Ceratophyllum demersum (common hornwort) is presently considered the worst invasive submerged aquatic macrophyte in New Zealand. We explored the global phylogeographic pattern of the species, based on chloroplast and nuclear DNA, in order to identify the origin of the invasive populations in New Zealand and to clarify if there were multiple introductions. The phylogeographic study identified geographically differentiated gene pools in North America, tropical Asia, Australia, and South Africa, likely native to these regions, and a recent dispersal event of a Eurasian-related haplotype to North America, New Zealand, Australia, and South Africa. At least two different invasive genotypes of this Eurasian-related haplotype have been found in New Zealand. One genotype is closely related to genotypes in Australia and South Africa, while we could not trace the closest relatives of the other genotype within our C. demersum sample set. Contrasting spectra of genetic distances in New Zealand and in a region within the native range (Denmark), suggest that the invasive population was founded by vegetative reproduction, seen as low genetic distances among genotypes. We also discovered the introduction of the same Eurasian-related haplotype in Australia and South Africa and that a cryptic invasion may be occurring in these continents. Islands often have unique and fragile ecosystems with ecological niches that have not been filled because of the distance from colonizing populations4. Furthermore, the geographical isolation of islands, such as New Zealand, makes them particularly vulnerable to invasions because of the lack of natural competitors and predators that control populations in their native ecosystems, i.e. introduced populations are released from the enemies of their native ranges according to the enemy release hypothesis6. The spread of alien species into New Zealand and the development of some of those into invasive nuisance plants threatening the indigenous flora have occurred several times in the past century and are likely to still occur7. Freshwater ecosystems are more sensitive to invasions than terrestrial habitats8, and in New Zealand several aquatic macrophyte species such as Ceratophyllum demersum L., Lagarosiphon major (Ridley) Moss and Egeria densa Planch. are spreading across freshwater ecosystems at a great cost to the local ecosystems and economy9.Global trade and transport has dramatically increased the number of alien plant species and populations introduced to novel areas either intentionally or accidentally by human activity10. These invasions are particularly difficult to recognize before changes in ecosystems are observed10.In some instances, introduced genotypes may have conspecific native populations in the invaded range and be morphologically similar to the native genotypes but sufficiently different in ecology or physiology to become invasive and outcompete the conspecific native populations as well as other species. This type of invasion is called cryptic, as the new invaders are difficult to distinguish from native populations due to their morphological resemblanceCeratophyllum demersum L. (common hornwort) is presently considered the worst invasive submerged macrophyte in New Zealand as it grows vigorously, forming surface-reaching canopies, and replaces native plant communities11. It is also an economic burden as it clogs hydrostations and prevents fishing and recreational activities in several lakes due to production of thick biomass mats12. Ceratophyllum demersum achieved a cosmopolitan distribution about 2.5 million years before present13. Given the fossil record, the species is considered native to North America, Eurasia, Africa and Australia16. In New Zealand, C. demersum was recorded for the first time in 1961 in the Hawke’s Bay region on the east coast of the North Island. Since then it has spread to many lakes and streams across the North Island7. In 2002, C. demersum was also found in two locations in the South Island, where it has now been eradicated.C. demersum were collected from all over the world to investigate the phylogeography of the species and trace the origin of the invasive populations in New Zealand. We also assessed the role of genetic differentiation in the invasion in New Zealand, i.e. if the invasion is driven by a population with high genetic diversity, and thus has potential for rapid evolution, or by one or few genotypes with high phenotypic plasticity17. We used both chloroplast DNA sequences (cpDNA sequences) to trace long-distance dispersal events, and nuclear amplified fragment length polymorphisms (AFLPs) to resolve intra-haplotypic relationships , within distribution ranges.In this study, samples of C. demersum  EU, and (3) the rest of the world, being the US, tropical Asia (Th and Ch), Aus, SA, and NZ in our sample set  bmersum,  EU, and bmersum,  EU, and C. demersum and within C. submersum. The second group (range 32–49) included pairwise genetic distances between C. demersum and C. submersum. Three samples of C. demersum showed similar genetic distances both to the other samples of C. demersum and to C. submersum. These may be regarded as an intermediate group of genetic distances (range 21–30) between the two species. One of these three samples was 201DK, which the network placed along the C. submersum branch, while the other two samples were included in the EU group  was 0.161 within the Danish C. demersum population and it decreased to 0.132 when the possible hybrids were omitted from the analysis  was 0.110 in the NZ population and decreased to 0.069 when sample 132NZ was removed .The spectrum of pairwise genetic distances within the NZ populations suggested two reproductively isolated ces Fig. . The secNei’s unbiased AFLP genetic identities showed that the most similar gene pools in our sample set to that of the NZ invasive population are in SA, Aus, and in Ch (for sample 172NZ), based on the pairwise genetic identities Table . The NZ The relationship of sample 172NZ from the most northern sampling locality in NZ with the Ch sample Table . We coulC. demersum to consist of two layers  . This is supported by our findings, as the spectrum of genetic distances . From the results in the present study we can conclude, that the previously studied population from NZ belongs to the most frequent genotype found in New Zealand. The NZ population was shown to have a higher level of phenotypic plasticity in photosynthesis and relative growth rate in response to temperature and nitrogen concentration in the water compared to the native populationCeratophyllum includes at least six species of which only C. demersum is cosmopolitan, whereas the other species have restricted distribution ranges37. The other Ceratophyllum species are allopatric among each other, but sympatric with C. demersum, suggesting that hybridization opportunities might have occurred for C. demersum37. Furthermore, hybridization can stimulate the evolution of invasiveness38 and has occurred frequently in similar invasive aquatic plants, such as the invasive watermilfoils Myriophyllum spicatum x sibiricum39 and Myriophyllum heterophyllum x hippuroides19 in North America. There is no evidence of hybrids from our study. However, the high genetic distances between the two NZ genotypes comparable to those between the C. demersum and C. submersum populations in the native range rise a suspicion that hybrids may exist  occurring in the US, NZ, Aus and SA is almost identical to that of the invasive haplotype M of Phragmites australis (Cav.) Trin ex Steudel (common reed) in North America42, which can serve as a reference of a recent cryptic invasion whose genetic dynamics have been exhaustively studied and documented46. A case of convergent evolution in NZ, Aus and SA cannot be ruled out; however, it would be very unlikely that the same mutations (in the chloroplast and in the nuclear DNA) had occurred independently in three disjunct ranges. In the case of independent mutations we would also expect diverging alleles among the populations of the three continents due to allopatric evolution, and an increase in gene diversity when the genotypes of the three populations are considered together. In our study, the genotypes in NZ, Aus and SA shared the same cpDNA sequences and diverged in 2 to 11 AFLP fragments, gene diversity did not increase significantly when we merged the Aus, SA and NZ genotypes and was about half of that found in the Danish population . The protocol was modified for dried samples as suggested by Lambertini et al.42 to include grinding of the sample in liquid nitrogen and a small amount of autoclaved sand prior to addition of extraction buffer. In addition, incubation at 65 °C was prolonged to 60 min and incubation on ice was 30 min.DNA was extracted from apical parts of shoots dried with silica gel using the E.Z.N.A.et al.48. Several primer combinations were initially tested on a subset of samples including the most geographically diverse samples to identify primer combinations that produced an adequate number of well separated, clearly polymorphic and reproducible fragments. The full set of samples were analysed using two sets of primers, based on the number of well separated, clear polymorphic fragments: (1) E-CAGcy (5′-GACTGCGTACCAATTCCAG-3′) + M-CAG (5′-GATGAGTCCTGAGTAACAG-3′) and (2) E-CGTcy (5′-GACTGCGTACCAATTCCGT-3′) + M-AGG (5′-GATGAGTCCTGAGTAAAGG-3′). Primers were 6-FAM labelled. Restriction digestion and adapter ligation were performed simultaneous in a 20 µL reaction on approx. 20 ng of genomic DNA, using 1.25 U of each of the restriction enzymes EcoRI and MseI and 0.3 U T4 DNA ligase to ligate 1.25 pmol EcoRI and 12.5 pmol MseI double-stranded nucleotide adapters. We incubated the restriction and ligation reaction in a thermal cycler  at 37 °C for 4 h followed by a 0.1 °C/s decrease to 16 °C for 2 h and finally 10 min at 70 °C. The restricted DNA was diluted four times prior to preamplification. One-nucleotide selective primers were used in the preamplification, performed with the program: 20 cycles of 94 °C for 30 s, 56 °C for 60 s, 72 °C for 60 s. The preamplification product was diluted 10 times prior to selective amplification. For the selective amplification the program was: 94 °C for 30 s, 65 °C for 30 s (decreased by 0.7 °C/cycle for the subsequent 12 cycles) and 72 °C for 60 s, followed by 23 cycles at 94 °C for 30 s, 56 °C for 30 s and 72 °C for 60 s. The PCR product from the selective amplification was diluted 20 times prior to electrophoresis. The AFLPs were run in an ABI sequencer with an internal ladder in each sample containing 16 fragments ranging in size from 35 bp to 500 bp.The AFLP genotyping was performed using a slightly modified protocol according to Vos C. demersum sequences from GenBank and tested variation in 13 chloroplast regions: trnQ-rps1649, trnT-trnL50, trnL-trnF50, matK51, rbcL gene53, rbcL–psaI54, rpl1655, rpl3249 and the two nuclear internal transcribed spacers of nuclear ribosomal DNA (ITS)56 in a subset of intercontinental samples. Given the low intraspecific variation in the published and tested sequences, we also screened variable regions within the chloroplast genome sequenced by Moore et al.57  and designed specific primers in two non-coding regions rich in microsatellites (Cdemex 1 and 2).We downloaded all rps16 gene encoding the S16′ protein, which is a component of the 40 S ribosomal subunit and is amplified by forward primer (rps16F) 5′-GTGGTAGAAAGCAACGTGCGACTT-3′ and reverse primer (rps16R) 5′-TCGGGATCGAACATCAATTGCAAC-3′58 and a microsatellite rich region of the cpDNA amplified by the specifically designed forward primer (Cdemex 1 F) 5′-CTATTGACCCCAAGTTCCCT-3′ and reverse primer (Cdemex 1 R) 5′-CAGGCCGTATCGACGGAAAG-3′.Based on the variation in the subset of samples, we chose to include two regions in the analysis, which showed both substitutions and variation at the microsatellite loci. We chose the The mastermix for one amplification reaction included 10 ng DNA, 5 µl Red Taq DNA Polymerase 2X Mastermix , 5 pmol of forward and reverse primer, sterile water was added to reach a final volume of 10 µl. PCR amplification was run with an initial step of 3 min at 95 °C, followed by 40 cycles of 95 °C for 30 s, 52 °C for 40 s, 72 °C for 1 min and terminated by a final step of 72 °C for 7 min. Subsequently, 3 µl of PCR products were run on a 1% agarose gel with EtBr for approx. 