MORG1 gene. Among other transcriptional factors binding sites, we identified that both (mouse and human) promoter regions contained several putative hypoxia-inducible factor binding motifs. Analyses of the human MORG1 promoter by reporter assays revealed that hypoxia and pharmacological inhibitors of prolyl-hydroxylases under in vitro conditions in HEK 293 cells differentially regulate the MORG1 promoter reporter activity. The exposure of the cells to 10% hypoxia showed inhibition of MORG1 promotor activity at 6 and 12 h, but stimulation after 24 h while treated with prolyl-hydroxylase inhibitors led to a time-independent MORG1 promoter activation. Mutational analyses of the individual HIF binding sites on human MORG1 promoter suggest that the binding sites work in a complex corporation because single mutations were not sufficient to abolish completely the MORG1 reporter activation by PHD inhibitors. Our data provide the first evidence that not only MORG1 regulate HIF stabilization through a PHD complex, but also that, vice versa, HIFs control MORG1 expression directly or indirectly by a complex regulatory mechanism.MAPK-organizer 1 (MORG1) is a molecular scaffold for prolyl-hydroxylase-3 containing a domain (PHD3) protein linking MORG1 to mechanisms of adaptation in hypoxic conditions. In this paper, we report the cloning of the promoter region of the murine and human  The MAPK organizer-1 (MORG1) protein belongs to the family of the WD40 repeat domain-containing proteins  and are MORG1+/− mice improved renal damage in animal models of type 1 and type 2 diabetes mellitus [Hypoxia often occurs because of a microcirculation injury and hypoperfusion in tissues and organs, including the kidney . Hypoxiamellitus ,18 and amellitus . Thus, o5 cells per well were seeded into 12 well plates. On the next day, the medium was replaced with DMEM, containing 1 g/L glucose, supplemented with 5% heat inactivated fetal bovine serum,  and penicillin/ streptomycin. To study the effect of hypoxia on the MORG1 expression in HEK 293 cells, they were exposed for 6, 12 or 24 h to 10% O2 in hypoxia cell culture incubator from TermoScientific , containing 5% CO2, 10% oxygen and 90% nitrogen gas. In addition to hypoxia, we studied the effect of two HIF-prolyl-hydroxylase (PHDs) inhibitors: 500 µM 3,4-dihydroxybenzoic acid  and 500 µM L-mimosine, both purchased from Sigma-Aldrich . The regulation of the human MORG1 expression was studied in parental human HEK 293 cells . HEK 293 cells were cultured in DMEM  containing 4.5 g/L glucose, supplemented with 10% heat inactivated fetal bovine serum , 100 U/mL penicillin and 0.1 mg/mL streptomycin . For analyses, 3 × 10® 480 SYBR Green I Master Kit . The amplifications were performed using a Q-Tower thermocycler . The sequences of the primers used for analyses were as follows: human gapdh forward 5′-GGAGTCAACGGATTTGGTCG-3′, reverse: 5′-CAGTGGACTCCACGACGTAC-3′, human MORG1 forward 5′-AGGAATACAAGCTGGACTG-3′, reverse: 5′-AGCTACGTCTCTAGAATAAGATGG-3′, human VEGFA forward 5′-TTGCCTTGCTGCTCTACCTCCA-3′, reverse 5′-GATGGCAGTAGCTGCGCTGATA. The relative gene expression was quantified by ΔΔCT method, and the relative expression ratio was calculated according to Livak et al. [Ratio  =  2−ΔΔCT [Total RNA was isolated from HEK 293 cells using the RNeasy kit , according to the manufacturers’ recommendation. To assess the quality of the RNA, gel electrophoresis with RNA stained with SYBR Green dye (Molecular Probes) was performed on samples. RNA concentrations were determined, and one µg total RNA was reverse transcribed using an M-MLV-RT kit  for 1 h at 37 °C. The gene expression was estimated by real-time PCR (RT-PCR) in a multiplex assay as previously described  using 0.k et al. , where R  2−ΔΔCT . The pri  2−ΔΔCT ,181 genomic sequence upstream of the ATG codon was amplified from a DNA isolated from a BAC clone pMQ155E2 purchased from Source Bioscience, Nottingham, U.K., containing the murine genomic MORG1 clone with the following primers: forward primer sequence 5′ GCAGCTGAGGGAGGGCGGA3′ and reverse primer sequence 5′ TGCATCAGTCCGCTGCCT 3′. The fragment was amplified with PhusionTM High-Fidelity DNA polymerase . After that, the PCR product was digested with HindIII endonuclease enzyme; the location of the internal HindIII restriction side is underlined on the murine promoter sequence. Next, the fragment was gel purified and cloned into the digested with SmaI and HindIII promoter-less pGL2-basic vector (Promega), containing the firefly luciferase gene as a reporter gene. The corresponding sequence of the murine MORG1 promoter was confirmed by sequencing. A fragment corresponding to 1473 bp of the 5′ untranslated region of the murine MORGMORG1 promoter by the generation of point mutations in the putative HRE binding sites, where the A/GCGTG was mutated to A/GAAAG in the corresponding constructs. The point mutations were generated using 100 ng of the WT MORG1 promoter reporter construct as a template, 5 U/reaction of Pfu-Turbo DNA polymerase  and 125 ng of each forward and reverse primer, caring the mutated site of the putative human MORG1 hypoxia-responsible element binding site (HBS). The reactions were performed in a final volume of 50 µL. The primer sequences used for the mutagenesis are presented in DpnI restriction endonuclease  for 3 h at 37 °C and the enzyme activity was inactivated for 15 min at 75 °C. 5 µls of the DpnI treated reactions were transformed into E. coli XL-1 Blue competent cells. Plasmid DNA was isolated from several colonies and subjected to sequencing using the sequencing primers pGL2 forward: 5′ CAATGTATCTTATGGTACTG 3′ and pGL2 reverse: 5′ CCTTATGCAGTTGCTCTCCA 3′. For simplicity, the constructs of the human MORG1 promoter caring the corresponding single mutations were termed HBS A, HBS B, HBS C and HBS D, respectively. For the generation of the double HBS-binding site mutations of the hMORG1 promoter, the plasmids caring the single HBS A, HBS B and HBS C mutants were digested with XhoI and HindIII endonucleases . The corresponding 496 bp fragment, containing the mutated sites, was gel purified and ligated into XhoI and HindIII digested HBS D vector plasmid containing the HBS D single mutation. Thus, were generated the MORG1 promoter constructs caring double mutations as follows: HBS A + D, HBS B + D and HBS C + D; the corresponding reporter constructs of the human MORG1 promoter were named thereafter as the double sites HBS A + B, HBS B + D and HBS C + D. All mutations were confirmed by sequencing analyses.Subsequently, we performed a specific analysis of the hypoxia-inducible factor binding sites on the hMORG1 promoter 3 × 105 HEK 293 cells were seeded into 12 well plates as described above and transiently transfected on the next day in DMEM medium, containing 1 g/L glucose, 5% heat inactivated serum and antibiotics. For transient transfections, 2 µg of the corresponding human WT MORG1 promoter reporter constructs or mutants of the hMORG1 promoter reporter plasmid were co-transfected with 0.5 µg of β-galactosidase control plasmid , using TurboFect transfection reagent  for 12 h. Thereafter, a fresh DMEM medium, supplemented with 1 g/L glucose, 5% heat inactivated serum, antibiotics and the corresponding stimuli, was added and the cells were incubated for different time periods at the absence  or at the presents of stimuli as shown under the figure legends. Finally, the cells were washed with sterile PBS and lysed in 100 µL of cell lysis reagent (Promega). The cell lysates were vortexed and centrifuged at 13,000 rpm for 15 min. The luciferase activity was determined on a Tecan Infinite M200  with a luminometer mode using 20 µL of the cells lysates and 100 µL firefly luciferase substrate (Promega). The ability of 3,4-DHB, L-mimosine and hypoxic conditions to induce HIFs activation was assessed by transient transfection of the pHIF-Luc reporter plasmid  [pHIF-Luc reporter activity was assayed as described above.To analyse the transcriptional activation of the h3VaO4 (Sigma-Aldrich). The protein concentration was detected in DeNovix, DS-11FX Spectrophotometer/Fluorometer  under UV 280 mode. Protein expression was detected with ECL reagent  and documented with a Fusion FX7 EDGE imaging system . Protein expressions were measured via ImageJ software and normalized to the corresponding loading control protein. For the detection of MORG1 protein, a rabbit polyclonal antibody generated in Biotrend  was used in dilution 1: 1000. The equal loading of total protein or cytosolic fraction protein was monitored by the detection of vinculin expression using a mouse antibody to vinculin 1: 1000 dilution . For detection of HIF-1α protein  and for HIF-2α, an antibody from Novus Biologicals  was used. The PCNA (Proliferating cell nuclear antigen) antibody was purchased from Santa Cruz Biotechnology Inc. and it was used in dilution 1:500. The corresponding secondary antibodies were used in dilution 1:2000 purchased from KPL . The protein expression was analyzed using Western blots after separation of proteins on 10 or 12% SDS-PAGE gels. After stimulations as appropriate, the cells were washed with PBS and lysed in complete lysis M buffer, supplemented with protease inhibitors  and 100 mM of NaThe primers for electrophoretic mobility assay were labelled using BIO 3′-END oligonucleotide labelling kit with Biotin 11-UTP , according to the manufacturer’s instructions. In brief, 5 pmol ssDNA oligonucleotides were labelled with 50 pmol Biotin 11-UTP by terminal deoxynucleotidyl transferase (TdT) for 30 min at 37 °C. We evaluated the Biotin 11-UTP labelling by dot blot analyses. Afterwards, two complementary ssDNA oligonucleotides (sense and anti-sense) were annealed in annealing buffer containing: 10 mM TRIS-HCl pH 7.5; 1 mM EDTA and 50 mM KCl in a heating block at 95 °C for 5 min, then the mixture was allowed to cool down to 25 °C. MORG1 putative HIF binding sites by electrophoretic mobility shift assay (EMSA). Nuclear protein extracts for EMSA were prepared using a mammalian nuclear and cytoplasmic protein extraction kit , as recommended by the manufacturer. The protein concentration was measured in DeNovixDS-11FX Spectrophotometer  and the proteins were kept frozen at −20 °C until the analyses. The EMSA binning reactions were performed for all four putative HBS of