity was routinely determined on a NanoDrop (Thermo Scientific), and RNA integrity was assessed by microfluidics technology on the Agilent BioAnalyzer 2100 (Agilent).Total RNA from four SL3-3-induced NMRI mouse tumors was depleted of rRNA by hybridization using Ribo-Zero rRNA Removal Kit for Human/Mouse/Rat (Epicentre). The samples were then concentrated with RNA Clean & Concentrator (Zymo Research), and cDNA libraries prepared with the ScriptSeq RNA-Seq Library Preparation Kit (Epicentre) following the manufacturer’s guidelines. The libraries incorporated barcodes for multiplexed sequencing using the RNA-Seq Barcode Primers for Illumina (Epicentre). The resulting cDNA libraries were amplified by limited 12-cycle PCR and size-fractionated using high-percentage agarose gel electrophoresis. The size of the sequenced libraries was 400–450 bp including adaptors. The libraries were selectively quantified on a LightCycler 480 II Real-Time PCR System (Roche), using the KAPA Library Quantification Assay (Kapa Biosystems). The libraries were pooled in equal amounts on one paired-end flow cell lane using the cBot cluster generation process (Illumina), and sequenced on an Illumina HiSeq 2000 producing 2x101-base sequencing reads.To filter chimeric read pairs a custom reference was built manually using BLAT  based onp-values based on random sampling was performed as previously described [ChIP-seq data  were obtescribed . The redCelf2 in Figure Celf2  and the SL3-3 proviral sequence  inserted at the predetermined integration site . Samples were measured in duplicates using PerfeCta SYBR Green FastMix (Quanta Biosciences) in 10 μL reactions, and beta-actin as reference. As controls, tumors were used without known integrations at the loci shown in Figure DNA and RNA were copurified with TRIzol (Invitrogen) followed by DNA back-extraction and clean-up using QIAamp spin columns (Qiagen). To confirm integrations Figure E 40 ng oMLV: Murine leukemia virus; IM: Insertional mutagenesis; LTR: Long terminal repeat; CIS: Common integration site; RNA-seq: RNA sequencing; ChIP-seq: Chromatin Immunoprecipitation with sequencing; EPU: Enhancer and promoter unit; TSS: Transcriptional start site; RTCGD: Retrovirus and Transposon tagged Cancer Gene Database; qPCR: Quantitative real-time PCR; RACE: Rapid amplification of cDNA ends; FPKM: Fragments per kilobase of transcript per million mapped reads; TSD: Target site duplication.The authors declare that they have no competing interest.MS designed the deep-sequencing experiments, analyzed data and drafted the manuscript. MW carried out the splinkerette-based-PCR determination of MLV integrations. IRR and MS performed qPCR and RACE experiments. FSP conceived the study and participated in its design and direction. All authors read and consented to the contents of the manuscript.Figure S2. The Tmem30b/Prkch locus is deregulated by a bidirectional-type activation mechanism. Figure S3. Integration in Wwox induces overexpression of distal Maf and activation of unannotated transcription outside Wwox. Figure S4. ChIP-seq datasets from ENCODE. Figure S5. Integrations in end-stage tumors form clusters at immediate, intermediate and distal positions from TSSs. Figure S6. Chromosomal distributions of promoter-distal integrations in the complete and reduced integration datasets. Figure S7. Distribution of colocalizing integrations with respect to H3K4Me1 and H3K27Ac ChIP-seq peaks from spleen and thymus. Figure S8. Content of SL3-3 LTR-like sequence in the mouse genome assembly (NCBIM37/mm9). Figure S9. PCR confirmation of integrations identified in RNA sequencing.Paired-end RNA-seq signatures expose retroviral integration sites. Click here for fileIntegrations identified using RNA-seq. In columns 2–3 the integrations for each tumor are numbered according to Figures Click here for fileIntegrations identified in NMRI mice using splinkerette-based PCR. The dataset contains integrations from several cohorts of NMRI mice infected with SL3-3 including a cohort of 20 mice from which the four mouse tumors (324 through 410) subjected to RNA-seq originate (described in Methods). The integrations are sorted in order of decreasing number of tags at a locus. The first column shows tumors used in the present study for RNA-seq and/or qPCR and RACE. The layout column shows the orientations of integrations with respect to the genes: e.g. at chr5:108,167,153 the provirus is located 6,660 bp downstream from Evi5 in the opposite (antisense) orientation relative to this gene.Click here for fileRNA-seq transcript expression values. This table lists Cufflinks fragments per kilobase of transcript per million mapped reads (FPKM) [s (FPKM)  values uClick here for filePrimer sequences. For the DNA analyses the mouse primer sequences are available upon request.Click here for file