1 hour (120 V) to check amplification quality and quantity. Sequencing with both forward and reverse primers was performed at GATC Biotech AG, Köln, Germany. All different haplotypes obtained by the cpDNA sequencing were deposited in GenBank.Genious R6 ver. 6.0.6  was used to analyse the AFLP data. The internal ladder peaks were aligned and the resulting aligned chromatograms were scored manually for the presence/absence of peaks of equal size.C. demersum and C. submersum. The DNA was extracted a second time from samples showing diverging chromatograms. The subset of samples used for primer screening was amplified multiple times to ensure reproducibility of the scored peaks. We removed genetically identical AFLP profile samples which were collected from the same locations. We entered question marks for uncertain peaks, either because of low intensity or uncertain size.For the first primer combination we scored fragments in the range between 70 to 300 bp in size. For the second primer combination we scored fragments between 40 and 400 bp. In total we scored 104 polymorphic fragments including both 59. Hereby, differences due to the number of repeats were counted as one character, independently of their number. Furthermore, indels were coded with two states . The final matrix for the two sequences combined contained 1518 characters. There were 9 substitutions and 5 variable microsatellite loci in total among the C. demersum samples. Because of possible DNA slippage at microsatellite loci, the data set was analysed with and without microsatellite repeat motifs, and haplotypes were defined only by substitutions.The sequences were aligned with the program Geneious R6 ver. 6.0.6. To avoid overestimation of the number of mutations associated with mononucleotide repeats we coded repeat motifs (microsatellites) as multistate characters60 and the trees were rooted with five samples of C. submersum as a functional outgroup. The consensus tree obtained from the cpDNA sequence matrix was compared with that obtained by the same matrix of cpDNA sequence data, but omitting the microsatellite variation.The resulting AFLP and cpDNA sequencing results were analysed separately. The cpDNA sequences were analysed with PAUP, ver. 4.0b10 . The data were subject to a jackknife analysis with 37% character deletion, “full heuristic search” and 1000 replicates61 among geographic regions with GenAlEx ver. 6.562 and Euclidean pairwise genetic distances (i.e pairwise no. of changes). Euclidean genetic distances were used to construct the spectrum of genetic distances and assess reproduction mode of the NZ population. A normal distribution of genetic distance frequencies indicates sexual reproduction, whereas a decreasing curve of genetic distances peaking close to zero is more likely due to clonal reproduction and somatic mutations22, and/or artefactual AFLP polymorphisms. We compared the NZ spectrum of genetic distance with that of a native population in Denmark (DK). We included C. demersum and C. submersum populations in the DK spectrum in order to have a reference of genetic distance patterns within the interbreeding population of C. demersum and between the non-interbreeding populations of C. demersum and C. submersum.The AFLP binary matrix was analysed in a median-joining network with the program Network ver. 4.6.1.1. Phylogenetic Network Constructions  which combines parsimony with minimum spanning tree. The data from the AFLPs were also used to calculate pairwise Nei’s unbiased genetic identitiesWe also calculated AFLP gene diversity (h) (or expected heterozygosity) within the interbreeding Danish population and the introduced population in NZ, and within haplotypes and populations at different geographic scales, in order to identify populations of closely related genotypes.The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.Supplementary Information


STAT3 signaling plays the pivotal role in tumorigenesis through EZH2 epigenetic modification, which enhanced STAT3 activity by increased tyrosine phosphorylation of STAT3. Here, another possible feedback mechanism and clinical significance of EZH2 and STAT3 were investigated in gastric cancer (GC).STAT3, p-STAT3 (Tyr 705) and EZH2 expression were examined in 63 GC specimens with matched normal tissues by IHC staining. EZH2 and STAT3 were also identified in five GC cell lines using RT-PCR and western blot analyses. p-STAT3 protein was detected by western blotting. In order to investigate whether EZH2 expression was directly regulated by STAT3, EZH2 expression was further detected using siRNA for STAT3 or IL-6 stimulation, with dual luciferase reporter analyses, electrophoretic mobility shift assay (EMSA) and chromatin immunoprecipitation (ChIP) assays. The clinical significance of STAT3, p-STAT3 and EZH2 expression was evaluated by multi-factor COX regression and Kaplan-Meier analyses.R = 0.373, P = 0.003), which was consistent with our data showing that STAT3 as the transcriptional factor enhanced EZH2 transcriptional activity by binding the relative promoter region (-214 ~ -206). STAT3 was an independent signature for poor survival (P = 0.002). Patients with STAT3+/EZH2+ or p-STAT3+/EZH2+ had a worse outcome than others (P < 0.001); Besides, high levels of STAT3 and EZH2 was associated with advanced TNM staging (P = 0.017). Moreover, treatment with a combination of siSTAT3 and EZH2-specific inhibitor, 3-deazaneplanocin A (DZNEP), increased the apoptotic ratio of cells. It is benefit for targeting STAT3-EZH2 interplay in GC treatment.Hyper-activation of STAT3, p-STAT3 and EZH2 expression were observed in GC cells and tissues. STAT3 signaling was correlated with EZH2 expression in GC  contains supplementary material, which is available to authorized users. Although the prevalence of gastric cancer (GC) has gradually decreased, it still accounts for a large portion of cancer-related deaths in China . One of Many recent studies have suggested that inflammation plays an important role in the development of GC. Aberrant IL-6/STAT3 signaling in cancer cells have emerged as a major mechanism for cancer initiation and development , 11. IL-S-adenosylmethionine to the 5′-position of cytosine in CpG dinucleotides present in gene promoters, thereby maintaining a consistent pattern of epigenetic gene silencing of TSGs in cancer cells [IL-6/STAT3 signaling plays an important role in regulating epigenetic aberrance during tumorigenesis, especially in the expression of certain key epigenetic enzymes, such as EZH2 . EZH2, aer cells .Although the genes methylated in cancer cells are packaged along with nucleosomes containing 3Me H3H27-marked genes, which are silenced in cancer, they have been shown to be independent of promoter DNA methylation, thus highlighting that 3Me H3K27 could potentially be an independent mechanism for silencing TSGs . DNA metPrevious studies have shown that increased EZH2 is involved in the pathology of gastrointestinal inflammation and associated cancers . MoreoveIL-6/STAT3 signaling plays a critical role in carcinogenesis by regulating various genes. EZH2, a protein that epigenetically silences tumor suppressor genes, was induced by IL-6 stimulation. To determine the inner relationship between STAT3 and EZH2, we first analyzed mRNA and protein expression levels of STAT3 and EZH2 in five GC cell lines using RT-PCR and real-time PCR Fig. , respectP = 0.003; Table P = 0.001). A close correlation between STAT3 and EZH2 was observed in the cohort by χ2-test  showed a combined positivity for STAT3 and EZH2  and 47 (74.6%) cases, respectively. Furthermore, 82.5% (52/63) of the patients were either EZH2P = 0.001, log-rank; Fig. P = 0.002; Additional file P = 0.757; Additional file Univariate analysis demonstrated that high expression of EZH2 was significantly associated with poor survival  than in STAT3−/EZH2− patients . Additionally, patients with EZH2+/STAT3+ expression had poorer prognosis than those with either EZH2− and/or STAT3− expression  rate was significantly correlated with STAT3 protein expression. In this cohort, the 5-year OS rate was 31.6%. As expected, the OS was significantly higher in the STAT3Therefore, these results indicate that STAT3 and EZH2 are important genetic markers in predicting a poor prognosis for GC patients undergoing resection. Thus, our findings highlight the value of EZH2 as a predictor of survival that could be more significant when considered in conjunction with STAT3 in GC.P = 0.024), TNM stage (P = 0.0001), and lymph node metastasis (P = 0.016), and EZH2 expression was positively correlated with patients’ gender (P = 0.043) and TNM stage (P = 0.002). The present study showed that hyperactivation of STAT3 and EZH2 in GC tissues was significantly associated with advanced TNM stage. Notably, 40% of GC tissues, corresponding to TNM stages I and II, and 65.7% of GC tissues, corresponding to TNM stages III & IV, showed a significantly high expression of both STAT3 and EZH2 .In addition, most of GC samples exhibited coexpression of STAT3 and EZH2 in the cohort Fig. . FurtherP = 0.000, and lymGiven the co-expression of STAT3 and EZH2 in GC, we investigated whether STAT3 could regulate the expression of EZH2; thus, we analyzed EZH2 expression at both mRNA and protein levels in SGC7901 cells transfected with three pairs of siSTAT3 primers and scrambled negative control siRNA. Interestingly, STAT3 siRNAs decreased the level of STAT3 and EZH2 expression Fig. , subsequEZH2 promoter activity. The level of EZH2 promoter activity in siSTAT3-treated SGC7901 cells was found to be significantly lower than that in the untreated control. The relative activity of EZH2 was decreased by siSTAT3 (P = 0.0248), but it was increased to 6.18-fold by IL-6 stimulation 4-6AA [EZH2 promoter at position −346 to +52, which, in turn, corresponds to the consensus STAT3-binding site TTN(4-6)AA. Corroborating these findings, the results of our study demonstrated a significant decrease in luciferase activity for the shorter length EZH2 promoter gene (−436 to +52), as compared to that of the full length EZH2 promoter 4-6AA . Hence, EZH2 promoter activation and to further investigate the role of the promoter