the hMORG1 promoter region by the aid of an active motif non-radioactive assay kit . The reactions were performed in final volume of 20 µL and usually contained 10 µg of nuclear extracts, 1 µL of 10× reaction binding buffer, 1 µL of 2.5 mg/mL BSA, 1 µL of 50% glycerol, 2 µL of 0.1 mg/mL dI-dC, and 2 µL of 3′-end Biotin-11 UTP labelled dsDNA oligonucleotides. After that, the reactions were incubated for 30 min at room temperature and loaded on a 6% non-denaturing PAGE. The gel was transferred via semi-dry transfer system  on a positively charged membrane for 1 h. Next, the membrane was removed and placed side down on a UV-transiluminator Vilber  equipped with 4 × 8 W 365 nm UV-lamp and cross-linked for 15 min. The membrane was blocked for 1 h in blocking buffer, followed by 1 h incubation with a stabilized Streptavidin–HRP conjugate in 1:330 dilution in blocking buffer. The membrane was washed 4 times each 5 min with washing buffer. Finally, the membrane was equilibrated for 10 min in equilibration buffer and incubated with an EMSA-ECL solution for 10 min. The signals were visualized on a Fusion FX7 EDGE imaging system . All buffers, the streptavidin–HRP conjugate and ECL reagents were provided with the active motif assay kit . For competitor studies to the binding reactions, either 100-fold excess of the corresponding unlabeled dsDNA oligonucleotides or 10-fold excess of unlabeled mutated dsDNA oligonucleotides were added. For super shift analyses, 1 µL of HIF-1α of HIF-2α antibodies from R&D System  were added for 15 min, prior to addition of the 3′-end Biotin-11 UTP labelled dsDNA oligonucleotides, followed by incubation for an additional 30 min at RT. The sequences of the oligonucleotides used in the EMSA analyses are shown in We further analyzed the p  <  0.05. * p  <  0.05; ** p  <  0.01. *** p  <  0.001. Statistical analyses were performed with the statistical package SigmaPlot 14.5 software . The results were evaluated by Analysis of Variance (ANOVA) followed by a t-test to analyze the differences between the two groups. The data are graphically presented as a box plot, where the values are shown as the median and percentiles, and to the box plot was added additional a vertical point plot of all sample’s value. In the bar graphs, the values are presented as mean  ±  standard error of mean (SEM). Differences were considered significant when MORG1 promoter, we cloned the murine and human promoter regions of MORG1 genes. We amplified 1473 bp fragment of the 5′ untranslated region of the murine MORG1 gene using a BAC clone pMQ155E containing the murine MORG1 genomic clone. The DNA sequence of the murine MORG1 promoter is shown in To study the regulation of the MORG1 promoter region was amplified from genomic DNA originated from human HEK 293 cell line. The DNA sequence of the human MORG1 promoter is present in Furthermore, a 1512 bp fragment containing the 5′ untranslated region up stream of the ATG codon of the human MORG1 genes, we searched for a reliable identification of the transcription factor binding sites in the DNA sequence of the murine and human promoters. To predict binding sites of the putative transcription factors in murine and human MORG1 promoter regions, we used the ALGGEN-PROMO link [MORG1 promoters DNA sequences are shown in MORG1 promoters contain several hypoxia-responsible element (HRE) binding sites. The murine promoter contains three HRE binding motives characterized with the canonical hypoxia-inducible factor consensus sequence 5′-GCGTG-3′, which are shown in MORG1 promoter DNA sequence are located at positions −339 to −343, −379 to −383 and −1292 to −1296 relative to the ATG codon. The analyses of the putative HRE binding elements on the human MORG1 promoter region revealed four consensus hypoxia-inducible transcription factors binding sites. Three of them are located in the proximal promoter region corresponding to positions −67 to −71, −334 to −339 and −383 to −388 relative to the ATG codon, and one HRE is located in the distal promoter at position −1003 to −1007 , while 3,4-DHB and L-mimosine significantly rose hMORG1 expression (p < 0.001). The exposure of the HEK 293 cells for 24 h at 10% hypoxia O2 supply also inhibited MORG1 gene expression levels relative to control (p < 0.001). On the other hand, both PHD inhibitors significantly induced hMORG1 expression in HEK 293 cells after 24 h treatment (p < 0.001). To assess whether HIF target gene transcription was affected under the same experimental conditions, we examined the mRNA expression of the human VEGFA from the same cDNAs at 12 h and 24 h periods. Our data revealed that hVEGFA expression was significantly elevated under 10% hypoxia after 12 h (p < 0.001) as well as after 24 h 10% hypoxia treatment relative to control, normoxic cells exposed to 21% oxygen (p < 0.001). Furthermore, the application of both PHD inhibitors 3,4-DHB and L-mimosine also significantly elevated the expression of hVEGFA mRNA in HEK 293 cells (p< 0.001). Likewise, L-mimosine was a more potent inducer of hVEGFA mRNA expression, while 3,4-DHB produced a stronger hMORG1 mRNA expression.In this study, we further investigated the effect of hypoxic conditions and hypoxia mimetics only on human MORG1 in HEK 293 cells. We first assayed whether hp < 0.05). The exposure of the cells to the PHD inhibitors 3,4-DHB (p < 0.05) and L-mimosine (p < 0.05) also significantly increased the expression of the MORG1 protein.We further evaluated the protein expression of human MORG1 in HEK 293 cells using Western blots. The analyses of the protein expression in cells treated for 24 h with 10% hypoxia, 3,4-DHB or L-mimosine revealed that 10% hypoxia elevated the protein expression of MORG1  [MORG1 promoter and its involvement in the hypoxia pathway through interaction with PHD3 [MORG1 promoter under hypoxic conditions. It was shown recently that the binding of hypoxia-inducible transcription factors HIF-1α and HIF-2α necessarily correlates with the degree of hypoxia [MORG1 promoter reporter activity and transiently transfected the wild-type hMORG1 reported construct in HEK 293 cells, which were left untreated or exposed to 10% hypoxia, in a hypoxia-cell culture incubator for 6 h, 12 h or 24 h. Our results reveal that, at 6 h, 10% hypoxia significantly inhibits hMORG1 reporter activity compared with controls exposed to 21% O2 (p < 0.001). Similarly, the exposure of the cells for 12 h to 10% hypoxia also decreased hMORG1 promoter reporter activity (p < 0.001) relatively to normoxia-treated cells. Interestingly, 10% hypoxia upon 24 h significantly elevated MORG1 promoter activity in HEK 293 cells (p < 0.05).We further continue our evaluation of the 3 cells) . The finith PHD3  prompted hypoxia . TherefoMORG1 promoter reported activity (MORG1 activity starting with 6 h time point (p < 0.01), while L-mimosine was less effective at the early time point. A longer incubation with the stimuli for 12 h revealed that 500 µM L-mimosine similarly to 500 µM 3,4-DHB significantly increased hMORG1 reporter activity (p < 0.001) relative to untreated cells. We detected a sustained activation of the PHD inhibitors from 24 h up to 48 h, with a single dose applied to the cells (p < 0.001) when compared to controls. In contrast to hypoxic conditions, the application of the PHD inhibitors significantly elevated the wild-type hMORG1 reporter activity. Although the 3,4-DHB application was stimulated at earlier time points than L-mimosine MORG1 promoter activation, with longer stimulation periods, L-mimosine had an equally enhancing effect as 3,4-DHB on the wild-type hMORG1 reporter activity. We did not observe a significant difference in hMORG1 promoter driven luciferase activity after 12 h, 24 h or 48 h, when the cells were stimulated with either PHD inhibitor.We evaluated the application of two small-molecules, 3,4-dihydroxybenzoic acid  and L-mimosine, which were already shown to effectively induce HIF accumulation and activation . The appactivity a–d. 3,4-MORG1 reporter activity, and especially, 3,4-DHB was more effective than L-mimosine in inducing hMORG1 promoter reporter activity at an early time point, raised the question of whether an application of 3,4-DHB during hypoxic conditions could reverse the inhibitory effect of 10% hypoxia on hMORG1 promoter reporter activity. First, we tested the ability of co-treatment of the cells with 500 µM 3,4-DHB and 10% hypoxia for 6 h period. We observed that the simultaneous application of 3,4-DHB and 10% hypoxia was able to reverse the inhibitory effect of hypoxia on human MORG1 promoter activity . We next tested whether hypoxic inhibition on the MORG1 reporter activity could be reversed after 12 h co-treatment with 3,4-DHB and 10% hypoxia. We measured a small, but significant, increase in the hMORG1 promoter activation when 3,4-DHB was added to 10% hypoxic conditions in HEK 293 cells . Nevertheless, 3,4-DHB was not able to reverse the hypoxia inhibitory effect after 12 h to levels comparable with 3,4-DHB alone . Similarly, 10% hypoxia also notably elevates pHIF-Luc reporter activity (p < 0.001). Considerably induced pHIF-Luc reporter activity was also detected in HEK 293 cells exposed for 12 h to 3,4-DHB or L-mimosine (p < 0.001). Furthermore, the exposure of the cells to 10% hypoxia for 12 h was also able to significantly induce HIF-driven luciferase activity (p < 0.001). Thus, the 10% hypoxia and PHD inhibitors used in our experimental settings can induce the activity of HIFs in HEK 293 cells. We also measured a stronger activation of the pHIF-Luc activity in L-mimosine treated cells compared with 3,4-DHB application or hypoxic conditions. Therefore, HIFs are activated from both mild hypoxia as well as by exposure to pharmacological inhibitors of PHD enzymes, although we measured a higher activation by hypoxia mimetics, compared to hypoxic conditions. Thus, our results support previous reports that 3,4-DHB and L-mimosine are capable to activate HIF reporter activity [To verify that under our experimental conditions HIFs are activated, we assayed the ability of a commercially available pactivity .MORG1 promoter to investigate the functional significance of the HBS A, HBS B, HBS C and HBS D on hMORG1 promoter activity. First, we performed single mutations of all four sites and analyzed their reporter activity