Selective maintenance of genomic epigenetic imprints during pre-implantation development is required for parental origin-specific expression of imprinted genes. The Kruppel-like zinc finger protein ZFP57 acts as a factor necessary for maintaining the DNA methylation memory at multiple imprinting control regions in early mouse embryos and embryonic stem (ES) cells. Maternal-zygotic deletion of ZFP57 in mice presents a highly penetrant phenotype with no animals surviving to birth. Additionally, several cases of human transient neonatal diabetes are associated with somatic mutations in the ZFP57 coding sequence.Fkbp6, which has a critical function in mouse male germ cell development. Genetic background-specific sequence differences also influence ZFP57 binding, as genetic variation that disrupts the consensus binding motif and its methylation is often associated with monoallelic expression of neighboring genes.Here, we comprehensively map sequence-specific ZFP57 binding sites in an allele-specific manner using hybrid ES cell lines from reciprocal crosses between C57BL/6J and Cast/EiJ mice, assigning allele specificity to approximately two-thirds of all binding sites. While half of these are biallelic and include endogenous retrovirus (ERV) targets, the rest show monoallelic binding based either on parental origin or on genetic background of the allele. Parental-origin allele-specific binding is methylation-dependent and maps only to imprinting control differentially methylated regions (DMRs) established in the germline. We identify a novel imprinted gene, The work described here uncovers further roles for ZFP57-mediated regulation of genomic imprinting and identifies a novel mechanism for genetically determined monoallelic gene expression.The online version of this article (doi:10.1186/s13059-015-0672-7) contains supplementary material, which is available to authorized users. Genomic imprinting is a mechanism of gene regulation occurring in eutherian mammals and to a lesser extent marsupials –3. UnlikPrevious studies demonstrated a critical role for the KRAB zinc finger protein ZFP57 in the maintenance of DNA methylation at multiple imprinted germline DMRs in mice and human , 20. FaiThe mechanism by which ZFP57 mediates its function is associated with its interacting partner KAP1, also known as TRIM28 or TIF1β. A complex containing ZFP57 and KAP1 appears to co-immunoprecipitate with all three catalytically active DNA methyl transferases as well as the hemi-methylated DNA binding protein NP95/UHRF1. Moreover, wild-type but not KRAB domain-deleted ZFP57 (incapable of KAP1 interaction) could substitute for the endogenous ZFP57 in ES cell DNA methylation maintenance . FinallyFkbp6 imprinted gene on chromosome 5.Utilizing an ES cell line expressing exogenous HA-tagged ZFP57, Quenneville and colleagues  mapped gIn addition, we discover that mouse strain-specific binding can be specified by sequence variation in the ZFP57 binding motif itself, and indirectly via genetically determined differential methylation. Analysis of allele-specific expression of genes associated with such non-imprinted ZFP57 binding provides the first mechanistic insight into how genetic variation might lead to monoallelic gene expression via epigenetic mechanisms regulated by ZFP57.In order to comprehensively map parental allele-specific ZFP57 binding targets, we performed chromatin immunoprecipitation (ChIP) followed by DNA sequencing (ChIP-seq) in hybrid ES cells derived from reciprocal F1 crosses between C57BL/6J  and Cast/EiJ  mouse strains  as outlined in Fig. p < 0.0001) either towards the parental or strain-specific origin of the allele. The remainder of the peaks were classed as being either biallelic or non-informative. In total, significant allelic assignments were made for approximately two-thirds of binding sites  and exons, including 3′ exon/untranslated regions of several zinc finger genes Fig. . AmongstPublished datasets for genome-wide epigenetic states in ES cells  were useAs ZFP57 has been shown to be a DNA methylation maintenance factor both in the embryo and in ES cells , 21, 22,Our DNA sequence analysis around ZFP57 bound sites has identified a heptamer TGCCGCN consensus binding motif Fig.  consisteGnas imprinted cluster, which harbors two germline-derived DMRs, ZFP57 was found to specifically associate with the one acting as the principal ICR for the region GATAGTTGTGYGGTAAGATGAGYGTTTTTTYGYGTTTTAGGAAYGGAATTTTATYGTYGYGAGAYGATTGTTAGGTAYGGAGYGGGGTTYGGG.Fkbp6 promoter region was amplified using the above forward and reverse primers and pyrosequenced sequentially using two sequencing primes. Genomic positions of CpGs covered by PSQ1 