fragment −436 to +52 containing three motifs of STAT3. Furthermore, in order to confirm that STAT3 bound to the EZH2 specific promoter_(-436-- + 52), we used a ChIP-PCR procedure, comprising 33 PCR cycles optimized to achieve amplification of DNA that had been precipitated with STAT3. In the absence or presence of siSTAT3 after IL-6 stimulation or not, the enrichment of STAT3-binding to EZH2 fragments was decreased after knocking down of STAT3 by PCR and quantitative real-time PCR analyses  containing the three Stat3-binding fragments, vector p222 (Fragment 2) lacking the Stat3-1 fragment, vector p163 (Fragment 3) lacking both Stat3-1 and STAT3-2 fragments, and vector p131 (Fragment 4), which has no Stat3-fragment . Thus, our findings demonstrate that EZH2 is the downstream target gene of STAT3 signaling and plays an important role in the anti-apoptotic effect of the latter in GC cells.Aberrant expression of STAT3 is known to contribute to malignancy . In the P = 0.003, P = 0.027, respectively), which in turn resulted in the down-regulation of EZH2 at both mRNA and protein levels. We next examined cell cycle variation and apoptosis in cells treated with siSTAT3 or DZNep alone or in combination. As shown in Fig. P < 0.05; Fig. In addition, our clinical data showed that hyperactivation of STAT3 and EZH2 occurred in GCs. Further supporting this clinical observation, knockdown experiments involving the treatment of GC cells with STAT3 siRNA in the presence of DZNep demonstrated an increased apoptotic rate, as well as enhanced caspase-3/9 activity . High levels of EZH2, STAT3 and p-STAT3 expression were significantly associated with poor prognosis in GC patients. Furthermore, combined EZH2 and STAT3 or p-STAT3 was apparently associated with worse clinical outcome, suggesting that the panel of EZH2 and STAT3 could be served as the potential of molecular prognostic signature. Our study also found that treatment of GC cells with STAT3-siRNA and/or the presence of EZH2 specific inhibitor, DZNep, enhanced the downregulation of EZH2 expression with increasing apoptosis. Thus, combination between siSTAT3 and EZH2 inhibitors could be contributed for the potential epigenetic therapy against GC patients.We found that both mRNA and protein expression levels of EZH2 were decreased by knocking down of STAT3 with siRNA in GC cells. Moreover, STAT3, the transcriptional factor, induced 2 incubator at 37 °C.Human IL-6 was purchased from Roche . Antibodies, including anti-STAT3, anti-p-STAT3 (Tyr 705), anti-EZH2, and anti-β-actin were obtained from Cell Signaling Technology , Abgent , and Sigma , respectively. DZNep  was used as EZH2-specific inhibitor. Five human GC cell lines were employed. The cell lines BGC823, MGC803, SGC7901 were purchased from the Cell Bank of Shanghai . The MKN45 and AGS cell lines were purchased from American Type Culture Collection . Cells were cultured in Dulbecco’s modified Eagle’s medium  with 10% fetal bovine serum  and were maintained in a humidified 5% COth edition of the American Joint Committee on Cancer System (AJCC) staging system [The study was scrutinized and approved by the Hospital Bioethics Committee, and patient consent was obtained prior to the initiation of the study. The prospective study group comprised 63 patients who had primary gastric adenocarcinomas and underwent gastrectomy between January and December in 2008 at the Department of Gastroenterology Surgery, Surgical Oncology Laboratory, Beijing Cancer Hospital and People’s Hospital. The inclusion criteria for the study were as follows: (a) the patient had no concurrent diseases precluding the administration of systemic chemotherapy, and (b) the patient had not received preoperative radiotherapy. All patients were followed-up prospectively for a maximum period of 66 months. Tissue samples of GC as well as adjacent non-cancerous  gastric tissues were fixed in 10% neutral formalin, processed for paraffin sections, and used for histopathology and immunohistochemistry (IHC) studies. Clinicopathological information was obtained from medical charts, and histopathological examination was performed according to the 6g system , 43. AllFormalin-fixed paraffin-embedded sections (4-μm-thick) from samples were collected for IHC experiments. STAT3 and EZH2 were detected using rabbit polyclonal antibodies. Briefly, sections were incubated with rabbit anti-STAT3 and anti-EZH2 (1:200) overnight at 4 °C. Normal goat serum was used as a negative control. After washing, tissue sections were treated with biotinylated anti-rabbit secondary antibody  followed by further incubation with streptavidin-horseradish peroxidase complex , and then immersed in 3,3-diaminobenzidine, counterstained with 10% Mayer’s hematoxylin, dehydrated, and mounted. Both nuclear and cytoplasmic staining was observed for STAT3, while only nuclear staining was seen for EZH2. The intensity of immunoreactivity was assessed for EZH2 and STAT3 as follows: high expression, ≥ 50% cells showing intense immunoreactivity; low expression, < 50% cells showing intense immunoreactivity. The mean percentage of positive tumor cells was determined in at least five areas using a high-power field microscopy. Immunopositivity was independently evaluated by two senior pathologists.Total RNA was extracted from cells by using Trizol reagent  according to the manufacturer’s instructions. The extracted RNA was pretreated with RNase-free DNase, and 5 μg RNA from each sample was used for cDNA synthesis primed with random hexamers. PCR amplification of STAT3 or EZH2 cDNA using STAT3 or EZH2-specific primers , 125 mM NaCl, 0.1% Triton-X, and 5 mM EDTA, and treated with 1% (v/v) protease inhibitor and 1% (v/v) phosphatase inhibitor cocktail II (Sigma). Subsequently, 50 μg total protein from each cell-lysate was separated by SDS-PAGE, followed by electrotransfer onto a PVDF membrane . Rabbit polyclonal antibodies against STAT3 (1:1000 dilution) and EZH2 (1:600 dilution) were used as primary antibodies; anti-β-actin  was used as a control. Horseradish peroxidase-conjugated anti-rabbit/mouse IgG antibody  was used as a secondary antibody. Immunodetection was accomplished using enhanced chemiluminescence (Amersham Biosciences).SGC7901 cells were seeded into 96-well culture plates, and 3- -2,5-diphenyltetrazolium bromide (MTT) was added to each well containing cells treated with DZNep and/or siRNA mimics for STAT3 (siSTAT3) from 24 to 120 h; plates were then further incubated for 4 h at 37 °C. Subsequently, MTT was removed and dimethyl sulfoxide  was added to the formazan product. The absorbance was read at 490 nm/570 nm using microplate readers .6 cells) and re-suspending in 100 μl of phosphate-buffered saline (PBS). Afterward, 5 μl of Annexin V (1 μg/ml)  was added and incubated at RT for 15 min, then 10 μl of propidium iodide  was added and incubated for additional 5 min at room temperature in the dark. Finally, the cells were subjected to flow cytometry (FCM) to measure the apoptosis rate with an Epics-XL-MCL flow cytometer .SGC7901 cells were treated with DZNep and or siSTAT3 for 48 h, followed by harvesting, counting . The SGC7901 cells, were treated with DZNep (500 μM) and/or siSTAT3 mimics for 48 h. Subsequently, the cells were sorted using a FACSCalibur , and cell-cycle profiles were analyzed using WinMDI v 2.9 software .EZH2 promoter from −1702 to +52 and inserting it between the SacI and XhoI restriction sites of the pGL3-Basic vector (Promega). After cloning and confirmation of the nucleotides of the EZH2 promoter by sequencing, the construct was named EZH2-promoter-Luc . Promoter constructs for the assays were generated by cloning the region of the human EZH2-promoter-Luc and 6 ng Renilla luciferase plasmid pRL-TK using Lipofectamine 2000 transfection reagent (Life Technologies), with or without IL-6 treatment (1000U/ml). Cells were also transfected with 300 ng of siSTAT3 to inhibit STAT3 signaling (Life Technologies). At 24 h after transfection, the cells were washed, lysed, and evaluated sequentially for firefly luciferase and Renilla luciferase activities (Promega) using a BD Monolight 3010 luminometer (BD Biosciences) or a Lumat luminometer . The results obtained were normalized for Renilla luciferase activity and expressed relative to the activity of the untreated cell group transfected with EZH2-promoter-Luc. Promoter activity was reported as mean ± SD values.SGC7901 cells were co-transfected with 800 ng of EZH2 gene. Real-time PCR was performed on non-amplified Stat3, rabbit IgG, and Input of SGC7901 cells. ChIP DNA-enrichments was used SYBR Green Master Mix reagents with an ABI PRISM 7900HT sequence detection system . The ready-to-use primers were employed for studying transcriptional regulation of EZH2 at or around its transcriptional start site (TSS). The enrichment of Stat3 motifs binding at or around the TSS of EZH2 could be reliably detected and quantified by ChIP real-time PCR assay or the PCR products were run on 2% agarose gel in 1× TBE buffer.SGC7901 parent cells or knockdown of STAT3 in cells, followed by stimulation with or without IL-6 (1000 U/ml), were fixed with 1% formaldehyde and lysed for 10 min at 37 °C, and sonicated to obtain sheared DNA fragments of approximately 200 ~ 1000 bp. The chromatin was then incubated and precipitated with antibodies against the STAT3 antibody or IgG (Santa Cruz), after which DNA-protein immunocomplexes were collected, using protein A/G-agarose beads , and treated with RNase A (Sigma) and proteinase K (Sigma), primers were designed for Stat3-ChIP enriched the promoter region of EZH2, containing three conserved STAT3-binding motifs, was used . For the mobility shift assay for STAT3-DNA binding activity, a nucleotide sequence corresponding to the 5′-flanking region of human 2, 50 mM NaCl, 0.5 mM EDTA, 4% glycerol, 0.5 mM DTT, and 0.5 mg of poly (dI:dC) for 10 min, and the biotin-labeled probe was added to the mixture; samples were incubated for 20 min at room temperature. The protein-DNA complexes were then electrophoresed on a 7% acrylamide gel and analyzed by autoradiography.SGC7901 cells were pre-treated with or without siSTAT3, and nuclear protein was extracted. EMSA was carried out with a Gel Shift assay System (Promega) in accordance with the manufacturer’s recommendation. Nuclear protein (10 μg) was pre-incubated in a final volume of 15 μl of buffer containing 10 mM Tris-HCl (pH 7.5), 1 mM MgCl5 SGC7901 cells in 1 ml of buffer containing at least 40 mM Ca2+. Cells were then added to a tube containing 5 ml of fluorescent-Annexin V/PI. Fluorescence was quantified on a Becton Dickinson FACScan flow cytometer (BD Biosciences) for at least 10,000 events.Annexin V-FITC/PI analysis  was used to measure apoptosis induction in the siSTAT3 transfected groups with or without the EZH2 inhibitor, DZNep treatment. Harvested cells were washed twice in buffer and resuspended at a concentration of 5 × 10pNA and Z-LEHD-pNA in microplates according to the manufacturer’s protocol. The plates were read at OD 405 nm.After cells were transfected with siSTAT3 (or scrambled siRNA), they were cultured for 12 h in 6-cm dishes containing serum-free medium with or without DZNep. The cells were washed with 1× PBS and resuspended in lysis buffer. Caspase-3/9 activity was assessed using a Colorimetric CaspACE™ assay System (Promega). The lysate was mixed with Z-DEVD-2) and Mann-Whitney U-tests. Spearman rank correlation coefficients and Fisher’s exact tests were used to assess significant associations between different clinicopathological variables. Kaplan-Meier survival analysis, followed by the log-rank test for Pair-wise comparisons, was used to analyze the influence of STAT3 and EZH2 protein expression on overall survival of GC patients. A P-value of < 0.05 was considered significant and exact two-sided P values are reported.Data were analyzed using SPSS . Statistical significance was evaluated using chi-square (χ