in transiently transfected HEK 293 cells. The ability of the single mutants to affect hMORG1 promoter activity was assayed for 12 h in control, 10% hypoxia-, 3,4-DHB- and L-mimosine-treated cells. Single mutants HBS A and HBS B significantly increased the 3,4-DHB- and L-mimosine-dependent activation of the hMORG1 promoter in HEK 293 cells, compared to wild-type hMORG1 promoter activity under the same experimental conditions (p < 0.001). We next performed a site-directed mutagenesis of the four putative HBS in the wild-type hMORG1 promoter-dependent reporter activity in the presence of 3,4-DHB or L-mimosine (p < 0.001). Similar effects were observed when the HBS D mutation was transiently expressed  in the hMORG1 promoter sequence. Our data revealed that the presence of the mutated HBS D site in all double mutants was able to inhibit the ability of the single HBS A and HBS B mutations to activate hMORG1 reporter activity in 3,4-DHB- or L-mimosine-stimulated HEK 293 cells. Therefore, the putative HBS D in the MORG1 promoter presumably positively regulates the human MORG1 promoter reporter activity by hypoxia mimetics. Our results support the hypothesis that HIF binding sites in the promoter collectively affect its reporter capacity. Similarly, the presence of the mutated HBS D in the double mutants HBS A + D and HBS C + D was able to inhibit the ability of the single HBS A and HBS C mutations to activate hMORG1 reporter activity in HEK 293 cells exposed to 10% hypoxia for 12 h. The observation that double mutations of putative hypoxia binding sites of the hMORG1 promoter, we assayed their binding ability by EMSA from HEK293 cells nuclear extracts prepared of untreated or stimulated cells with 3,4-DHB or L-mimosine or exposed to 10% oxygen supply for 12 h. We used 3′ 11-UTP Biotin labeled dsDNA oligonucleotides corresponding to HBS A  as a binding partner of several binding modules provoked an interest in the function and regulation of the MORG1 gene and protein expression in health and diseased conditions. Although studies from MORG1mellitus , it redumellitus  and showmellitus ; the mecmellitus , we idenmellitus . Furthermellitus  can resumellitus , diabetemellitus . This comellitus ,33,34. Tse cells . It is wse cells ,17,20, b hypoxia . Our datMORG1 transcriptional activity. Indeed, 10% hypoxia is approximately a 50% reduction of the normal oxygen supply. A very recent report demonstrated that even mild hypoxia can enhance HIF stabilization and the expression of VEGF and may induce destructive changes in kidney tissues [MORG1 reporter activation after 24 h exposure to mild hypoxia could be a result of the direct binding of HIFs to the putative HREs on the MORG1 promoter. This binding may enhance the promoter reporter activity at the latter time point because the association is likely time dependent, or it is a result of competitive binding. Although, in general, it is expected that the genes that are HIF targets in hypoxic conditions are positively regulated, characterized with an increased transcriptional activation , it was  tissues . On the  tissues ). It is 1 promoter activity starting at 6 h for the 3,4-DHB inhibitor. We detected a sustained hMORG1 promoter reporter activation at 24 h and 48 h with a single treatment of the reagent. Furthermore, the application of L-mimosine, another PHD unspecific inhibitor, was less effective at the early time point, but its effect was undistinguishable from the 3,4-DHB action at 12 h, 24 h or 48 h treatments. Interestingly, these data suggest that small PHD inhibitor molecules probably need more initial time until HIF transcription factors accumulate and promote the hMORG1 transcriptional activity. Therefore, HIFs could have a slower kinetic of accumulation at the presence of L-mimosine compared with 3,4-DHB treatment, which could be due to the slower effect of L-mimonsine on the suppression of the PHD enzymatic activity. Such a stronger, but slower, inhibitory action of L-mimosine on tyrosinase inhibition has already been reported [MORG1 transcriptional regulation. This hypothesis is also supported from our experiments to reverse the hypoxic inhibition of hMORG1 promoter by simultaneous exposure of the HEK 293 cells to 10% hypoxia and 3,4 –DHB. Although 3,4-DHB very potently reversed the hMORG1 promoter reporter activation after 6 h, it was not able to overcome the hypoxic inhibition of the hMORG1 promoter after 12 h co-treatment. This finding suggests that, at 12 h of mild hypoxia, different transcription factor(s) could be activated by HIFs indirectly, causing an inhibitory effect on MORG1 transcriptional activity. Both HIF-1α and HIF-2α are expressed in HEK 293 cell, but the kinetic how they associate with the putative MORG1 HRE and how are activated from hypoxia or 3,4-DHB alone or in simultaneous treatment is unknown at present.In contrast to hypoxia-dependent HIF activation, the suppression of HIF hydroxylation by PHD enzymes under normoxic conditions, by the application of pharmacological inhibitors of PHDs, was associated with an enhanced hMORGreported . Althougreported . Consequreported . Our datMORG1 promoter revealed that single HBS A or HBS C mutants were able to reverse the inhibitory effect of 10% hypoxia compared with the wild-type hMORG1 promoter reporter activity, suggesting that these sites have an inhibitory function on the hMORG1 promoter at 12 h treatments. We detected opposite results when hMORG1 reporter constructs carrying single mutation at the HBS B or HBS D were used. Interestingly, hMORG1 reporter constructs carrying mutation in the HBS A and HBS B further increased the activity of the mutated promoter’s reporter constructs, compared with the wild-type hMORG1 reporter activity, while HBS C and HBS D mutations were able to suppress the 3,4-DHB- or L-mimosine-dependent hMORG1 promoter activation at 12 h treatments. Therefore, these HIFs putative binding sites in the hMORG1 promoter could be more sensitive to the pharmacological activation of HIFs. The analyses of the hMORG1 promoter reporter constructs carrying two mutations revealed that the presence of the mutated HBS D binding site was sufficient to reduce the reverse the effects observed in the single mutants’ analyses. These data suggest that the distal putative HIF binding site in the hMORG1 promoter sequence HBS D could play an essential role for the HIF-dependent regulation of the hMORG1 promoter.Furthermore, mutational analyses of the putative HIF binding sites on the hMoreover, HIFα subunits can also be hydroxylated at asparagine 851 blocking interactions with coactivators, suggesting an additional mechanism of regulation .MORG1 promoter region contains four HIF binding sites, it is difficult to exactly determine whether there are preferential binding sites. It is expected that the proximal TF binding sites are probably positive regulators of the promoter activity, although we observed here that the HBS A presumable negatively regulate the hMORG1 promoter activity in hypoxic conditions and by the pharmacological activation of HIFs in normoxic conditions through the suppression of PHD enzymes. In addition, cis-active elements may build complexes with other trans-factor and/or coactivators, further adding to the complexity of the regulation [MORG1 regulates HIF stabilization through a PHD complex, but also, vice versa, HIFs control MORG1 expression directly or indirectly by a complicated regulatory mechanism, which needs to be further elucidated.EMSA assays confirmed the complex specificity between the hMORG1 promoter putative HIF binding sites and HIF-1α and/or HIF-2α TFs, but is not yet clear how the exact complex formation occurs in hypoxic conditions and the pharmacological activation of HIFs via PHD suppression under normoxic conditions. Because the hgulation . Further


Trypanosoma brucei nuclear organization and how it impacts gene expression, with an emphasis on antigen expression. It highlights several dedicated sub-nuclear bodies that compartmentalize specific functions, whilst outlining similarities and differences to more complex eukaryotes. Notably, understanding the mechanisms underpinning antigen as well as general gene expression control is of great importance, as it might help designing effective control strategies against these organisms.African trypanosomes are early divergent protozoan parasites responsible for high mortality and morbidity as well as a great economic burden among the world's poorest populations. Trypanosomes undergo antigenic variation in their mammalian hosts, a highly sophisticated immune evasion mechanism. Their nuclear organization and mechanisms for gene expression control present several conventional features but also a number of striking differences to the mammalian counterparts. Some of these unorthodox characteristics, such as lack of controlled transcription initiation or enhancer sequences, render their monogenic antigen transcription, which is critical for successful antigenic variation, even more enigmatic. Recent technological developments have advanced our understanding of nuclear organization and gene expression control in trypanosomes, opening novel research avenues. This review is focused on  Incertae sedis Eukarya (ex-Excavata) supergroup  in the mammalian host. The slender forms are capable of sensing the population density, which triggers differentiation into stumpy forms. The latter are pre-adapted to life in the tsetse, where they will eventually differentiate into the procyclic forms. In the mammalian host, besides the BSFs, these parasites can occupy multiple tissues , some recently identified as important reservoirs , approximately one-third of its genome   that share the same DNA elements, and yet, one is active whereas the remaining are silent – a classic epigenetic paradigm  is divided into three classes of linear chromosomes: 11 pairs of megabase chromosomes (at least 1 Mbp), one to five intermediate-sized chromosomes and more than 100 minichromosomes (50–150 kbp)  revealed that the transcribed chromosome core regions and the sub-telomeric regions coding for the large reservoir of silent VSG genes appear to fold into structurally distinct compartments  also function as architectural proteins in this organism , part of the inositol phosphate pathway, has been recently shown