and PSQ2 sequencing primers are indicated in Fig. The Note, quantitative bisulphite pyrosequencing is a bulk population assay measuring overall methylation levels and as such is not allele-discriminatory. We could, however, determine allele-specificity for one CpG, which was present only in M. m. Cast genome (rs13487942). We initially attempted conventional clonal bisulphite sequencing but encountered strong cloning bias towards methylated alleles even in generally hypomethylated tissues (testis and ESCs).Assessment of allele specificity of gene expression was performed using quantitative RT-PCR followed by SNP pyrosequencing of the resulting product. The assays were performed using cDNA from four hybrid ES cell lines (2 BC and 2 CB) and neural stem cells derived therefrom. Genomic DNA corresponding to each cell line was used as a reference for 50 %:50 % BL6/Cast allele percentage relative to which skews in cDNA were analyzed Table . A dilutp < 0.01 (Bonferroni post-test) and an overall difference in median allele ratio ≥15 % (either towards BL6 or Cast) relative to genomic DNA  along the x-axes in Fig. Fkbp6 and Sdk2 genes, due to position of informative SNPs, PCR primers had to span exon boundaries and a reference hybrid gDNA standard could not be used to test for bias. Instead we constructed standards using a dilution series of a cDNA where we found these genes to be highly abundant and biallelic . Using this control we were able to demonstrate that monoallelic expression  did not arise from random PCR amplification biases.In the case of allele-specific analysis of Illumina sequencing data have been submitted to the Gene Expression Omnibus database under accession GSE55382.


The chronic infection of hepatitis B virus (HBV) is closely related to the occurrence and development of hepatocellular carcinoma (HCC). Accumulated evidence has shown that HBV X protein (HBx protein) is a multifunctional regulator with a crucial role in hepatocarcinogenesis. However, information on the mechanism by which HBV induces HCC is lacking. This review focuses on the pathological functions of HBx in HBV-induced hepatocarcinogenesis. As a transactivator, HBx can modulate nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and transcription factor AP-2. Moreover, HBx can affect regulatory non-coding RNAs (ncRNAs) including microRNAs and long ncRNAs (lncRNAs), such as miRNA-205 and highly upregulated in liver cancer (HULC), respectively. HBx is also involved in epigenetic modification, including methylation and acetylation. HBx interacts with various signal-transduction pathways, such as protein kinase B/Akt, Wnt/β-catenin, signal transducer and activator of transcription, and NF-κB pathways. Moreover, HBx affects cellular fate by shifting the balance toward cell survival. HBx may lead to the loss of apoptotic functions or directly contributes to oncogenesis by achieving transforming functions, which induce hepatocarcinogenesis. Additionally, HBx can modulate apoptosis and immune response by direct or indirect interaction with host factors. We conclude that HBx hastens the development of hepatoma. Hepatocellular carcinoma (HCC) is one of the most common malignant cancers worldwide and ranks the third in annual cancer mortality rates. The chronic infection of hepatitis B virus (HBV) has been identified as a major risk factor in the development of HCC, accounting for 55% of cases worldwide; 80% or more of such cases are found in the eastern Pacific region and sub-Saharan Africa, which are the areas with the highest tumor incidenceHBx is the smallest gene of the four partially overlapping ORFs of HBV. The genome comprises 452 nucleotides, which encode a 154-amino acid regulatory protein with a molecular mass of 17 kDaHepadnaviridae but not to avian virusesThe HBV genome is circular with partly double-stranded DNA. The long (minus) strand is approximately 3,200 bases in length and contains four open reading frames (ORFs), namely, S, C, P, and X. The enigmatic HBx protein is a transactivator that can activate various viral and cellular promoters and enhancers. HBx protein fails to bind directly to DNA, and its transcriptional activity is mediated via protein-protein interaction. The transactivation function of HBx may be exerted in the cytoplasm via signaling pathways and in the nucleus via DNA-binding proteins. HBx transcriptional activity is necessary for viral replicationHBx gene is usually conserved in the integrant and is often found in the malignant hepatocytes of chronic HBV carriers. Our groups identified the HBx gene inserted into the upstream of a 20-bp Alu core sequence with a secondary deletion of 382 to 465 bp at its 3' end, followed by subtelomeric DNA; HBx gene-integration causes the recombination