Culaea inconstans) that inhabit them, provide a unique aquatic system whereby populations are highly isolated from one another. These heterogeneous and extreme environments are prone to winterkills, an event whereby most of the fish die and frequent bottlenecks occur. In this study, we characterized the genetic population structure of brook stickleback among several lakes, finding that the species is hierarchically influenced by within‐lake characteristics in small‐scale watersheds. Landscape genetic analyses of the role of spatial features found support for basin characteristics associated with genetic diversity and bottlenecks in 20% of the sampled lakes. These results suggest that brook stickleback population genetic patterns may be driven, at least in part, by ecological processes that accelerate genetic drift and landscape patterns associated with reduced dispersal. Collectively, these results reinforce the potential importance of connectivity in the maintenance of genetic diversity, especially in fragmented landscapes.Investigating the consequences of landscape features on population genetic patterns is increasingly important to elucidate the ecological factors governing connectivity between populations and predicting the evolutionary consequences of landscapes. Small prairie lakes in Alberta, Canada, and the brook stickleback ( Lakes are intermittently connected by streams and also prone to winter anoxia resulting from ice cover  versus within‐lake processes . This approach permitted testing these landscape genetic consequences in brook stickleback inhabiting fragmented and heterogeneous environments.22.1N = up to 48 per site) were haphazardly selected from the captured group and euthanized using an overdose of eugenol in accordance with the Canadian Council for Animal Care before being preserved in 95% ethanol for subsequent DNA isolation. All samples were collected under our fish research license issued by Alberta Environment Sustainable Resource and Development Fish and Wildlife, while all methods were approved by the University of Calgary's Life and Environmental Sciences Animal Care Committee.Baited Gee minnow traps were set in 52 lakes near Caroline, Alberta, during the summers of 2011 and 2012 Figure . Sites w2.2To facilitate tests of the effects of environmental variables on genetic diversity, we generated 10 landscape metrics using a variety of data sources  were assimilated into individual features using the near to table tool in ArcGIS. Lakes that had a nearest stream value of zero  were classified as connected, and those with values greater than zero were classified as unconnected. Hydrological distances between lakes that were connected to a stream network were calculated by first converting the stream network polyline layer into a network dataset using ArcCatalog 10. The closest facility analysis was then used, where lake features were converted to points using the feature to point tool and added to the closest facility analysis using the add locations tool . The closest facility tool is typically used to identify the shortest distance following a road network between locations of emergencies (incidents), and the facilities required for assistance for such incidents. All lake points where added both as incidents and as facilities, and the stream network was used as the road network. Using the closest facility analysis, pairwise distances via known lotic pathways were calculated between lakes.Euclidean distances between all lentic water bodies, distances to the nearest five lentic water bodies, and water body connectivity to stream networks, were calculated using the Surrounding topographic complexity was estimated because gene flow may be restricted by slope  drainages. We used a measure of flow accumulation to estimate drainage and a measure of flow direction to estimate flow accumulation in ArcGIS. We converted these layers to define “pour points” in the watershed delineation according to three different scales of flow accumulation  representative of large‐, medium‐, and small‐scale watersheds, respectively  and five for ninespine stickleback (Pungitius pungitius). DNA was isolated using standard phenol–chloroform DNA extraction methods , 0.03 units/μl NEB TAQ DNA polymerase, and 20 ng of template DNA. Reactions were carried out with the following PCR protocol: 93°C for 180 s, 95°C for 30 s, 56°C for 30 s, 72°C for 30 s, then five cycles of 94°C for 30 s, 56°C for 30 s, 72°C for 30 s, then 35 cycles of 90°C for 30 s, 56°C for 30 s, 72°C for 30 s, and a final extension phase of 72°C for 10 min and 4°C until removal from the thermocycler. Ninespine stickleback primers utilizing an M13 fluorescence tag required the following adjusted reaction conditions of 0.175 μmol/L forward primer and 0.5 μmol/L of the M13 tag, and an additional cycle prior to the 10‐min 72°C stage of the PFC8 PCR protocol, with nine cycles of 94°C for 30 s, 53°C for 45 s, and finally 72°C for 45 s being added. PCR products and alleles were separated and genotyped via electrophoresis on an Applied Biosystems 3500xl sequencer. Alleles were visualized and scored against an internal size standard using GENEMAPPER 4.1 (Applied Biosystems).We characterized 11 polymorphic loci for brook stickleback Table , of whic2.4p‐values from multiple comparisons from within populations were corrected using a false discovery rate procedure . Each STRUCTURE run for each putative population number was replicated 10 times. STRUCTURE provides the natural logarithm of the probability (ln(P)) for each independent run. The runs with the same assumed number of populations (K) with the highest ln(P) is regarded as the most likely number of populations. This method has been shown to be particularly sensitive to isolation‐by‐distance patterns  were used to assess population structure.Population structure was assessed using assignment methods implemented in STRUCTURE , which has been demonstrated to provide more accurate FST estimates than uncorrected data  and allelic richness. These metrics of genetic diversity were used because, for example, heterozygosity decreases at a slower rate than allelic richness during a bottleneck  that a population genetic analysis would have been uninformative. Sampled lakes were generally small, ranging in area from 0.005 to 3.9 kmMicrosatellite polymorphism varied from five to 21 alleles per locus, with a mean of 12 Table . PopulatFST estimates were variable, ranging from a maximum of 0.563 to a minimum of 0.000. The STRUCTURE analysis provided a series of ln(P) values that generally increased as the assumed number of K increased, reaching a peak ln(P) at an assumed K of 20. The Evanno method also best supported a K of 20. Lakes that shared membership in the same cluster were generally located within close proximity of one another , explaining 78.20%, 77.07%, and 79.23% of the total molecular variance in the large‐, medium‐, and small‐watershed tests, respectively. All three tests revealed that among‐lake molecular variation was highly significant, within at least one watershed , explaining 16.50%, 13.25%, and 11.32% of the total variance enumerated in the large‐, medium‐, and small‐scale tests, respectively. Significant variation was also explained among watersheds at the large‐, medium‐, and small‐watershed tests , although only 5.30%, 9.68%, and 9.45% of the total variation was explained. Pairwise FST/1‐FST values did not vary significantly with either pairwise stream network or Euclidean distance , indicating the absence of an isolation‐by‐distance pattern.AMOVA results indicated that molecular variation was influenced by population (Table 2/lake area)  Figure , maximum) Figure , and the) Figure .Z23 = −2.11, p = .04).Outcomes of bottleneck tests were dependent on the assumed mutation mode (data not shown). Tests assuming the IAM detected ten populations that exhibited excess heterozygosity. Tests assuming the TPM detected five populations that exhibited excess heterozygosity, all of which were also significant when the IAM was assumed. Finally, all tests assuming SMM detected heterozygosity deficiency. All other lakes that demonstrated a significant heterozygosity deficiency, and did not have significant excess heterozygosity assuming either the IAM or TPM. Putative bottlenecks were significantly correlated with maximum lake depth  versus within‐lake processes . Hierarchal genetic structure patterns between lakes supported the SHM , and demonstrate the continuum between the SHM and DVM, where connectivity is restricted but populations do not appear to be completely isolated. These results are not completely unexpected because, although the focal landscape is highly fragmented, brook stickleback—in contrast to the pupfishes of Death Valley Turner, —exhibit The relationship between populations was not consistent with isolation by distance. No isolation‐by‐distance patterns were detected by stream network or Euclidean distance. STRUCTURE results suggest that most lakes contain groups of individuals that breed more within their group than outside of it. AMOVA results emphasized this conclusion, estimating  that lake explained approximately 70% of the molecular variation observed. AMOVA results also indicated that a significant but small proportion (<10%) of molecular variance was explained between watersheds at all scales (approximately 15% of variation was explained between lakes within watersheds), indicating that watersheds structure populations together hierarchically. Divergence has evidently been accumulating among watersheds and the watershed genetic divergence is arguably pretty large given the young age of this system that was likely colonized from Mississippian refugium following the retreat of the last glacial maximum (12–15 Kya) . Thus, more precise measures of lake could overcome this caveat to improve estimation of lake shape variables. Nonetheless, these patterns collectively demonstrate the importance and impact of watershed scale in structuring brook stickleback genetic variation.5Brook stickleback populations appear to be influenced by within‐lake characteristics, although weak hierarchical structuring by small‐ and medium‐scale watersheds was apparent, suggesting some gene flow between nearby populations. Gene flow between brook stickleback populations is likely the result of dispersal through the stream network to nearby lakes. This study supports the findings of other studies demonstrating the importance of hydrological structure in determining population structure (e.g., Castric et al., Landscape fragmentation has been linked to decreased (e.g., Keller & Largiadèr, None declared. Click here for additional data file.