to interact with T. brucei (similarly to Plasmodium), Pol-II transcribed genes are located in the central core whereas the antigen genes are located in sub-telomeric regions , which in mammals is composed mainly by nuclear lamins. A growing number of nuclear proteins are known to bind lamins and are implicated in nuclear and chromatin organization, mechanical and genome stability, cell signalling, gene regulation, among others . Each NPC is located in and stabilizes an ~800 Å-wide nuclear pore, which is generated by the fusion between the inner and outer nuclear membranes . This symmetric core surrounds the central transport channel and functions as the scaffold onto which asymmetric nucleoporins attach on the cytoplasmic and nuclear compartments to form structures known as the cytoplasmic filaments and nuclear basket, respectively  and C-terminal (tail) domains, which self-assemble into higher-order structures whose basic subunit is a coiled-coil dimer  and two major B-type lamins (lamin B1 and B2) have been identified and characterized    into a primary transcript further processed into the mature 18S, 5.8S, 28S rRNAs . CBs are more abundant in cells with high transcriptional activity and are highly dynamic but structurally stable structures. They continuously exchange components into and out of the domain in response to changes in the cellular environmental  and small nucleolar RNAs (snoRNAs) and the biogenesis of nuclear RNPs, including some nucleolar proteins, snoRNPs and snRNPs -containing subnuclear site that did not colocalize with SL-RNA  copy number  and the upstream binding factor (UBF), are essential for Pol-I recruitment to the rRNA promoter . The interaction between Pol-I and SL1 is mediated by a single polypeptide named RRN3 in humans; a UBF dimer is further required to activate rRNA transcription. In yeast, RRN3 is conserved, whereas the three subunits of the core factor  and the six UBF subunits share no sequence similarity with the mammalian counterparts . Recent CryoEM studies suggest that, unlike the Pol-II system, promoter specificity relies on a distinct ‘bendability’ and ‘meltability’ of the promoter sequence that enables contacts between initiation factors, DNA and polymerase , RPA31, which is critical for Pol-I transcription and cell viability   and in the nucleolus; a blockade in TDP1 synthesis results in a pronounced reduction of Pol-I-derived transcripts ; these observations extend the roles of the Elp3-related proteins to Pol-I transcription units, as they are usually associated with Pol-II transcription in humans and yeast  has been identified in T. brucei Pol-I transcription identified so far are conserved among all trypanosomatids, suggesting that they fulfil general Pol-I functions -RNA, the latter is detrimental for trans-splicing.Pol-II synthesizes pre-mRNAs and U-rich short nuclear RNAs (snRNAs). The latter form the core of the spliceosome, involved in processing pre-mRNAs into mature mRNAs. In both cases, the 5′ ends are capped, which requires adding met al., T. brucei; RPB1, RPB2, RPB3 and RPB11 were also considered the functional and structural core  was found to be responsible for RPB1 phosphorylation, however, surprisingly, when silencing CRK9, there was no impact on Pol-II transcription or co-transcriptional m7G capping. Instead it led to a block of trans-splicing caused by hypomethylation of the SL-RNA unique cap4 , and a sixth protein (TFIIA1)  replaces a small percentage of thymine residues, mostly in telomeric regions and is synthesized at the DNA level via the precursor 5-hydroxymethyluracil. In T. brucei for instance, base J is exclusively present in the BSF. Notably, in T. brucei and Leishmania major, base J and H3.V are enriched at sites involved in Pol-II termination. Loss of base J and H3.V led to transcription read-through  and other cellular processes (7SL RNA). In SL-RNA must be added to the 5′ end of every single mRNA in T. brucei, trans-splicing relies on large quantities of SL transcripts generated by Pol-II transcription from a diploid tandem-repeat locus. Indeed, Pol-II largest subunit is highly concentrated at the SL-RNA genomic loci  , which results from a combination of high levels of transcription by Pol-I and multiple mechanisms of post-transcriptional control , designated the expression-site body (ESB), a transcription factory that contains a local reservoir of Pol-I  monogenic antigen transcription and (2) efficient mRNA splicing , which form discrete protein condensates in the nucleus in BSFs specifically, associate with the SL-RNA array and the active-VSG ES, respectively  and the SL-array.Notably, the single active-et al., et al., et al., et al., More recently, liquid–liquid phase separation (LLPS) has been proposed  3  can interact with the SL-RNA array in the mammalian-infective stage  as well as genes encoding for other highly abundant proteins interact with the SL-array, uncovering a potential enhancer-like mechanism , ChIP-Seq (chromatin-association) and CLIP-Seq (RNA-binding) have now been amply used in trypanosomes and other Trypanosomatids. More recently, ATAC-Seq (chromatin accessibility) and single-cell RNA-Seq have been performed in T. brucei, protein and DNA loci have been recently tracked at high resolution, using confocal-based or structured illumination microscopy (XY resolution 100–120 nm), which has been critical to characterize specific sub-nuclear compartments  of barcoded Oligopaint probes to enable the rapid visualization of multiple targeted genomic regions  /CRISPR-associated system (Cas) technology has revolutionized molecular biology; indeed, it is a powerful tool that allows highly efficient and reproducible manipulation of genomic sequences for both locus-specific and genome-wide approaches. But its huge potential is not exclusively linked to the site-directed nuclease activity. A catalytically inactive Cas9 (dCas9) can be used as a universal recruitment platform in order to control transcription, visualize DNA sequences or investigate et al., T. brucei, this could be used to bring genomic loci in proximity to the SL-array or the NL and assess how that impacts gene expression. Recently, a CasDrop system was designed to study the formation of phase-separated compartments in the nucleus by enabling liquid condensation of transcriptional regulators at target loci  system. It delivers a highly efficient and versatile control over the spatial positioning of genomic loci relative to specific nuclear compartments, including the nuclear periphery, CBs and promyelocytic leukaemia bodies to study how nuclear structure affects gene regulation and cellular function  of specific factors.et al., The molecular understanding of the mechanisms underpinning gene expression control in different developmental stages of these parasites is of great importance, as it might aid future vaccine and drug development efforts. For instance, acoziborole, a single-dose oral drug to treat trypanosomiasis, was shown to target cleavage and polyadenylation specificity factor 3 (Wall Notably, recent technological advances on sequencing, imaging and affinity purification techniques have led to important discoveries and paved the way to novel research avenues regarding nuclear organization and gene expression control in trypanosomes. Indeed, we live in exciting times where the pace of technology development is phenomenal and hopefully will allow us to address long-standing questions in infection biology that were previously inaccessible.


Chronic alcohol consumption is more frequently associated with advanced, aggressive hepatocellular carcinoma (HCC) tumors. Alcohol adversely impacts ER/Golgi membrane trafficking and Golgi protein N‐glycosylation in hepatocytes; these effects have been attributed (in part) to dysregulated adenosine diphosphate‐ribosylation factor (ARF) GTPase signaling. Here, we investigated the role of the ARF GTPase guanine exchange factor PSD4 in HCC progression.R‐based bioinformatics analysis was performed on publicly available array data. Modulating gene expression was accomplished via lentiviral vectors. Gene expression was analyzed using quantitative real‐time PCR and immunoblotting. PSD4 promoter methylation was assessed using quantitative methylation‐specific PCR. Phospho‐p65(S276)/DNMT1 binding to the PSD4 promoter was analyzed via chromatin immunoprecipitation. We constructed ethanol/DEN‐induced and DEN only‐induced transgenic murine models of HCC.We identified PSD4 as a hypermethylated, suppressed gene in alcohol‐related HCC tumors; however, PSD4 was not dysregulated in all‐cause HCC tumors. Certain HCC cell lines also displayed varying degrees of PSD4 downregulation. PSD4 overexpression or knockdown decreased and increased cell migration and invasiveness, respectively. Mechanistically, PSD4 transcription was repressed by TNF‐α‐induced phospho‐p65(S276)’s recruitment of DNA methyltransferase 1 (DNMT1), resulting in PSD4 promoter methylation. PSD4 inhibited pro‐EMT CDC42 activity, resulting in downregulation of E‐cadherin and upregulation of N‐cadherin and vimentin. Hepatocyte‐specific PSD4 overexpression reduced ethanol/DEN‐induced HCC tumor progression and EMT marker expression in vivo.PSD4 is a hypermethylated, suppressed gene in alcohol‐related HCC tumors that negatively modulated pro‐EMT CDC42 activity. Furthermore, we present a novel phospho‐NF‐κB p65(S276)/DNMT1‐mediated promoter methylation mechanism by which TNF‐α/NF‐κB signaling represses PSD4 transcription in HCC cells. PSD4 is a hypermethylated, suppressed gene in alcohol‐related HCC tumors. PSD4 decreases cell migration and invasiveness in HCC cells. PSD4 is epigenetically repressed by TNF‐α‐induced phospho‐p65(S276)’s recruitment of DNMT1. We also demonstrate that PSD4 functions as tumor suppressor in HCC cells via negatively modulating pro‐EMT CDC42 activity. Furthermore, we present a novel phospho‐NF‐κB p65(S276)/DNMT1‐mediated promoter methylation mechanism by which TNF‐α/NF‐κB signaling represses PSD4 transcription in HCC cells. These findings reveal that PSD4 may be a promising therapeutic target for alcohol‐related HCC.2t test was used. For comparison of multiple groups, a one‐way ANOVA with Bonferroni's post‐hoc testing was used. For comparison of qualitative variables, a Pearson χ2 or Fisher exact test was