of HBx/Alu core sequence/subtelomeric DNA in the host genomeet al.The clinical relevance of HBx begins with the integration of HBV DNA into the genome of the hepatocytes of chronic HBV carriers. The The perspective on gene regulation in biology has mostly focused on the protein-coding genes via the central dogma of DNA→mRNA→protein. However, with the development of new techniques, such as high-resolution microarray and massively parallel sequencing, a minimum of 90% of the human genome has been actively transcribed into ncRNAs, and less than 2% of the genome sequences encode proteinsSmad4Small ncRNAs include microRNAs (miRNAs), small-interfering RNAs (siRNAs), or PIWI-interacting RNAs. Among these small ncRNAs, miRNAs are the most detailed. MiRNAs are evolutionarily conserved noncoding RNAs (21 to 25 nt in length). These RNAs serve critical functions in gene expression regulation and multiple cellular processes, including proliferation, development, differentiation, and tumorigenesis. The alterations in miRNA expression have been observed in HCCs and are linked to the molecular pathogenesis of HCC because of their effect on the expression of crucial mRNAs. Depending on the target genes, miRNAs can function as tumor suppressor genes or oncogenes through mRNA cleavage or translational repression. HBx suppresses the p53-mediated activation of miR-148a, resulting in the upregulation of AKT and ERK and subsequent activation of mTOR to promote cancer growth and metastasis in a mouse model of HCCin vitro and in vivo and functions as a tumor suppressor in the development of HBV-related HCCLncRNAs are broadly defined as endogenous cellular RNA molecules that are longer than 200 nt. LncRNAs are poorly conserved and can regulate gene expression at various levels, such as during chromatin modification, transcription, and post-transcriptional processing. An increasing amount of evidence showed that lncRNAs serve critical functions in the development and progression of HBV-related HCC. For example, highly upregulated in liver cancer (HULC) is dysregulated in HCC. HULC expression level is positively correlated with HBx expression in clinical HCC tissues. Moreover, HBx can upregulate the HULC expression in human immortalized normal liver L-O2 cells and hepatoma HepG2 cells. The upregulation of HULC by HBx can promote the proliferation of hepatoma cells by p18 suppressionEarly research on the pathogenesis of HBx-induced HCC and on other etiological forms of the tumor focused on the changes at genetic level. Recent studies showed that epigenetic changes, which inactivate tumor suppressor genes or chromosomal instability, serve an increasingly important function in hepatocarcinogenesisINK4Ap16 gene that negatively regulates cell cycleReversible DNA methylation occurs at the sites of cytosine-guanine dinucleotides (CpGs) and can be generated by adding a methyl group to the carbon-5 position of cytosine. DNA methylation patterns are established by a family of DNA methyltransferases (DNMTs). The HBx-induced upregulation of DNMT activity is implicated in the aberrant methylation of CpG islands in some host genes that are involved in tumor suppression. Moreover, these DNMT-mediated methylated genes serve a function in cell cycle regulation, cell growth, dedifferentiation, invasiveness, apoptosis, immune escape, and xenobiotic metabolism, thereby contributing to HBV-related HCC onset and/or progressioncadherin-1 (CDH1) genes involves the recruitment of the transcription factor mSin3A/HDAC1 complex to the promoters of the Snail-binding sites in human CDH1HCC usually occurs with aberrant acetylation, particularly histone acetylation. Such abnormal histone modifications perturb cellular gene expression, thereby disrupting normal cellular activities. Acetylation is controlled by histone acetyltransferases and histone deacetylases. HBx can promote HBV-induced HCC pathogenesis by inducing the histone acetylation of some tumor promotion-related genes. For example, HBx can strongly induce the expression of HDAC1 at transcription level, thereby enhancing the stability of hypoxia-inducible factor-1α, which may serve a critical function in the angiogenesis and metastasis of HBV-associated HCCsNAD-dependent deacetylase sirtuin-1 (SIRT1), a member of the class III histone deacetylases, is conserved during evolution of different species, from yeasts to humans. SIRT1 is implicated in several biological processes, including metabolism, cell division, differentiation, survival, and senescenceAs one of the most versatile kinase families, Akt  serine–threonine kinases critically regulate cell survival, proliferation, metabolism, and migration. The dysregulation of Akt kinases is frequently associated with human diseases, such