Dissostichus eleginoides have shown that oceanographic and geographic discontinuities drive in this species population differentiation. Studies have focused on the genetics of D. eleginoides in the Southern Ocean; however, there is little knowledge of their genetic variation along the South American continental shelf. In this study, we used a panel of six microsatellites to test whether D. eleginoides shows population genetic structuring in this region. We hypothesized that this species would show zero or very limited genetic structuring due to the habitat continuity along the South American shelf from Peru in the Pacific Ocean to the Falkland Islands in the Atlantic Ocean. We used Bayesian and traditional analyses to evaluate population genetic structure, and we estimated the number of putative migrants and effective population size. Consistent with our predictions, our results showed no significant genetic structuring among populations of the South American continental shelf but supported two significant and well-defined genetic clusters of D. eleginoides between regions . Genetic connectivity between these two clusters was 11.3% of putative migrants from the South American cluster to the South Georgia Island and 0.7% in the opposite direction. Effective population size was higher in locations from the South American continental shelf as compared with the South Georgia Island. Overall, our results support that the continuity of the deep-sea habitat along the continental shelf and the biological features of the study species are plausible drivers of intraspecific population genetic structuring across the distribution of D. eleginoides on the South American continental shelf.Previous studies of population genetic structure in  The long-held idea that the deep-sea environment is composed of spatially homogeneous habitats that remain stable for long periods of time  led to tChaenocephalus aceratus, Notothenia coriiceps, and Lepidonotothen larseni distributed in the Southern Ocean , facilitating gene flow . In addirn Ocean , as well mawsoni , and evelanticus , and Berplendens .Dissostichus eleginoides Smitt, 1898, is the most productive and lucrative fishery in the entire Antarctic, Southern Ocean, and southern portions of the oceans around the southern South American cone. This species is vulnerable to overfishing because of its size, long life span, relatively small numbers of eggs and delayed onset of reproductive maturity . Later, Population genetics studies of D. eleginoides populations across their Southeastern Pacific Ocean distribution. The only study carried out in the Southeastern Pacific Ocean was developed by D. eleginoides populations across their Southeastern Pacific Ocean distribution.All previous studies discussed above have focused mainly on islands of the Southern Ocean, leaving a distinct gap in our knowledge of the genetic structure of the D. eleginoides to test whether this species shows population genetic structure on the South American Plateau. We hypothesized that D. eleginoides would show limited genetic structure due to the continuity of suitable habitats along the South American continental shelf, from Peru in the Pacific Ocean southward and eastward to the Falkland Islands in the Atlantic Ocean  in collaboration with the Instituto del Mar del Peru (IMARPE). Tissue samples from Chilean EEZ were obtained during scientific research programs with the permission of the Chilean Fishery Government and obtained by the Instituto de Fomento Pesquero (IFOP). Additional tissue samples from the Falkland Islands and South Georgia Island were obtained from commercial long-liner vessels within the Total Allowed Catch quotas assigned by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR).Dissostichus eleginoides were sampled from a portion of the species range around South America and the South Georgia Island , expected (HE), and observed (HO) heterozygosity to determine the genetic variability of the population surveyed; these population summary statistics were calculated for each locus and population using GENALEX v6.5 software (LD) association by testing the hypothesis that genotypes at one locus are independent from genotypes at another locus using GENEPOP v3.1 . We obtained FST and RST pairwise indices in ARLEQUIN to estimate the degree of genetic differentiation among samples locations. The probability values for FST and RST were obtained by permutation tests with 10,000 replicates. We applied the sequential Bonferroni correction for multiple comparisons  present in our data set, we used two Bayesian clustering methods, one in the program GENELAND v1.0.7 , 5 × 106 MCMC iterations, a thinning interval of 1,000, the maximum rate of process Poisson fixed at 357, and the maximum number of nuclei in the Poisson-Voronoi tessellation fixed at 1,071. Following recommendations of K in the study area, the spatial model, and the null allele model. Finally, we plotted a map of South America over the output of GENELAND, in order to visualize the results in the context of geography.To infer the most likely number of genetic clusters  in R, followed by cluster matching and permutation in CLUMPP  in ADEGENET v2.0, which does not make assumptions of HWE and LD (FST∕1 − FST) and the logarithm of the geographic distance among sampling sites. To identify significant correlations, Pearson’s correlation coefficient, r was calculated in the software ZT . We performed Mantel tests in order to test for two processes that can arise in an IBD pattern: (a) a continuous cline of genetic differentiation or (b) the existence of well differentiated and disjunct populations  of each location and number of clusters were determined using the LD method  for each population were estimated using NEESTIMATOR ∕8μ  1 × 10−2 and (b) 1.0 × 10−5 mutations / locus / generation, both of which were based on D. eleginoides genome have been estimated in the literature; therefore, we chose these broad range of mutation rates reported for marine, freshwater and anadromous fishes in D. eleginoides.The effective population size ∕8μ ; where Hp ≤ 0.008): cmrDe4 in SP; cmrDe9 in IQ; cmrDe30 in IQ, FI, and SGI; cmrDe2 in PW; and To5 in FI , STRUCTURE software, found the same two genetic clusters , even when we excluded the comparisons given by the South Georgia cluster   .A total of 12 putative migrants were detected in all samples . The number of immigrants from the obtained clusters ranged between 2 and 8 individuals in the smallest  and largest cluster , respectively. Each cluster showed a high percentage of self-assignment, with 89% of the SGI cluster including individuals from the South Georgia Island and 99.3% of the South American cluster composed by locations from South America this clearly supported patterns of genetic structure indicated by GENELAND and STRUCTURE. The same pattern of genetic structure was also supported when analyses were performed based exclusively on sampling locations . We deteNe based on linkage disequilibrium was variable across locations, and ranged from 133 in the Gulf of Penas to infinite for the localities of southern Peru, Diego Ramírez Islands, and the Falkland Islands  compared to the South Georgia Island  (HE = 0.68–0.79) described by HE by location  . These vIndividuals from the SGC showed less allele number and privative alleles than the SAC , S3B. ThD. eleginoides and its distribution on the South American continental shelf . Conversely, our results suggest that the deep-sea habitat continuity on the South American continental shelf, in addition to the inherent biological features of D. eleginoides should be key factors explaining the lack of genetic differentiation across this large area. The genetic cluster around the South Georgia Island  is clearly isolated from the cluster associated with the South American continental shelf . The continuity of the sea floor of these two clusters are separated by abyssal depths , Southern Ocean (South Georgia), and sub-Antarctic islands and seamounts of the Indian sector (D. eleginoides distributed across the Southeastern Pacific Ocean.The two well-differentiated clusters are located (i) on the South American continental shelf  and around to the South Georgia Island. Along the South American continental shelf, two biogeographic breaks have been described consequence of the currents  and upwelling patterns  of these areas which has been correlated with changes in species composition  can explain the asymmetrical migration from SAC to the SGC given that it has a clockwise direction. Thus, individuals that go into the AAC may move from west to east  all possible pairwise combinations (in red SGI), and (B) pairwise excluding South Georgia Island samples.Click here for additional data file.10.7717/peerj.4173/supp-7Figure S3Blue bar correspond to South American Cluster (SAC) and red bar correspond to South of Georgia Cluster (SGC).Click here for additional data file.


Acacia tortilis, a keystone desert tree, at the northern edge of its global distribution, where the population is under threat from climatic, ecological, and anthropogenic changes. We sampled A. tortilis from 14 sites along the Dead Sea region and the Arava Valley in Israel and in Jordan. In addition, we obtained samples from Egypt and Sudan, the hypothesized origin of the species. Samples from all sites were genotyped using six polymorphic microsatellite loci.Our results indicate a significant genetic structure in A. tortilis along the Arava Valley. This was detected at different hierarchical levels—from the basic unit of the subpopulation, corresponding to groups of trees within ephemeral rivers (wadis), to groups of subpopulations (communities) that are genetically more connected relative to others. The latter structure mostly corresponds to the partition of the major drainage basins in the area. Network analyses, combined with classical methods, allowed for the identification of key A. tortilis subpopulations in this region, characterized by their relatively high level of genetic diversity and centrality in maintaining gene flow in the population. Characterizing such key subpopulations may enable conservation managers to focus their efforts on certain subpopulations that might be particularly important for the population’s long-term persistence, thus contributing to species conservation within its peripheral range.Genetic diversity and structure of populations at the edge of the species’ spatial distribution are important for potential adaptation to environmental changes and consequently, for the long-term survival of the species. Here, we combined classical population genetic methods with newly developed network analyses to gain complementary insights into the genetic structure and diversity of  Identifying the spatial patterns of a species’ genetic diversity can inform conservation priorities, enabling management and conservation efforts to focus on particular populations ,2. Of paA growing number of studies across species’ geographical ranges have reported high levels of genetic variability in peripheral areas –7. They Revealing the genetic diversity of peripheral populations, in particular of species that are of conservation concern, may provide an important basis for protection and management strategies. Specifically, understanding the genetic structure of edge populations, at different hierarchical levels , and characterizing important subpopulations that are critical for maintaining gene flow and sustaining genetic diversity can contribute to the allocation of conservation and management resources to specific subpopulations within peripheral regions.nodes, which are connected by links or edges. In a population-genetic context, a node can represent either an individual or a group of individuals defined by