used. p < 0.05 was considered significant for all analyses.The Supporting Methods details the bioinformatics analysis, lentiviral constructs, qPCR, Western blotting, immunoprecipitation, immunofluorescence, cell assays, quantitative methylation‐specific PCR (qMSP), chromatin immunoprecipitation (ChIP) and Re‐ChIP assays, and the transgenic alcoholic diethylnitrosamine (DEN) murine model of HCC. Data are represented as means ± standard deviations (SDs) unless otherwise specified. SPSS was used to perform all statistical analysis. For comparison of two groups, a Student's 33.1GSE59261) derived from eight matched alcohol‐related HCC tumor (HCC) and normal liver control (CTRL) samples revealed 18 distinct gene co‐expression modules differentiating HCC from CTRL , 771 (26%) of which were profoundly downregulated (log2FC < −1.0). Segregating DEGs by module membership revealed nearly uniform suppression in M2 DEGs  and the immortalized hepatocyte cell line LO2. PSD4 was downregulated in HuH‐7, SK‐HEP‐1, and PLC/PRF/5 cells when compared to L02 cells Figure . In HepG3.3To examine the role of PSD4 in HCC tumorigenesis, we assessed the impact of PSD4 overexpression or silencing on HCC cell proliferation, migration, and invasiveness in vitro. We selected HepG2 and PLC/PRF/5 cell lines, which displayed the highest and lowest PSD4 expression, respectively, for further experiments. Based on their endogenous PSD4 expression levels, PLC/PRF/5 were transfected with a lentiviral vector (LvPSD4) to increase PSD4 expression, while HepG2 cells were transfected with one of two PSD4 shRNAs (shPSD4.1 or shPSD4.2) to decrease PSD4 expression Figure . LvPSD4 PSD4 is an ARF guanine nucleotide exchange factor (GEF) of ARF6; functionally, the EFA6B‐ARF6 dyad is necessary for tight junction maintenance.To determine whether CDC42 mediates PSD4’s inhibitory effects on HCC cell proliferation, migration, and invasiveness, PSD4 and CDC42 lentiviral vectors were transfected into PLC/PRF/5 and HepG2 cells. PSD4 and CDC42 modulation were confirmed via qPCR Figure . PLC/PRF3.4Having uncovered CDC42 as a key downstream mediator of PSD4, we next investigated potential upstream regulators of PSD4 in HCC cells. Our CemiTool R analysis revealed that PSD4’s gene module M2 contains the NF‐κB p100 (NFKB2) as a key hub gene Figure , a key rPrevious studies have shown that a specific phosphorylated form of the NF‐κB p65 subunit—phospho‐p65(S276)—functions as a transcriptional repressor downstream of TNF‐α/cAMP‐dependent protein kinase (PKA) signaling in cancer cells.3.5In vitro, TNF‐α increased PSD4 methylation in a time‐dependent manner in HepG2 cells ‐mediated methylation of the PSD4 promoter. By immunoprecipitation, we explored the interaction between p‐p65(S276) and the three major DNA methyltransferases in HCC: DNMT1, DNMT3A, and DNMT3B.The ENCODE ChIP‐seq database has identified the PSD4 promoter as a target of p65 (RELA),We next explored whether DNMT1 inhibition by 5‐AZA could restore PSD4 expression in WT p65‐overexpressing HepG2 cells and, consequently, the metastatic phenotype. 5‐AZA decreased DNMT1 protein levels but increased PSD4 gene and protein levels in WT p65‐overexpressing HepG2 cells under TNF‐α conditions Figure . Our dat3.7in vivo, we constructed hepatocyte‐specific Psd4‐overexpressing transgenic mice by placing the murine Psd4 cDNA under the control of the hepatocyte‐specific murine albumin (Alb) promoter. This Alb‐PSD4 transgene construct  and non‐transgenic (non‐TG) neonate littermates were screened for the Alb‐PSD4 transgene by qPCR /DNMT1 complex binding.Here, we demonstrate that PSD4 is downregulated in alcohol‐related HCC tumors and certain HCC cell lines. Moreover, below‐median PSD4 expression in HCC tumors is associated with inferior survival outcomes. Functionally, PSD4 inhibited HCC cells proliferation, migration, and invasiveness in vitro and suppressed alcohol‐induced HCC tumor progression TNF‐α promotes the migratory and invasive capabilities of HCC cells.Other than the ARF GTPase ARF6, little is known about the downstream targets of PSD4.in vivo HCC mouse models are based on intraperitoneal injection of diethylnitrosamine (DEN), a carcinogenic DNA alkylator.A number of In conclusion, this study identifies PSD4 as a hypermethylated, suppressed gene in alcohol‐related HCC tumors. We also demonstrate that PSD4 functions as tumor suppressor in HCC cells via negatively modulating pro‐EMT CDC42 activity. Furthermore, we present a novel p‐p65(S276)/DNMT1‐mediated promoter methylation mechanism by which TNF‐α/NF‐κB signaling represses PSD4 transcription in HCC cells. These findings reveal that PSD4 may be promising therapeutic target for alcohol‐related HCC.None.Conceived and designed the study: SJN. Performed the experimental procedures: SJN, SSP, LSX, and ZKL. Analyzed the data: ZKL and LYH. Drafted the manuscript: LYG.All protocols and procedures were approved by the Ethics Committee of the First Affiliated Hospital of Harbin Medical University. The established HCC cell lines HepG2 (HB‐8065), Hep3B, HuH‐7, SK‐HEP‐1, and PLC/PRF/5 and the hepatocyte cell line LO2 were obtained from American Type Culture Collection (ATCC) in October–November 2019. All cell lines were commercially authenticated by short‐tandem repeats (STR) genotyping in November 2019 by Guangzhou Jenniobio Co., Ltd. .Supplementary MaterialClick here for additional data file.


Aggressive behavior in pigs after mixing is an issue of animal welfare. This trial was conducted to evaluate the impact of the dopamine receptor (DR) D2 gene on aggressive behavior in pigs. We found that the core promoter region of the DRD2 gene is located in −2212~−1127 bp. A single-nucleotide polymorphism, rs1110730503, for the DRD2 gene is associated with aggressive behavior in pigs and influences the binding of the transcriptional factor interferon regulatory factor (IRF) 2. The transcriptional factor IRF1 upregulates the expression of the DRD2 gene; however, IRF2 downregulates the gene’s expression. IRF1 and IRF2 are functionally antagonistic to each other. Aggressive behavior in pigs is impacted by the DRD2 gene and its expression. These results provide insight into the genetics and neurophysiology of aggressive behavior in pigs.Aggressive behavior has negative effects on animal welfare and growth performance in pigs. The dopamine receptor D2 (DRD2) has a critical neuromodulator role in the dopamine signal pathway within the brain to control behavior. A functional single-nucleotide polymorphism (SNP), rs1110730503, in the promoter region of the porcine DRD2 gene was identified, which affects aggressive behavior in pigs. A chromatin immunoprecipitation (ChIP) assay was used to identify the interactions between interferon regulatory factor 1 (IRF1) and IRF2 with the DRD2 gene. The overexpression or knockdown of these two transcription factors in porcine kidney-15 (PK15) and porcine neuronal cells (PNCs) indicate that the binding of IRF1 to DRD2 promotes the transcription of the DRD2 gene, but the binding of IRF2 to the DRD2 gene inhibits its transcription. Furthermore, IRF1 and IRF2 are functionally antagonistic to each other. The downregulation of DRD2 or upregulation of IRF2 increased the apoptosis rate of porcine neuroglial cells. Taken together, we found that transcriptional factors IRF1 and IRF2 have vital roles in regulating the transcription of the DRD2 gene, and rs1110730503 (−915A/T) is a functional SNP that influences IRF2 binding to the promoter of the DRD2 gene. These findings will provide further insight towards controlling aggressive behavior in pigs. To improve the efficiency of space utilization and management, regrouping is a common practice in intensive pig farms . HoweverSingle-nucleotide polymorphisms (SNPs) located in noncoding RNA genes are classified functional SNPs which affect different biological processes and continually confer risk for multifactorial diseases . EmerginDopamine (DA) is an important neurotransmitter in the central nervous system, and the majority of DA synthesis occurs directly from tyrosine . It reguA series of proteins that specifically bind to DNA called transcription factors regulate the process of transcription from DNA to mRNA . SNPs loOur bioinformatics prediction analysis found that transcription factors IRF1 or IRF2 could bind to the promoter of the porcine DRD2 gene. Therefore, we hypothesized that the SNPs located in the promoter region of porcine DRD2 gene changed the binding sites of transcription factors IRF1 or IRF2, which could influence the aggressive behavior of pigs through the DA signaling pathway and immune pathway. The objective of this study is to identify the SNPs of the DRD2 gene that are involved in the regulation of IRF1 and IRF2 binding, as well as to explore the molecular regulation mechanism of the DRD2 gene.This study was approved by the Animal Care and Use Committee of Nanjing Agricultural University (SYXK Su 2017-0027).The animal trial was carried out at Huaiyin pig breeding farm, Huai’an, Jiangsu Province. A total of 500 piglets were randomly selected from 65 litters of multiple sows. Piglets were weaned at 35 days of age, then nine or ten weaned piglets from different litters were mixed in pens of dimension 2.5 m × 2.2 m. The pens were equipped with slatted floors, stainless steel feeders, and nipple drinkers to allow ad libitum access to feed and water. The pigs’ behavior was recorded for 72 h after mixing, with a digital video recording system . All pigs were marked with different numbers on their backs using a spray paint, for individual identification in the video record. Aggressive behavior was assessed by observing the video and recording the frequency and duration of active attacks, bullying, and stand-off behaviors for each pig in a pen for 72 h after mixing . A compoThe total DNA was extracted from ear tissue of piglets by standard phenol/chloroform method . Specific primers  were deshttps://www.ncbi.nlm.nih.gov/) (9 July 2020) and ENSEMBL (http://asia.ensembl.org/index.html) (9 July 2020) websites. The upstream sequence of the porcine DRD2 gene was used for promoter prediction by online Neural Network Promoter Prediction (http://www.fruitfly.org/seq_tools/promoter.html) (9 July 2020) [http://www.cbs.dtu.dk/services/Promoter/) (9 July 2020) [http://jaspar.genereg.net/) (9 July 2020) and ALGGEN-PROMO  (9 July 2020) [http://www.protocol-online.org/cgi-bin/prot/view_cache.cgi?ID=2943) (9 July 2020) and Meth Primer (http://www.urogene.org/methprimer/) (9 July 2020) [The gene sequences for DRD2 (ENSSSCG00000015048), IRF1 (ENSSSCG00000014277), and IRF2 (ENSSSCG00000015782) were downloaded from the NCBI (ly 2020)  and Promly 2020) . Transcrly 2020) ,34. The ly 2020) .