as cancerWnt pathway, an essential regulator of early embryonic development, contributes to somatic stem cell homeostasis. The canonical Wnt signal is initiated by the binding of Wnt ligands to the receptor Frizzled to activate Dishevelled (Dvl) proteins. Dvl transduces Wnt signal by suppressing the degradation of β-catenin via the adenomatosis polyposis coli (APC)/Axin/GSK3β destruction complex. The accumulation of β-catenin in the cytosol leads to its translocation into the nucleus, where the transcription of Wnt target genes is initiatedSTAT pathway serves a crucial function in the regulation of many key cellular processes, such as survival, proliferation, and differentiation. In the liver, the activation of STAT proteins, including STAT3 and STAT5, is critical for anti-viral defense against hepatitis viral infection and for controlling injury, repair, inflammation, and tumorigenesisHBx is an activator of the transcription factor NF-κB, which is the first HBx-responsive motif to be identifiedHBx gene expression can disrupt the normal cell growth in transgenic micein vivo and in vitro. HBx can increase the transcription of NF-κB, AP-1, and survivin to upregulate the expression levels of c-Myc and survivin. Abnormal centrosome duplication and activated human telomerase reverse transcriptase are responsible for the transformationHBx gene in normal cells strongly affects cell cycle progression and growth advantage, which promotes cell transformationThe hepatocarcinogenic effect of HBx on HBx-transgenic mice has been investigated. Programmed cell death (apoptosis) is necessary for the elimination of redundant, damaged, and virally infected cells. Generally, proteins regulate apoptosis in three ways, as follows: as effectors and initiators of apoptosis, as inducers and suppressors of apoptosis, and as intermediate proteinsin vitro and in vivo evidence that HBx can enhance the susceptibility of hepatocytes toward oxidative stress-induced apoptotic killing by accelerating the loss of Mcl-1 protein, which is mainly caspase-3 dependentCaspases are a group of cysteine-containing proteases responsible for cellular maturation and reconstruction, number and quality of cell regulation, and apoptosis initiation of old cells or those that cannot perform their normal rolesHBV infection alters mitochondrial metabolism. The selective removal of damaged mitochondria is important for maintaining mitochondrial and cellular homeostasis. Raf serine/threonine kinases 1 (Raf-1) can translocate to the mitochondria, thereby protecting the cells from stress-mediated apoptosis. Recent studies showed that HBx stimulates the mitochondrial translocation of Raf-1, thereby contributing to the anti-apoptotic effectPDCD4 was originally identified as a tumor-related gene in humans. PDCD4 is expressed ubiquitously in normal tissues, and the highest level is found in the liver. PDCD4 is a proapoptotic molecule involved in the TGF-β1-induced apoptosis in human HCC cells and a possible tumor suppressor in hepatocarcinogenesisTo successfully detect and eliminate invading pathogens by discriminating self from non-self, the mammalian immune system develops mechanisms, which comprise two distinct components, namely, the innate immunity and the adaptive immunity. In most multicellular organisms, the highly conserved innate immune system provides the first line of defense to limit infection by detecting pathogens using germline-encoded proteins58. Further analysis on clinical samples revealed that MAVS protein is downregulated in the HCCs of HBV origin; this downregulation is correlated with the increased sensitivities of the primary murine hepatocytes isolated from HBx knock-in transgenic mice upon vesicular stomatitis virus (VSV) infectionsThe innate immune system is characterized by the production of type I interferon-α/β (IFN-α/β) cytokines and the activation of NK cells; this immune system has important roles in detecting and eliminating invading pathogens. Previous studies reported that HBx interacts with mitochondrial antiviral signaling (MAVS) and promotes the degradation of MAVS through Lys136 ubiquitin in MAVS protein to prevent the induction of IFN-βin vivoCD8+ T cells contribute to the clearance of HBV infection, and the lack of CD8+ T cells may be among the major factors that cause chronic HBV infection and HCC development. HBx expression induces the low production of interferon-γ and the apoptosis of CD8+ T cells without affecting CD8+ T cell proliferationin vivo should be the focus of future studies.Overall, we summarized the mechanism of HBx in hepatocarcinogenesis. HBV-associated hepatocarcinogenesis can be viewed as a multi-factorial process, which includes direct and indirect mechanisms that may act cooperatively (