habitat patches, sampling sites, or subpopulations; edges represent genetic similarity among individuals or genetic connectivity between patches/sites, depending on their role in the network. Network methodologies have been applied in identifying and analyzing population structure [Network theory  approaches have been increasingly applied in conservation biology and population genetics ,13. A netructure , as welltructure ,15,16.Fst, AMOVA), network methods have been used for inferring population genetic structure at different hierarchical levels. Specifically, network methods have been developed for analyzing structure at the individual-subpopulation level , and folations; . Howeverparisons ,16. One of nodes . Communiof nodes  and to aof nodes ,18.random walk betweenness centrality measure  is a mere  Hayne trees , as well as indicating to changes in the timing and duration of flood events, which are critical for acacia trees [The Dead Sea region and the Arava Valley of Israel and Jordan  hosts a ortilis, ), growinortilis, . Consideortilis, , the treortilis, –26, but a lis, [-1  has showia trees . Studiesia trees , as wellia trees . Reductiia trees ,33, as wia trees .A. tortillis in the Arava Valley of Israel and Jordan, aiming to guide future management efforts in the region. We examined connectivity within the population at different hierarchical levels (subpopulations and communities of subpopulations) and identified key subpopulations that may be particularly important for maintaining gene flow and diversity at the northern edge of A. tortilis global distribution.In this study, we applied a combination of classical population genetic methods and network analysis procedures in order to understand the population structure of This study was conducted along the Dead Sea region, the Arava Valley and the Gulf of Eilat/Aqaba in Israel and Jordan . The eleAcacia tortilis’s northernmost distribution area . Fragments were scored using Peak Scanner v1.0 software  to obtain a DNA profile of each individual tree. To account for error rates in the genotyping data set, a random selection of ~15% of all samples (n = 48) was independently re-genotyped [Total genomic DNA was extracted from each sample, using 20 mg of silica gel-dried leaf material, with the DNeasy Plant Mini Kit . DNA samples were amplified using polymerase chain reactions (PCR) with eight fluorescent microsatellite primers developed for s et al. . PCR proenotyped . EstimatTests for linkage disequilibrium among all loci pairs in all sampling sites and tests for significant deviations from the Hardy-Weinberg equilibrium (HWE) at each locus in each sampling site were performed using Genepop . PossiblO) and expected (HE) heterozygosity were calculated for each sampling site in GenAlEx v6.501 [To determine the genetic diversity levels in the population, allele frequencies, the average number of unique alleles, and the observed  between subpopulations were calculated [To examine genetic relationships between subpopulations , overall and pairwise estimates of genetic divergence  and the total homozygosity of the total population (HT). Hence, the upper bounds of Fst were calculated for each locus across the population and then averaged over all loci [It has been argued that estimating  Hedrick  suggestelesworth  suggesten et al.  showed tall loci .Fst values [cGD) obtained using a network analysis [A. tortilis was constructed using POPGRAPH [cGD was extracted between each pair of subpopulations as estimated by the shortest path connecting them within the population graph. STRUCTURE v2.3.4 [a priori information about sampling location [Isolation-by-distance between subpopulations was tested using the Mantel permutation method implemented in GenAlEx v6.501 . Two dift values ; and 2) analysis . A populPOPGRAPH  . We constructed a population graph for A. tortilis using POPGRAPH [a priori assumptions about the geographic arrangement of the subpopulations. We used a spectral decomposition method [Network analysis was used to identify “groups of subpopulations”  was characterized by:Network methods were used to identify key (“hotspot”) subpopulations of particular importance for maintaining gene flow and genetic diversity in the The level of within-site genetic diversity as determined by heterozygosity and allelic richness.The degree of centrality for gene flow in the network, determined by the RWB measure .Betweenness measures the amount of flow through nodes assuming that flow only occurs along the shortest paths, where a shortest path is a sequence of edges that connects nodes in a graph and minimizes the weight of the edges from which it is composed. However, it has been noted that since gene flow in natural populations does not act in such a confined and deterministic way, a more appropriate measure for modeling gene flow is one that does not limit flow only to the shortest paths but rather considers random walks, such as RWB [Various centrality measures have been developed to enable the identification of central nodes in networks ,19. Sinch as RWB . Other mh as RWB . HoweverNo significant linkage disequilibrium was detected for any pair of loci. Two of the eight loci, L3 and L9, deviated significantly from HWE in all subpopulations. The MICRO-CHECKER analysis suggested that these loci might have been affected by null alleles, and consequently, they were removed; in total, six loci were included in the analyses presented below.The mean number of alleles per locus was 18.83, ranging from 15 to 26 . An averAnalysis at the individual-subpopulation level aimed to examine whether sampling sites could be regarded as basic units of population structure, was performed by testing the null hypothesis that population structure is independent of assignment of individuals to sampling sites. The analysis examined different genetic similarity networks with different edge-removal thresholds, and in each, the null hypothesis was rejected . The calculated upper limit (F) for Fst revealed that Fst was bound by the level of homozygosity and not the frequency of the most common allele, with results for the upper limits of 0.341 and 0.429, respectively. Hence, the Fst obtained for this population (0.036) was evaluated within the range of 0 to 0.341 instead of 0 to 1.Most pairwise Fst values, correlated positively with the geographic distance between them , to groups of subpopulations that are genetically connected, located along the species’ geographic distribution. Subpopulations of particular genetic importance were identified in each of the communities. These key subpopulations may play an important role in maintaining gene flow and diversity in the population.The combination of classical population genetic procedures with network analysis procedures used here enabled us to gain complementary insights into the population genetic structure and diversity of A. tortilis in Israel and Jordan, contrary to what would be expected from apparently isolated groups of trees within wadis, growing at the northern edge of the population’s world distribution. The estimates of the heterozygosity values were much higher than those found for related species such as A. senegal [A. raddiana  supported this basic level of differentiation (subpopulations). Additionally, evidence was provided for a north-south gradient of differentiation. Firstly, STRUCTURE identified two clusters: the northern subpopulations were strongly associated with the first cluster and the southern subpopulations with the second cluster, while the central subpopulations had intermediate associations with both clusters. These results seem most consistent with the stepping-stone model of gene flow [cGD) and linearized Fst. Results showed that the network-based measure, cGD, provided stronger evidence for IBD than the traditional Fst measure, as suggested by [cGD is based on a simultaneous analysis of the entire data set [A significant genetic structure was detected in the ene flow . Secondlested by ,70, sincdata set .A. tortilis communities and the division into the four main drainage basins in the area  or a single node that connects two regions but with relatively little gene flow between them . Thus, RWB may be a more appropriate measure than other connectivity measures for identifying conservation priorities, in order to maintain gene flow throughout a population. When analyzing population genetic data for the purpose of prioritizing management efforts, one should consider the complexity in defining key subpopulations. As highlighted in this study, subpopulations with high genetic diversity are not necessarily associated with high gene flow with other subpopulations.A. tortilis at the edge of its global distribution, at different hierarchical levels: from the basic unit of the subpopulation, corresponding to groups of trees within ephemeral rivers, to groups of genetically connected subpopulations (communities). These results may help in making conservation decisions aimed at maintaining gene flow throughout the population. The use of a combination of classical and novel network theory applications provided complementary insights into understanding patterns of genetic diversity and levels of connectivity within a species. Specifically, these methods, combined, allowed for the identification of key A. tortilis subpopulations, which have relatively high level of genetic diversity and are central in maintaining gene flow in the entire population. Identifying key subpopulations may enable conservation managers to focus their efforts on specific subpopulations that are potentially crucial for the species conservation within its peripheral range.In this study we revealed the population structure of S1 Fig(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 Figa priori assumptions or underlying model. The distribution of communities in the population is not independent of sampling sites, with a rejection of the null hypothesis at p-value < 0.0001 Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 TableFst values are below the diagonal; p-values based on 999 permutations are shown above the diagonal.(DOCX)Click here for additional data file.S4 TableThe table lists, for all tested edge-removal thresholds, the number of communities detected using the FastGreedy algorithm in Netstruct, the number of connected nodes in the network after edge removal, and the p-value of the Fisher’s exact test of rejecting the null hypothesis that detected communities that are not dependent on sampling sites. All p-values are significant (p < 0.05), regardless of the edge-removal threshold used.(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