™ SoSoo Cloning Kit (Tsingke Biotechnology) and mutagenic primers  and wild-type (containing the transcription factor binding sites) DNA fragments of DRD2 promoter were amplified from pig genomic DNA using the primers  and inse primers . The ove®ACS-4004™) and 293T (ATCC®ACS-4004™) cells were cultured in high-glucose medium with 10% fetal bovine serum (FBS) (10%FBS + 90%DMEM)   at 37 °C and 5% CO2. To isolate and culture porcine neuronal cells (PNCs) briefly, the porcine brain tissue was obtained from a newborn piglet, then the piglet was anesthetized by ethyl ether and euthanized. Dissected brain tissue was then cut into small pieces (about 1 mm3), and was digested with Papain  for 30 min. Then, the cells were seeded with high-glucose medium (20%FBS + 80%DMEM). After 48 h, the cells were cultured with Dulbecco’s minimum essential medium/nutrient F-12  supplied with 15% FBS at 37 °C with 5% CO2. When the number of cells grew by approximately 70~80%, they were transfected with plasmid DNA or small interfering RNAs (siRNA) utilizing Lipofectamine 2000 . After 24 h, the 293T and PK15 cells were collected using luciferase assay buffer . The luciferase activity of cell lysates was assayed by Promega dual luciferase assay system. Porcine nerve cells were transfected with plasmid DNA or small interfering RNAs (siRNA) utilizing Lipofectamine 3000 . All transfection/silencing experiments were repeated three times.Porcine kidney-15 (PK15)  at 260/280 nm. Total RNA was reversely transcribed into cDNA with the HiScript III RT SuperMix . Removal of residual genomic DNA and synthesis of cDNA were performed with the HiScript III RT SuperMix (Vazyme Biotech). RT-qPCR was performed on a QuanuStudio 5 using SYBR Green Master Mix (Vazyme Biotech). Relative expression levels were calculated using the 2−ΔΔCt method [Total RNA was isolated from transfected cells and different tissues  of the Suhuai pigs, using TRIzol  according to the manufacturer’s instructions . The purt method . Gene exv/v) . Total protein extracts were separated using 4% to 20% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS–PAGE) gels  and blotted onto polyvinylidene fluoride (PVDF) . The membranes were blocked with 5% bovine albumin (BSA)  at 4 °C for 2 h, then incubated overnight with the following primary antibodies: immunoreactive proteins detected with rabbit polyclonal antibody to DRD2 , IRF1 , IRF2 , and rabbit polyclonal antibody to GAPDH . The suitable secondary antibody used was anti-rabbit , and chemiluminescence was detected by Image LAS-4000 system. The band density was analyzed by ImageJ 1.53c software.Cell protein lysates were collected with radio immunoprecipitation assay (RIPA) buffer  and 1% protease inhibitors (PMSF) . Then, the cells were incubated with one of the following primary antibodies: anti-microtubule-associated protein 2 (anti-MAP2) ; or anti-tubulin (anti-TUJ1) , at room temperature for 1 h, washed with PBS, and incubated with fluorescein-conjugated goat anti-rabbit secondary antibodies  for 1 h. Nuclei were stained using 4,6-diamidino-2-phenylindole (DAPI) (GeneCopoeia). Using a confocal laser microscope , randomly-selected fields were evaluated for each slide for the strength of the staining signals. The number of positive pixels in each field is an indicator of the strength of the staining signal and level of gene expression.PNCs were seeded on coverslips and cultured routinely. After 72 h, when the cells reached 70% confluence, they were fixed for 15 min in 4% paraformaldehyde . The cells were washed with PBS three times, incubated with 0.1 mL Triton X-100 (0.5%) for 20 min, and blocked for 1 h with QuickBlockThe level of cell apoptosis was assessed by flow cytometry with an Annexin V-fluorescein isothiocyanate (FITC)/propidium iodide (PI) apoptosis detection kit  according to the manufacturer’s protocol. In brief, cells were seeded in a six-well plate and transfected with a negative control or siDRD2 or overexpression plasmids and then incubated for 48 h. Afterwards, cultured cells were harvested by trypsinization and cells were washed twice with PBS and resuspended in Annexin V-binding buffer. Cell suspension was then incubated with 5 μL of Annexin V (AV)-FITC and 5 μL of PI staining solution in the dark for 10 min, and the cells were read by a FACS can flow cytometer. A total of 10,000 cells were detected, and to calculate the apoptosis rate, FlowJo v7.6 software  was used to analyze the data.t-test. Data were reported as means ± standard error and p-value < 0.05 was considered significant.Data analysis was performed using SAS Studio . Chi-square tests were used to compare the difference of the gene and genotype frequencies of the pigs between the most aggressive and the least aggressive pigs. The GLIMMIX procedures with a model option DIST = EXPO in SAS were used to analyze the differences of behavioral indicators of 36 h after mixing. Genotype, sex, and group were used as the fixed effects to split the pigs into two equal-sized groups of the two least aggressive and two most aggressive pigs in each pen of 9 or 10 pigs, and the random effect was the pen. For the cell experiments, statistical significance was assessed using the student’s The sequence (2250 bp) on the upstream of the porcine DRD2 gene was used for the prediction of the promoter, transcription factor binding sites, and CpG islands. Two transcription initiation sites, seven promoter regions (from −1818 bp to −1517 bp) , and foup < 0.05) in 293T cells. No difference was found in the luciferase activity between vector P1 and P3 (p > 0.05)  and P3 (−527 to +47 bp) in the 5′-flanking region was significantly greater than that of vector P2 (−1127 to −527 bp) ( > 0.05) A. These To explore whether there are transcription factors binding in the transcriptional promotion region or the transcriptional suppression region, we predicted a potential site (TTTCC) for IRF1 in the transcriptional promotion region and a potential site (AAGTGA) for IRF2 in the transcription suppression region B.p < 0.05) (p < 0.05) (p < 0.05), as the AA genotype was significantly greater than the GG genotype  and rs1107428594 (−385A > G), in the 5′-UTR of the DRD2 gene in Suhuai pigs . The thr < 0.05) . The AA  < 0.05)   . The CAS < 0.05) . These dgenotype .p < 0.05)  D.To further verify the binding of transcription factor IRF1 with the promoter region of DRD2 gene, we performed a chromatin immunoprecipitation (ChIP) assay using porcine kidney-15 (PK15) and neuronal cells. Sheared crosslinked DNA was immunoprecipitated by a specific anti-IRF1 or anti-Immunoglobulin G (anti-IgG). Precipitated DNA was purified and used as a template for PCR amplification. In addition, the specific PCR primers  were conp < 0.05)  C, which A previous study found that the DRD2 gene not only plays a role in the classic DA pathway but is also essential for signal transduction in the brain . Our tisp < 0.01) in PK15 cells and PNCs (p < 0.05) the mRNA and protein expression level of the porcine DRD2 gene both in PK15 cells  were used to knock down the expression level of IRF1 by transfecting siRNAs to PK15 cells and PNCs. The results indicated that the relative mRNA expression levels of siIRF1-2 were lower than those of siIRF1-1  (p < 0.05)  C. Furthe < 0.05) D. In add < 0.05) E. These p < 0.05) (p < 0.05) . As expected, when IRF2 was overexpressed A, the re < 0.05) B. Meanwh < 0.05) C.p < 0.01) (p < 0.01) (p < 0.01) both in PK15 (A siRNA-mediated knockdown of IRF2 in PK15 cells and PNCs showed that siRNA siIRF2-1 had a better efficiency of inhibition in PK15 cells and PNCs ( < 0.01) D. Thus,  < 0.01) E. Consis in PK15 F and PNC in PK15 G.p < 0.05).To investigate the relationship between transcription factor IRF1 and IRF2 in PNCs, we detected the protein expression of IRF1 when IRF2 was inhibited A, as welp < 0.05). RT-qPCR of BAX and BCL-2 genes indicated that the siDRD2-1 group resulted in lower mRNA expression of BAX but greater expression of BCL-2 compared with the negative control group (p < 0.05); the ratio of BCL-2/BAX was significantly lower in the siDRD2-1 group (p < 0.05). These results indicated that the DRD2 gene inhibits porcine neuroglial cell apoptosis. A fluorescence activated cell sorting (FACS) analysis also revealed that the neuroglial cells both in siDRD2-1 and pcIRF2 group had a higher (p < 0.05) apoptosis rate than the negative control or pcDNA3.1 group  of the DRD2 promoter significantly decreased, while transcription factor IRF2 was predicted to bind into this region. Transcription repressors are a group of proteins that play a crucial role in the negative regulation of gene transcription . Here, wSeveral previous studies have investigated genetic variants associated with aggressive behavior ,57. In tIn previous study, researchers found that aggression is often comorbid with neuropsychiatric diseases such as drug addiction , depressTranscription factors IRF1 and IRF2 were originally identified as transcriptional regulators of the interferon (IFN) and IFN-stimulated genes . They alIn the central nervous system, DA exerts a multitude of functions, including control of locomotion , affectiThe promoter activity analyses indicate that the core promoter region of the porcine DRD2 gene is located between −2212 bp and −1127 bp from the initiation site of transcription. Meanwhile, two SNPs in the promoter region of the DRD2 gene were associated with aggressive behavior of weaned pigs after mixing. We also discovered that transcription factors IRF1 and IRF2 are crucial regulatory factors for DRD2 transcription, and identified the binding sites of IRF1 and IRF2 on the promoter region of the DRD2 gene. An SNP (rs1110730503) in the binding site of IRF2 affected the binding between IRF2 and the promoter of the DRD2 gene, regulating the mRNA and protein expression level of the DRD2 gene, which could be the reason for changes in aggressive porcine behavior .