STAT3 is a pleiotropic transcription factor involved in homeostatic and host defense processes in the human body. It is activated by numerous cytokines and growth factors and generates a series of cellular effects. Of the STAT-mediated signal transduction pathways, STAT3 transcriptional control is best understood. Jak kinase dependent activation of STAT3 relies on Y705 phosphorylation triggering a conformational switch that is stabilized by intermolecular interactions between SH2 domains and the pY705 motif. We here show that a second tyrosine phosphorylation within the SH2 domain at position Y640, induced by Tyk2, negatively controls STAT3 activity. The Y640F mutation leads to stabilization of activated STAT3 homodimers, accelerated nuclear translocation and superior transcriptional activity following IL-6 and LIF stimulation. Moreover, it unlocks type I IFN-dependent STAT3 signalling in cells that are normally refractory to STAT3 transcriptional activation. All STATs are characterized by six conserved domains: an N-terminal domain (NTD), a coiled-coil domain (CCD), a DNA-binding domain, a linker domain (LD), a Src homology 2 domain (SH2) and a C-terminal transactivation domain (TAD). Different ligands can activate STAT proteins, including growth factors, hormones and cytokines. Also mutated kinases, observed in neoplastic disease, are able to activate STAT proteins4. Abnormal STAT signalling may lead to chronic inflammatory diseases and cancer development6. Therefore the role of STATs in numerous physiological and pathological processes including cell survival, immunity, angiogenesis, metastasis and oncogenesis is being investigated in great detail11. According to the canonical model, STATs are activated by cell-surface receptors associated with tyrosine kinases of the JAK (Janus Tyrosine Kinase) family. JAK members include JAK1, JAK2, JAK3 and TYK27. Following structural analyses of JAKs, four distinct domains are identified: an N-terminal FERM domain and an SH2-like domain responsible for binding to the intracellular region of the cognate receptor12, followed by a central kinase-like (KL) domain and a C-terminal tyrosine kinase (TK) domain. The KL domain harbors an auto- and trans-regulatory activity that either keeps the protein in an inactive or low phosphorylated state in absence of ligand13 or stabilizes the enzyme in an activated state upon cytokine stimulation14. Upon ligand-binding to the receptor, JAKs are activated by trans-phosphorylation of key tyrosines in the activation loop of the TK domain15 and in turn phosphorylate specific tyrosine residues on the intracellular domains of the associated receptor chain. This process creates docking sites for the recruitment of proteins containing a Src homology 2 (SH2) domain, such as STATs. Upon recruitment to the receptor, STATs are phosphorylated and can form activated parallel homo- and hetero-dimers stabilized by reciprocal, intermolecular interactions between the phosphotyrosine of one STAT and the SH2 domain of another STAT. These homo- and hetero-dimers can subsequently translocate to the nucleus and initiate transcription by binding specific sites in the promoters of target genes. In non-canonical models, some features differ from the above outlined mechanism. For instance, also non-phosphorylated STAT dimers are reported to translocate to the nucleus and contribute to gene regulation17. STATs may control gene expression not only as dimers but also as tetramers at particular gene promoters18 or even as conglomerates of a higher molecular mass, called “statosomes”19. STAT3 is known to be abnormally activated in autoimmune diseases, chronic inflammation and in a large percentage of human cancers. This is mostly the consequence of aberrant tyrosine phosphorylation by oncogenic receptor tyrosine kinases, JAKs and Src family kinases21 rather than mutations in STAT3 itself. Similar to the other STAT members, STAT3 activity is tightly controlled by posttranslational modifications including phosphorylation at Y70522 and S72723, lysine acetylation26, methylation28, ubiquitination29 and SUMOylation30. A plethora of studies identify Y640 as a fundamental residue modulating STAT3 transcriptional activity in response to known STAT3 transcriptional activators like interleukin-6 (IL-6) and leukemia inhibitory factor (LIF) and even to type I IFNs35. Yet, its role and mechanism of action has remained elusive. Here we show that phosphorylation of Y640 is involved in the negative regulation of STAT3 transcriptional activity in response to different cytokines. Remarkably, only the kinase domain of TYK2 and not of the other JAK kinases is able to catalyze this alternative phosphorylation.The signal transducers and activators of transcription (STATs) are transcription factors able to carry specific signals from the cell membrane to the nucleus and induce gene transcription. The STAT protein family comprises seven different members  that are encoded by distinct genes39. To test whether additional tyrosines could be targeted by the JAKs, we used an unbiased approach by transiently co-expressing constitutively active tyrosine kinase domains (FLAG-TKs) of JAK1, JAK2, JAK3 or TYK2 together with either wild type (wt) or Y705F mutant E-tagged-STAT3 in HEK293T cells. Immunoprecipitation analysis demonstrated that, as expected, all four JAK TKs can induce Y705 phosphorylation of wt STAT3  was immunoprecipitated. Western blot analysis was used to reveal STAT3 phosphorylation and dimerization. As expected, both IFNα2 and LIF stimulation induced phosphorylation and dimerization of wild-type STAT3 and this was further enhanced when combining the STAT3 Y640F mutants , respectively. We focused on IFNα2, given the pronounced promoting effects of STAT3 Y640F on formation of activated homodimers as observed above. We found that the IFNα2-induced nuclear accumulation of STAT3 was significantly faster in HEK293T cells transiently transfected with Etag-STAT3 Y640F compared to Etag-STAT3 wt expressing cells. The latter cells showed a clear nuclear accumulation only upon 15 min of stimulation with IFNα2 Fig. . Of noteSOCS3 promoter. As shown in Fig. OCS3 promoter binding. For all the condition tested there was no evidence for binding at the endogenous promoter of the non STAT3-responsive HPRT gene . We further evaluated the effect of the phospho-mimicking STAT3 Y640E mutant on activation of the rPAP1 reporter Fig. . HEK293TSOCS3 gene in HEK293T cells transiently transfected with STAT3 wt or mutants and stimulated with either IFNα2 or LIF. Importantly, a dramatic increase was again observed when expressing the STAT3 Y640F mutant, whereby IFNα2-dependent transcription almost equalled the increased LIF-induced transcription  have been reported to influence STAT3 signaling, possibly involving cross-talk mechanisms, that ultimately govern the complex modulation of STAT3 activity58. Our findings now point to a surprising new role for TYK2 as a kinase that can impair STAT3 transcriptional activity. Enhanced STAT3 reporter activation and gene transcription are observed for IL-6 and LIF, which signal through gp130-based receptor complexes, but the enhanced transcriptional effect upon mitigation of the TYK2-targeted phosphorylation site is most pronounced in response to IFNα2. This is not unexpected since TYK2 is the key kinase associated to the IFNAR1 chain of the IFNAR complex. This is in contrast to signaling through gp130-based cytokine receptors complexes, which rely mainly on JAK1-JAK2 activity, where TYK2 functions more like a bystander kinase64.The precise role of STAT3 in type I IFN signaling has been a matter of debate due to the complex nature of STAT3 itself and to its diverse functions that strongly depend on the cellular contextSOCS3 promoter, as a consequence of the Y640F mutation (data not shown). Of note, phospho-specific antibodies directed against pY640 cross-react with the pY705 site and vice versa, since both are embedded in a highly similar sequence motif. Y640 phosphorylation can thus only be detected using a Y705F mutant and not using wt STAT3, possibly explaining why this regulatory phosphosite has remained undetected so far. Notwithstanding, the STAT3 Y640F mutation was already described as one of the most common STAT3 somatic mutations associated with the onset of several cancers, both hematological and solid32. Y640F mutated STAT3 was found to be constitutively phosphorylated at tyrosine 705 and to dimerize in absence of any stimulus. Moreover, STAT3 Y640F showed a hyper-responsiveness to IL-6 treatment, as compare to STAT3 wt, with an increased transcription of its downstream target genes. IL-6 dependent phosphorylation at S727 was not increased in STAT3 Y640F when compared to IL-6 activated STAT3 wt31. These results show that Y640 is a key residue in modulating STAT3 activity and thus can be target of a negative regulatory phosphorylation event. In this study, we demonstrate that STAT3 Y640F is able to respond to IFNα2 showing accelerated nuclear translocation and increased DNA-binding, as compared to IFNα2-activated STAT3 wt, ultimately resulting in a remarkable IFN-dependent transcriptional activity. In line with our findings, it was recently reported that in anaplastic large cell lymphomas (ALCLs) constitutively active Tyk2 fusion proteins are oncogenic by driving the STAT3 signaling pathway65.In line with these findings, only the TK domain of TYK2 and not of any other JAK was capable of inducing an alternative phosphorylation in STAT3 Y705F mutant co-expressing cells. This led us to hypothesize the presence of an alternative STAT3 phosphorylation site which, based on structural considerations involved in engaging the active STAT3 dimer, was identified as the Y640 residue. Its critical role in down-modulating STAT3 transcriptional activity was subsequently demonstrated using dimerization, nuclear translocation, ChIP, reporter and qPCR assays using the Y640F STAT3 mutant. In line with a receptor-proximal control mechanism, we could not detect any variability on histone acetylation or any variation of co-factor recruitment on the 66. It has already been reported that STAT molecules can be found intracellularly as preformed dimers in a latent and unphosphorylated state68. In the same context, STAT3 was found to have a peculiar behavior in being the only STAT protein with two different parallel orientations: one for the latent dimers and one for the activated dimers, both different from the antiparallel orientation of the latent dimers of STAT1 and STAT569. In STAT3, Y640 can be targeted by a downregulating phosphorylation, impairing the molecular switch needed for the formation of a parallel activated dimer from a parallel latent dimer. Conversely, the Y640F mutation is predicted to result in a more hydrophobic surface32 which might lead to an enhanced stability of activated STAT3 dimers.Tyrosine 640 is located in the inside a PYTK sequence motif conserved in STAT1, STAT2, STAT3 and to a lesser extent in STAT4. Interestingly, in STAT5α, STAT5β and STAT6 the tyrosine residue within this motif is replaced by a phenylalanine residue. The importance of STAT2 Y631  has been already described showing that mutating this residue to phenylalanine leads to resistance to dephosphorylation by the nuclear tyrosine phosphatase TcPTP and thus to sustained IFN-dependent STAT1 Y701 phosphorylation, STAT1-STAT2 Y631F heterodimerization and induction of ISGF3-responsive genes with consequent enhanced cell apoptosis70, IFN-dependent pro-apoptotic activity in primary murine pro-B cells71 and IFNα-dependent inhibition of viral replication of Influenza and Vaccinia viruses in STAT3-/- Murine Embryonic Fibroblasts (MEFs)72. On the basis of these observations one might speculate about the existence of cell-context dependent fine-tuning of IFN-induced STAT3 activity by e.g. a protein tyrosine phosphatase acting on Y640 phosphorylated STAT3 and releasing the IFN-specific transcriptional brake on STAT3. Finally, Src kinases are also known to regulate STAT3 phosphorylation and therefore STAT3 activity. We could however rule out a role for Src kinases since the selective inhibitor PP2 did not lead to an upregulation of either IFN- or LIF-induced STAT3-dependent transcriptional activity in HEK293T cells transiently expressing either STAT3 wt or STAT3 Y640F (data not shown).The activity of IFN-activated STAT3 varies in a cell context-dependent way. Indeed, depending on the cell type, the level of IFN-induced STAT3 phosphorylation is not followed by a comparable activation of STAT3-responsive genes, whilst in others STAT3 can induce ISGs transcription in response to IFNs. This is supported by a number of studies concerning IFN-driven antiviral and antiproliferative effects in IFN-resistant Daudi cellsIn conclusion, we demonstrate that mitigation of the STAT3 Y640 phosphorylation, as demonstrated by the Y640F mutation, renders