C. elegans THAP domain proteins, LIN-15B and LIN-36, co-localize with DREAM and function by different mechanisms for repression of distinct sets of targets. LIN-36 represses classical cell-cycle targets by promoting DREAM binding and gene body enrichment of H2A.Z, and we find that DREAM subunit EFL-1/E2F is specific for LIN-36 targets. In contrast, LIN-15B represses germline-specific targets in the soma by facilitating H3K9me2 promoter marking. We further find that LIN-36 and LIN-15B differently regulate DREAM binding. In humans, THAP proteins have been implicated in cell-cycle regulation by poorly understood mechanisms. We propose that THAP domain proteins are key mediators of Rb/DREAM function.The DREAM  complex controls cellular quiescence by repressing cell-cycle and other genes, but its mechanism of action is unclear. Here, we demonstrate that two   •THAP domain proteins LIN-36 and LIN-15B cooperate with the Rb-DREAM complex•LIN-36 and LIN-15B repress distinct sets of DREAM targets via different mechanisms•With LIN-36, DREAM represses cell-cycle genes through gene body enrichment of H2A.Z•With LIN-15B, DREAM represses germline genes through H3K9me2 promoter marking C. elegans, repressing distinct targets by different mechanisms. With LIN-36, DREAM represses cell-cycle genes through gene body enrichment of H2A.Z; with LIN-15B, DREAM represses germline-specific genes in the soma through H3K9me2 promoter marking.Gal et al. show that two THAP domain proteins are key mediators of retinoblastoma-DREAM function in  During animal development, cell proliferation is tightly controlled, and differentiated cells spend the majority of the time in a quiescent, nondividing state. The regulation of quiescence is crucial, as uncontrolled proliferation can lead to tumor formation, while premature senescence is associated with aging. Despite its importance, mechanisms of quiescence regulation remain poorly understood.1 to S phase and maintaining the G0 state via transcriptional repression of proliferation-promoting genes (The retinoblastoma (Rb) family of pocket proteins  are key regulators of the cell-division cycle, regulating progression from Gng genes . The majng genes . Loss ofng genes . A mechaOf the Rb family of proteins p130 is the most highly expressed during stable cell-cycle arrest, such as quiescence and senescence, through which it represses proliferation-promoting genes as part of a repressive complex called DREAM  represses cell-cycle-specific genes and others, including germline genes in somatic tissues , validating the screen. Others include components of the MCM complex, a number of RNA-binding proteins, proteins required for kinetochore function, and lin-36, which encodes a THAP-domain-containing protein.To identify proteins involved in DREAM transcriptional repression, we constructed a DREAM-regulated reporter gene by fusing the promoter of the target pression A. The scthe soma B. In linild type B. The RNrter see . Followirter see , includiC. elegans has 17 THAP- or THAP-like-domain-containing proteins, of which 7 have been shown to genetically interact with lin-35/Rb  in wild-type starved L1 animals using antibodies to LIN-35 and LIN-15B and detecting LIN-36 by an endogenous GFP-tag see . We founlin-35(n745) and lin-15B(n744) and generated full-deletion allele lin-36(we36) using CRISPR-Cas9 gene editing (see lin-36(n766). For all mutants, we observed that the primary effect was loss of repression , but strikingly, genes regulated by LIN-36 and LIN-15B are mostly distinct  . To asseEvaluating gbHTZ-1 levels on targets in wild-type and mutant starved L1s, we found that the majority of LIN-36-shared targets require both LIN-35 and LIN-36 for high gbHTZ-1 levels, but loss of LIN-15B had no obvious effect at these loci . In contIn addition to differences in gbHTZ-1, we observed a substantial difference in the HTZ-1 profiles over the promoters of different sets of DREAM targets. While LIN-36-shared targets have a bimodal distribution of HTZ-1 flanking the associated LIN-35 and LIN-36 peaks in wild-type animals, HTZ-1 was instead centrally enriched at LIN-15B-shared target peaks C. The HTWe considered that repression of LIN-15B-shared targets could involve a chromatin-based repression mechanism involving H3K9me2, as previous work showed that LIN-15B facilitates H3K9me2 marking of some DREAM target promoters, although the relevance of H3K9me2 at these genes was not determined . In addilin-15B mutants, and to a lower extent in lin-35 mutants  by LIN-35/Rb  mutants are similar to those of the full-deletion mutant (LIN-36 and LIN-15B both harbor a THAP domain A. To ass5B genes A. We fou5B genes B and S5Cn mutant . As LIN-lin-15B(ΔTHAP) (lin-15B(ΔTHAP) mutants, including 29% of LIN-15B-shared targets, all of which retained LIN-15B(ΔTHAP) binding (We found that the LIN-15B(ΔTHAP) mutant protein localized to the nucleus similar to the endogenous protein A. LIN-15B(ΔTHAP) . Despite binding . We conc binding  or by bilin-36 and lin-35 mutants .Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Julie Ahringer (All unique/stable reagents generated in this study are available from the lead contact without restriction.Strains were cultured using standard methods . Strainspsep-1::his-58::eGFP transgene was generated using three-site Gateway cloning (Invitrogen) in the MosSCI compatible vector pCFJ150, which targets Mos site Mos1(ttTi5605) (sep-1 promoter (chr I: 3439109-3438531) was cloned into site one. Plasmids pJA273 and pJA257  . The sepd pJA257  were useescribed .lin-15B(we23[ΔTHAP]) X, JA1810: lin-36(we30[lin-36::eGFP]) III, JA1811: lin-36 III, and JA1850: lin-36(we36) III (in vitro (dpy-10 co-CRISPR method was used to enrich for desired edit (CRISPR-Cas9 genome editing was used to generate the following strains: JA1798: e36) III . Injectiin vitro . dpy-10 red edit . Cas9 prred edit ; tracrRNred edit . crRNAs red edit . JA1798,psep-1::his-58::eGFP animals were placed into each well. The animals were monitored and when most had L1 progeny the L1s were analyzed for increased expression of the reporter using a COPAS (Union Biometrica) profiler by measuring fluorescence intensity of L1 sized progeny. In the primary screen, 210 clones induced de-repression of the reporter in two out of the four replicates and were included in four replicates of a secondary screen conducted using YA aninals. Of these, 36 showed de-repression in three out of four replicates and were considered to be hits  mutants were hatched at 26°C  against amino acids 72-171. ChIP-seq datasets are described in Frozen starved L1 worms were ground to a powder, which was incubated in 1.5 mM EGS (Pierce 21565) in PBS for 8 minutes, followed by the addition of formaldehyde to a final concentration of 1%, and incubated for a further 8 minutes. The fixation was quenched for 5 minutes by the addition of 0.125 M glycine. Fixed tissue was washed 2X with PBS with protease inhibitors (Roche EDTA-free protease inhibitor cocktail tablets 05056489001) and once in FA buffer  with protease inhibitors (FA+), then resuspended in 1 mL FA+ buffer per 1 mL of ground worm powder. The extract was sonicated to an average size of ∼250 base pairs using a Bioruptor Pico (Diagenode), and 10-20 ug of DNA was used per ChIP reaction, together with ∼1ug DNA from A single ball of frozen worms was used for RNA extractions. Total RNA was extracted using TriPure (Roche) and further purified using an RNeasy column (QIAGEN). RNA-seq libraries were prepared from 100-1000 ng of total RNA using the Illumina TruSeq RNA kit according to the manufacturers’ instructions. RNA-seq datasets are given in A list of putative DNA binding domains annotated in Pfam was obtained from C. elegans and C. briggsae genomes using BWA v. 0.7.7 with default settings   as a guide . BigWig tracks were generated using the wigToBigWig tool downloaded from the UCSC website (https://hgdownload.soe.ucsc.edu/downloads.html). Processing of genomic coordinates was performed using the BEDTools suite (version 2.27.1) and in-house scripts. Statistical analyses were performed in R  , but onl utility  was used utility . RNA-seq utility  with themed in R . CommandA gene model was built based on the WS260 annotation. Tag counts for each gene were extracted from STAR aligned BAM files, and differential gene expression between N2 and mutant backgrounds was tested using DESeq2 . A falsehttps://github.com/jurgjn/yapc)  . Brieflyjn/yapc) , and onleepTools ) over theepTools . TherefoeepTools  and the eepTools . Peak oveepTools  was deteeepTools .lin-15B or lin-36, respectively  tool  availablAverage gene body HTZ-1 (gbHTZ-1) read coverage was calculated from the region from the most upstream Wormbase TSS +500bp to the most downstream TTS. We identified genes showing a significant loss of HTZ-1  by running DESeq2 on the coding genes in the top 90% of gbHTZ-1 coverage in N2. Genes shorter than 500 bp in length were excluded from the analysis.Average H3K9me2 signal  was calculated over LIN-35 + LIN-36 or LIN-35 + LIN-15B ChIP peaks associated to the putative promoter region  of their respective LIN-36-shared or LIN15B-shared direct targets. Peaks showing a significant loss of H3K9me2  were identified using DESeq2 on LIN-35, LIN-36 and/or LIN-15B peaks overlapping a wild-type H3K9me2 peak , and at LIN-36-shared and LIN-15B-shared promoter-associated peaks (with: -objfun classic) were detected using MEME . Motifs P-value < 0.001 and LFC > 0. Peaks with decreased signal in mutants have adjusted p < 0.001 and LFC < 0.DESeq2 was used to identify peaks differentially bound between wild-type and a mutant background by comparing the read counts from the bwa aligned BAM files mapped in wild-type peak regions. Peaks with increased signal in mutants have adjusted Biorender.com.The Graphical abstract was created with The significance of the overlap of LIN-15B and LIN-36 direct targets with LIN-35 direct targets was assessed with a hypergeometric test.Significant differences in a) germline-specificity for LIN-15B shared targets, LIN-36 shared targets and LIN-35-only targets; b) gbHTZ-1 and promoter H3K9me2 signal at LIN-15B and LIN-36 shared targets were determined using a Wilcoxon rank sum test.Overrepresentation of LIN-15B or LIN-36 shared targets showing a significant loss of gbHTZ-1 or promoter H3K9me2 in different mutant backgrounds was assessed with a hypergeometric test (with BH correction for multiple testing).met-2 strains were determined using a t test.Significant differences in LIN-15B and LIN-36 shared targets expression between wild-type and Significant differences in a) the fraction of LIN-15B and LIN-36 shared targets upregulated in different DREAM components mutants; b) the fraction of LIN-15B and LIN-36 shared targets peaks containing distinct motifs; c) the fraction of LIN-15B and LIN-36 shared targets peaks showing a significant difference in LIN-15B, LIN-15B(ΔTHAP), LIN-36 or LIN-35 ChIP-seq signal in the corresponding factor’s mutant compared to wild-type were determined using Fisher’s exact tests (with BH correction for multiple testing).∗:p < 0.05, ∗∗:p < 0.01, ∗∗∗:p < 0.001.In all figures, significant differences were marked as follows: ns: p > 0.05;


Subsequently, GO analysis and KEGG pathway analysis were performed for these differential genes for investigating their pathways and function. At the same time, we established a genetic risk scoring model for predicting the overall survival (OS) by using the mRNA expression levels of these differentially regulated TFs, and defined the CRC into low and high-risk categories which showed significant survival differences. The genetic risk scoring model included four high-risk genes  and two low-risk genes (ETS2 and SALL1), and validated the OS in two GEO databases . To analyze the genetic and epigenetic changes of these six risk-related TFs, a unified bioinformatics analysis was conducted. Among them, ZNF26 is progressive in CRC and its high expression is linked with a poor diagnosis as well. Knockdown of ZNF26 inhibits the proliferative capacity of CRC cells. Moreover, the positive association between ZNF26 and cyclins  was also identified. Therefore, as a novel biomarker, ZNF26 may be a promising candidate in the diagnosis and prognostic evaluation of colorectal cancer.The dysregulation of transcriptional factors (TFs) leads to malignant growth and the development of colorectal cancer (CRC). Herein, we sought to identify the transcription factors relevant to the prognosis of colorectal cancer patients. We found 526 differentially expressed TFs using the TCGA database of colorectal cancer patients ( Colorectal cancer is the third most common cancer form in the world, accounting for nearly 600,000 deaths per year . ColorecTranscription factors are a class of proteins that bind to unique sequences upstream of a target gene’s 5′ end, which is commonly referred to as a promoter region . Hence, ZNF26 belongs to the zinc finger protein family, and we discovered that its high expression is linked to a poor prognosis. Moreover, ZNF26 is positively associated with cyclins .This study aimed to learn more about the biology of TFs in CRC. Up to this level, the expression of TF, already contained in the Animal TFDB database in colorectal cancer, was analyzed. Through the GO and KEGG pathways analyses, we investigated the pathways and functions in which differentially expressed TFs may be involved. At the same time, a set of CRC-specific TFs was identified in which the TF prognostic features were obtained. By analyzing various public data sets, we claimed that a further analysis of the expression and sequence changes of these differentially regulated TFs with different regulations may help to alter the expressions in CRC. Additionally, 1 was used to collect miRNAs, mRNA data, and related clinical information from CRC patients in the TCGA project. After that, R-studio software 3.5.3 was used for examining the data for 525 tumor tissue samples and 44 normal tissue samples . According to the pathologic stage, the number of tumor tissues in stages I, II, III, and IV were 93, 206, 148, and 78, respectively. Using the “edgeR package” of p-value < 0.05 and | log2FC| > 0.5 as a guideline for screening DEGs and DEMs, mRNAs (DEGs) and miRNAs (DEMs) were found to be differentially expressed. The volcano plot and heat map showed the expression patterns between the cancer and normal samples. The study followed the TCGA guidelines.The GDC Data Portal2 for validating the prognostic potential of genetic risk score. Three microarray expression profile datasets, GSE32323, GSE39582, and GSE74602, were also downloaded from the GEO database to validate the differentially expressed TFs.Two distinct data sets were acquired from the directory of the GEO The GSE17536 was submitted by 3) is a resource directed to come up with the most far-reaching and precise knowledge of transcription factors  and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out. Enrichment analyses for the DEGs were studied in the “clusterProfiler” package of R software . CertainFor more research and development of a genetic risk score, we chose the differentially regulated TFs first. To assess the connection between the expression of the differentially regulated TFs and their comprehensive durability, a univariate Cox proportional hazards regression analysis was carried out. The top ten important prognosis-related genes from the univariate Cox regression analysis were then considered candidate variables, and they were further investigated using a stepwise multivariate Cox proportional hazards regression analysis to predict each individual’s risk score .4.The percentage of samples in which a particular RBP was amplified or deleted was calculated using the data from cBioPortalvia the linkedomics website5. The prediction of the CpG island of the ZNF26 promoter was carried out with MethPrimer6.Analysis of the level of methylation derived from the TCG database (Illumina Human Methylation 450 platform) was carried out 7 was used for prognosticating the gene targets of miRNAs.StarBaseThe miRNA−mRNA relationship, protein-protein interaction network was predicted using the CytoscapeVersion3.5.3 software.The CRC tissues and adjacent normal tissues were obtained from Xiangya Hospital of Central South University. This study was approved by the ethics committee of Xiangya Hospital. The patients were informed, and signed an informed consent form.8). The colorectal cell lines HCT8, Caco2, and RKO were kindly provided by Professor Wancai Yang . The NCM460, SW480, Caco2, HCT116, HCT8, SW620, and RKO cells were maintained in RPMI-1640  with 10% FBS. All cells were cultured at 37°C with 5% CO2.The normal human colon mucosal epithelial cell line NCM460 and human colorectal cell lines HCT116, SW480, and SW620 used in this study were purchased from the American Type Culture Collection . The sequence of ZNF26 siRNA is: ZNF26 si#1: GAAGTCTCCATTCGTTGTA, ZNF26 si#2: CTAGGAAGGCATCACTTCA. siRNA was transfected with jetPRIME DNA and siRNA transfection reagent (PolyPlus-transfection). Cells were harvested 48 h after transfection, and the knockdown efficiency was tested by quantitative reverse transcription polymerase chain reaction (qRT-PCR).Small interfering RNA (siRNA) targeting ZNF26 ; CDK2 ; CCNE2 ; CDK6 ; CHEK1 ; GAPDH . GAPDH was used as an internal control.Trizol reagent (Vazyme) was used for extracting the total RNA. Go Script reverse transcription system (Promega) was used for performing reverse transcription PCR. On the ABI Prism 700 thermal cycler, real-time qPCR was performed using GoTaq qPCR Master Mix (Promega). The following is the primer sequence: The cells  were cultured in a 96-well plate for 24 h, and the cell proliferation ability was investigated using the Cell Counting Kit 8 (CCK-8). For the colony formation assay, a 12-well plate containing 200–300 infected RKO cells was used. After 10–14 days of incubation, the cells were fixed with 4% paraformaldehyde, stained with crystal violet, photographed, and then counted. The EdU  assay was performed according to the standard protocol. All experiments were repeated at least three times.ZNF26 in RKO cells with two siRNAs, the cells were collected and resuspended in 1 × PBS. The cells were then fixed in 70% ethanol overnight. The next day, after washing with PBS solution and centrifugation, the cells were stained with propidium iodide (PI) and analyzed by the FACSCalibur system .After 48 h of specific interference with P < 0.05 was considered statistically significant.We used R Studio (R version 3.5.2) for the statistical analysis. For univariate and multivariate analysis, SPSS version 20.0 was used. Graph Pad Prism 8.0 version for Windows was used to carry out the Kaplan Meier survival analysis. We obtained 1,665 transcription factors from Animal TFDB to elucidate different aspects of TF biology in CRC. To recognize the transcription factors that are modified in CRC due to the genetic and epigenetic effects, we performed an integrated bioinformatics analysis . Using tThe Animal Transcription Factor DataBase  is a resource aimed to have the most far-reaching and authentic knowledge for TFs . For furWe carried out the GO function and KEGG pathway to further investigate these differentially expressed TFs. The “cluster Profiler” package of the R software was used to examine the enrichment analyses for the upregulated and downregulated DEGs .In the biological process group, the progressive genes are principally intensified in regionalization, pattern specification procedure, embryonic organ development, anterior/posterior pattern specification as well as appendage development. As for the enrichment of retrogressive genes, steroid hormone intervenes with the signaling pathway, cell fate commitment, cellular feedback to steroid hormone stimulus, embryonic organ development, as well as embryonic organ development have been exhibited.In another sort of analysis focusing on the GO cell components, upregulated transcription factors were mainly enriched in the transcription factors, nuclear transcription factor network, RNA polymerase II transcription factor network, heterochromatin, and nuclear chromatin, while the downregulated TFs are enriched in the transcription factor complex, nuclear chromatin, nuclear transcription factor network, RNA polymerase II transcription factor complex, and fibrillar center.Besides, in the GO cell function analysis, upregulated TFs are significantly enriched in the transcription factor activity, DNA-binding transcriptional repressor activity, DNA-binding transcriptional activator activity, proximal promoter DNA-binding transcription repressor activity, and proximal promoter DNA-binding transcription activator activity. Meanwhile, downregulated TFs are enriched in the DNA-binding transcriptional activator activity, transcription factor activity, proximal promoter DNA-binding transcription, DNA-binding transcription repressor activity, and DNA-binding transcription repressor activity . These sAt the same time, the KEGG pathway analysis was carried out on the up- and downregulated TFs. Upregulated TFs have enriched in herpes simplex virus type 1 infection, transcriptional misregulation in cancer, TGF-β signaling pathway, maturity onset diabetes of the young, and Cellular senescence, while downregulated TFs are enriched in herpes simplex virus type 1 infection, transcriptional misregulation in cancer, maturity onset diabetes of the young, parathyroid hormone synthesis, secretion, and action, Th17 cell differentiation .ETS2, LBX2, HSF4, SALL1, SIX4, HEYL, POU6F1, SIX2, ZNF26, and TLX2, were sought out by a univariate Cox regression (p < 0.001)  . To furtrognosis . The folP < 0.001)  . To veri= 0.0023  and p = = 0.0193 , respect= 0.0193 . The ass= 0.0193 .p = 0.002), M stage (p = 0.044), TNM stage (0.017), CEA level (0.044), and the genetic risk score (<0.001) were separately prognostic of OS , T (T3–T4 vs. Tis–T2), N (N1–2 vs. N0), M (M1–2 vs. M0), Stage (III–IV vs. I–II), Kras mutation (mut vs. wt), CEA level (≥5 vs. <5 μg/ml), and Risk (high risk vs. low risk). Multivariate survival analysis further uncovered that age   . Next, wC tissue .ZNF26 function in CRC, GO analysis and KEGG analysis were performed on ZNF26 positively/negatively correlated (| R| > 0.3) genes.To figure out the mechanism of ZNF26 were mainly enriched in the detection of the chemical stimulus involved in sensory perception, DNA conformation change, and chromosomal region, while the genes that are negatively associated with ZNF26 were mainly enriched in neutrophil-mediated immunity, neutrophil activation, and cell adhesion molecule binding . More studies are needed to investigate the potential of the CRC-related TFs, especially ZNF26, for diagnosis, prognosis, and targeted therapy.Based on GSE29623, GSE17536, GSE32323, GSE39582, and GSE74602, validating the expression and survival analysis of these six TFs proceeded. It was shown that t member . New stut member , lung cat member , and ovat member . CurrentZNF26 has been demonstrated in CRC indicating a poor prognosis of patients. Hence, we tested the hypothesis that, as a novel biomarker, ZNF26 might be potentially valuable in the diagnosis and prognostic evaluation for colorectal cancer.In conclusion, our research developed a TFs signature through a series of bioinformatics analyses, which proved to be an accurate independent prognosticator in CRC. Concurrently, we comprehensively view the six abnormally regulated TFs in CRC and mechanisms which may result in this abnormal regulation. Moreover, the elevated expression of The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/JL and YL designed the research studies and analyzed the data. JL wrote the manuscript. YG, QX, RT, and GS contributed to check the figures and manuscript. GY supervised the project. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.