STAT3 transcriptionally competent in response to IFNα2. We suggest a key role for the tyrosine kinase activity of TYK2 in the repression of STAT3 activity. The proposed mechanism may serve to fine-tune IFN-specific effects in different cell types.45. The plasmid pMET7-Etag-STAT3wt was constructed using the primer pair 5′-GCGGCGGCCGCAGCCCAATCAGCTACAG-3′ and 5′-CGCCTCGAGTCACATGGGGGAGGTAGCGCA-3′ to clone STAT3 into pMET7 vector using NotI-XhoI. By site-directed mutagenesis we generated STAT3 mutants starting from pMET7-Etag-STAT3 wt using the mutagenesis primers: Y705F 5′-GGTAGCGCTGCCCCATTCCTAAAG ACCAAGTTTATCTG-3′ and 5′-CAGATAAACTTGGTCTTTAGGAATGGGGCAGCGCTACC-3′; Y640F 5′-GATCCAGTCCGTGGAACCATTCACAAAGCAGCAACTGAACAACATGTCATT T-3′ and 5′-CAAATGACATGTTGTTCAGTTGCTGCTTTGTGAATGGTTCCACGGACTGGAT C- 3′; Y640E 5′-GATCCAGTCCGTGGAACCAGAGACAAAGCAGCAACTGAACAACATGT CATTTG-3′ and 5′-CAAATGACATGTTGTTCAGTTGCTGCTTTGTCTCTGGTTCCACGGAC TGGATC-3′; Y657F 5′-GAAATCATCATGGGCTTTAAAATCATGGATGC- 3′ and 5′-GCATCC ATGATTTTAAAGCCCATGATGATTTC-3′. The TK domains of the four different JAKs were cloned in the pMET7-FLAG vector using EcoRI-NotI after PCR with primers: 5′-GACGGAATTCATGGACCCTACGGTTTTCCAC-3′ and 5′-GACGGCGGCCGCTCAGCACA CG-3′ (TYK2); 5′-GACGGAATTCATGGACCCCACACATTTTGAAAAGC-3′and 5′-GACGGC GGCCGCTTATTTTAAAAG-3′ (JAK1); 5′-GACGGAATTCATGGATCCTACACAGTTTGAAG AG- 3′ and 5′-GCAGGCGGCCGCTCATCCAGCCATG-3′ (JAK2); 5′-GACGGAATTCATGCA AGACCCCACGATCTTCGAGGAG-3′ and 5′-GACGGCGGCCGCCTATGAAAAGGACAG-3′ (JAK3). Recombinant human IFNα2, recombinant mouse IFNαA  and recombinant human LIF  were used at 10 ng/ml, recombinant human IL-6 (Peprotech) was used at 100 ng/ml.The STAT3-responsive rat pancreatitis-associated protein 1 (rPAP1) promoter was used to generate pXP2d2-rPAP1-luciferase reporter as described elsewhere73. For immunoprecipitation experiments, 2 × 105 HEK293T cells were seeded in each well of a 6 well plate per condition. The next day cells were transfected with 1.5 µg of total plasmid DNA for each well: 0.2 µg of pMET7-Etag-STAT3, 0.5 µg or 0.1 µg pMET7-FLAG-JAKs TK and 0.7 µg or 1.2 µg empty pMET7 vector. For STAT3 dimerization experiments 1 × 106 HEK293T cells were seeded in a 10 cm dish and transfected the next day with 10 µg of total plasmid DNA: 0.5 µg of pN.1-STAT3-EYFP (WT or Y640F) and 9.5 µg of pMET7-Etag-STAT3 (WT or Y640F). For ChIP experiments, 3 × 106 HEK293T cells were seeded in a 14 cm Petri dish and transfected the next day with 25 µg of total plasmid DNA for each 14-cm dish: 6 µg of pMET7-Etag-STAT3 and 19 µg empty pMET7 vector. For RT-PCR experiments, 2 × 105 HEK293T cells were seeded in each well of a 6 well plate and transfected the next day with 1.5 µg of total plasmid DNA: 0.2 µg of pMET7-Etag-STAT3 constructs and 1.3 µg empty pMET7 vector. For the immunofluorescence analysis 1.5 × 105 Hek293T cells were transfected with 1 µg total DNA in a 6 well plate: 0.2 µg of pMET7-Etag-STAT3 constructs and 0.8 µg empty pMET7 vector. For the luciferase assay in HEK293T cells 1.5 × 105 cells were seeded in each well of a 6 well plate and transfected the next day with 1.5 µg total plasmid DNA: 0.2 µg luciferase reporter vector; 0.2 µg expression vector; 0.1 µg pSVL-hIL-6Ra-HAtag coding IL-6Rα chain; 1 µg empty pMET7 vector. For luciferase assay in murine N38 and NIH3T3 cells 4.5 × 105 cells were seeded in each well of a 6 well plate and transfected the next day using Lipofectamine 2000 Transfection Reagent (Thermo Fisher Scientific) following the manufacturer’s instructions with 2.5 µg total plasmid DNA: 0.2 µg luciferase reporter vector; 0.5 µg expression vector; 1.8 µg empty pMET7 vector. The next day cells were washed with PBS, trypsinized, transferred to a 96-well plate and left untreated or stimulated with the indicated amount of hIFNα2, mIFNαA, hLIF or hIL-6 for 24 h. Luciferase activity was measured by chemiluminescence with a TopCount  and expressed as fold induction (stimulated/nonstimulated).HEK293T cells were grown in DMEM + 10% FCS and transiently transfected overnight using a standard calcium phosphate precipitation procedure3VO4; 1 mM NaF and complete Protease Inhibitor Cocktail; Roche Applied Science, Penzberg, Germany). Cleared lysates were incubated with monoclonal mouse anti-Etag antibody (Phadia) overnight at 4 °C. Immune complexes were precipitated by adding 35 µl (50% slurry) of Protein G Dynabeads (Thermo Fisher Scientific) and rotated for 3 h at 4 °C. Beads were washed 3 times with modified RIPA buffer, resuspended in 30 µl of 5xSDS loading buffer and boiled at 98 °C for 10 min. For STAT3 dimerization experiments, 48 hours after transfection cells were serum-starved for 6 hours and stimulated for 15 minutes with 5 ng/ml IFNα or 15 ng/ml LIF, or left untreated. Cells were lysed using 1 mL of modified RIPA buffer (for STAT3 dimers: lysates were homogenized with a dounce homogenizer), lysate supernatans were collected and Etag-STAT3 was immunoprecipitated using 2 µg mouse anti-Etag (Phadia) and 30 µl (50% slurry) Protein G Dynabeads (Thermo Fisher Scientific). Immunoprecipitated proteins were eluted in 2x Laemli buffer and heated at 95 °C for 5 minutes. Lysates were loaded on a 10% SDS-polyacrylamide gel and transferred to a polyvinylidene difluoride (PVDF) membrane (Millipore). The following antibodies were used: mouse monoclonal anti-phosphotyrosine clone 4G10 (Millipore); anti mouse monoclonal anti-Etag (Phadia); mouse monoclonal anti-STAT3 ; rabbit polyclonal anti-phosphorylated Y705 STAT3 ; mouse monoclonal anti-β-actin (Sigma Aldrich); mouse monoclonal anti-FLAG (Sigma Aldrich); either peroxidase AffiniPure goat anti-mouse IgG or peroxidase AffiniPure goat anti-rabbit IgG  was used as secondary antibody. SuperSignal pico Chemiluminescent Substrate (Thermo Fisher Scientific) was used as substrate of peroxidase and targeted proteins on the blot were visualized using CL-XPosure films (Thermo Fisher Scientific).48 h after transfection, HEK293T cells from a single 6 well plate were collected, pooled in ice-cold PBS and lysed with 1 ml of modified RIPA lysis buffer  at room temperature while shaking. Cross-linking was reverted by incubation at 67 °C overnight. All samples were treated with 50 ng/µl of RNase A and 100 ng/µl of Proteinase K. DNA fragments were purified using the QIAquick PCR purification kit (Qiagen) and subjected to quantitative PCR using SYBR Green Master (Roche Applied Science) with the specific primer pair for the SOCS3 promoter 5′-AAAAGGGGAAGGGGAACC-3′ and 5′-GGAGAGCGGGCAGTTCTA-3′. The results were analyzed using the ΔΔCT method. The fold change of STAT3 promoter occupancy was calculated by normalizing the relative amount to the input and comparing with normal mouse Immunoglobulin G precipitated chromatin.48 hours after transfection cells were serum-starved for 4 hours and stimulated with IFNα2 or LIF for 1 h, followed by fixation with 1% formaldehyde at room temperature for 10 min. Fixation was quenched by addition of 2 M glycine at the final concentration of 10%. Cells were washed once, collected in with PBS + 0.5 mM EDTA and centrifuged for 5 minutes 1500 rpm 4 °C. Cell pellets were resuspended and sonicated in PBS + 0.5 mM EDTA + 0.5% Nonidet P-40 + complete Protease Inhibitor Cocktail to shear the chromatin DNA to fragments of 500–1000 bp. The supernatant was clarified by centrifugation and 1/10 of the chromatin was saved as input. The remaining chromatin solution was incubated with mouse monoclonal anti-Etag (Phadia) or normal mouse Immunoglobulin G (Santa Cruz Biotechnology) at 4 °C overnight. Antibody-protein-DNA complexes were incubated with 40 µl (50% slurry) Protein G Dynabeads (Thermo Fisher Scientific) for 3 h at 4 °C and immunoprecipitates were washed once with RIPA buffer , twice with RIPA-500 buffer , once with LiCl buffer  and one last time with TBS buffer . Immune complexes were eluted from beads using elution buffer , 5′-GCCCAATACGACCAAAT CC-3′ (reverse); SOCS3 5′-CTTCGACTGCGTGCTAA-3′ (forward), 5′-GTAGGTGGCGAGGGG AAG-3′ (reverse); 2′5′OAS 5′-GACGGATGTTAGCCTGCTG-3′ (forward), 5′-TGGGGATTTGG TTTGGTG-3′ (reverse); IFITM1 5′-CACGCAGAAAACCACACTTC-3′ (forward), 5′-TGTTCCT CCTTGTGCATCTTC-3′ (reverse); ISG54 5′-TGGTGGCAGAAGAGGAAGAT-3′ (forward), 5′-G TAGGCTGCTCTCCAAGGAA-3′ (reverse). The fold change of mRNA expression was calculated by normalizing the relative amount to the internal control GAPDH and comparing with untreated cells.Cell lysates were homogenized with the QiaShredder Column (Qiagen) and total cellular RNA was extracted using RNasy Mini kit (Qiagen) following the manufacturer’s instructions. Equal amounts of RNA (1 µg) were used for reverse transcription using the Primescript RT Reagent kit (Takara Bio) following the manufacturer’s instructions. cDNA was then diluted 1:20 and amplified in quantitative PCR which was monitored using Lightcycler 480 SYBR Green I Master  in a LightCycler 480 Real-Time PCR System thermocycler (Roche Applied Science) and the results were analyzed using the ΔΔC5 HEK293T cells were seeded on coverslips previously coated with poly-L-lysine and grown in serum free medium for 16 h. After stimulation cells were washed once with PBS + Ca/Mg, fixated 15 minutes in 4% paraformaldehyde and washed three times with PBS + Ca/Mg. Cells were permeabilized 10 minutes at −20 °C with ice-cold methanol, washed once in PBS + Ca/Mg and incubated with blocking buffer  for 1 h and then 16 h at 4 °C with goat anti-Etag (Bethyl Laboratories) (1:2500) in blocking buffer. The day after, cells were washed 3 times with washing buffer , followed by probing with donkey anti-goat secondary antibody  for 45 minutes (1:500) in blocking buffer at room temperature. Cells were then washed 3 times with washing buffer and once with PBS + Ca/Mg. 4,6-diamidino-2-phenylindole (DAPI) was used to stain the cell nuclei. A motorized inverted IX81 FluoView FV1000 laser scanning microscope (Olympus) was used to record high-resolution images.1.5 × 1074. A phosphate group was built on the tyrosine hydroxyl group of a STAT3 monomer , followed by energy minimization using the AMBER99 force field. During four minimization steps, atoms within 0.25, 0.5 and 0.75 nm of the phosphate phosphorous atom were subsequently allowed to move, followed by minimization of all residues with any atom within 0.9 nm of this atom. Via the UCSF Chimera75 matchmaker protocol, the monomer STAT3 model with Y640 phosphorylation was superposed on the STAT3 dimer found in the crystal structure to test the compatibility of Y640 phosphorylation with the dimerization mode in the crystal structure.Phosphorylation of Y640 was modeled in YASARA structurecFOS promoter  and SOCS3 promoter  were labelled by filling in 5′ protruding ends with Klenow enzyme using (α-32P) dATP  and purified using the Qiaquick PCR purification kit (Qiagen). Nuclear extracts (5 μg of protein) were incubated with approximately 10 fmol (20000 cpm) of probe in gel-shift incubation buffer ·(dI-dC)) for 10 min at room temperature (25 °C). The supershifting mouse anti-STAT3 antibody  was incubated with the nuclear extracts for 10 min at room temperature before addition of the radiolabeled probe. The protein–DNA complexes were separated on a 4.5% (w/v) polyacrylamide gel containing 7.5% glycerol in 0.5-fold TBE (Tris/borate/EDTA) at 20 V/cm for 90 min. Gels were fixed in water/methanol/ethanoic (acetic) acid  for 30 min, dried and autoradiographed.HEK293T cells transiently transfected with the desired constructs were starved for 4 h in serum-free medium and were subsequently stimulated with 5 ng/ml IFNα2 or 15 ng/ml LIF for 15 min or were left untreated. Protein concentrations of the nuclear extracts were measured with the Bio-Rad protein assay. Double-stranded oligonucleotides based on the The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.Supplementary Data