Schizosaccharomyces pombe, is an important model species with a low intron density. Previous studies showed extensive intron losses during its evolution. To test the models of intron loss and gain in fission yeasts, we conducted a comparative genomic analysis in four Schizosaccharomyces species. Both intronization and de-intronization were observed, although both were at a low frequency. A de-intronization event was caused by a degenerative mutation in the branch site. Four cases of imprecise intron losses were identified, indicating that genomic deletion is not a negligible mechanism of intron loss. Most intron losses were precise deletions of introns, and were significantly biased to the 3′ sides of genes. Adjacent introns tended to be lost simultaneously. These observations indicated that the main force shaping the exon-intron structures of fission yeasts was precise intron losses mediated by reverse transcriptase. We found two cases of intron gains caused by tandem genomic duplication, but failed to identify the mechanisms for the majority of the intron gain events observed. In addition, we found that intron-lost and intron-gained genes had certain similar features, such as similar Gene Ontology categories and expression levels.The fission yeast,  Spliceosomal intron densities vary greatly among different organisms Saccharomyces cerevisiaeDictyostelium discoideum, Schizosaccharomyces pombe, Mycosphaerella, Cryptococcus, Caenorhabditis elegans, Anopheles, and mammals Fusarium graminearum, Magnaporthe grisea or rice Neurospora crassa and Aspergillus, the biased position of intron loss has been observed in a comparative analysis among distantly related species Arabidopsis and Drosophila, conflicting results have been reported Cryptococcus, Fusarium, Aspergillus, Drosophila and mammals Caenorhabditis, Plasmodium or ArabidopsisDrosophila, and Muridae ArabidopsisDrosophilaThree models of intron loss have been proposed: the reverse transcription (RT) model, the genomic deletion model, and the model of non-homologous end joining (NHEJ) repair of double strand breaks Daphnia and AspergillusOikopleura and MycosphaerellaDrosophila and ArabidopsisCladosporium and Dothistroma, introner-like elements (ILEs) were found to contribute to new introns Chironomus, Aspergillus and MycosphaerellaCompared with intron loss, the possible mechanisms underlying intron gain are much more diverse. At least six mechanisms have been proposed for intron gain: intron transposition, transposon insertion, group II intron insertion, tandem genomic duplication, intron transfer and NHEJ-mediated intron gain events Cryptococcus, Fusarium, Caenorhabditis, mammals and PopulusA broad definition of intron loss and gain could include de-intronization and intronization, which are conversions of intron sequences to exon sequences, and vice versa, by mutations Schizosaccharomyces pombe, has a low intron density S. pombe with distantly related organisms, such as vertebrates and plants. Most of the evolutionary traces of intron loss and gain could not be retained over such a long evolutionary history. For this reason, we compared four closely related species; Schizosaccharomyces cryophilus, Schizosaccharomyces octosporus, S. pombe and Schizosaccharomyces japonicus with six outgroup fungus species  from the Broad Institute . Data for Saitoella complicata, Aspergillus niger, Nectria haematococca, Sporobolomyces roseus, Cryptococcus neoformans and Phycomyces blakesleeanus were obtained from JGI . Saitoella complicata was selected because it is closely related to the Schizosaccharomyces genus and the other five outgroup species were selected because of their relative abundance of introns. The phylogenetic relationship between the Schizosaccharomyces species and the outgroup species is shown in We downloaded the genome sequences and gene annotations of four fission yeast species , 1 (has an intron) or ? (no orthologous regions). This data list was imported into the Dollop (Dollo and Polymorphism Parsimony) program in the PHYLIP 3.6 Schizosaccharomyces group. Finally, considering the limitation of Dollop . The transcripts were mapped onto the corresponding genomic sequences using BLAT 34 http://www.broadinstitute.org/annotation/genome/schizosaccharomyces_group/FeatureSearch.html) to get additional transcripts that are only available on the webpages.In order to exclude the possibility that the unique intron positions, including intron losses/gains and intronizations/de-intronizations, were gene structure annotation artifacts instead of real intron changes, it is necessary to use transcriptome data to identify related gene structures. The RNAseq/Inchworm/PASA assembly sequences of the four fission yeast species were downloaded from the Broad Institute  created the two splicing sites  could produce alternative products Multiple signals are required for efficient splicing The identification of intron loss or gain events were initially based on the gene structure annotations of the four fission yeast species. However, some of these annotations might be erroneous, which could be seen from the fact that most putative intronization or de-intronization events were annotation errors. For intron losses and gains, gene annotation errors could also lead to false-positive results. If an exonic segment was mis-annotated as an intron in orthologous genes, a simple deletion of it would lead to a false-positive case of intron loss. Similarly, a simple insertion of exonic sequence mis-annotated as an intron would lead to a false-positive case of intron gain. Therefore, the transcriptome data were also required to support the target intron loss or gain events.In our datasets, we found that most intron loss and gain positions were supported by transcriptome data –S3. In oSchizosaccharomyces group was negatively correlated with the intron loss rates ; therefore, we suggest that the genomic deletions that have actually occurred in evolution should be more frequent than the imprecise intron deletions we observed.In addition, the frequency of genomic deletion might also been underestimated for methodological reasons. In previous studies S. cryophilus and S. octosporus, 17 in S. pombe, seven in the ancestor of S. cryophilus, S. octosporus and S. pombe and eight in S. japonicus. Referring to the method of Roy and Gilbert −7) to deny the null hypothesis. Furthermore, we observed a preferential loss of introns at the 3′ side of genes. As shown in Similarly to previous studies in other taxa P>0.10 in all species and nodes). Thus, the NHEJ-mediated model was not supported by the intron losses in fission yeast.Meanwhile, we also tested the NHEJ-mediated model of intron loss by surveying the micro-homology between 5′ and 3′ splice sites of lost introns SPOG_01682 gained its intron at position 576-0  and its ortholog SOCG_00815 gained its intron at another position 612-0. Both of the introns comprised tandem repeats . Therefore, the NHEJ model of intron gain was not supported in fission yeasts.We also attempted to test the NHEJ model of intron gain by surveying the frequency of direct repeats near intron-exon boundaries S. pombe GO annotation from the Gene Ontology website (http://www.geneontology.org). Compared with the whole gene sets, IL genes were more likely to participate in metabolism, molecular transportation and enzyme activity regulation  genes and intron-gained (IG) genes are clustered in certain special features. BiNGO 2.44 gulation . Interesgulation . We alsogulation . These rAs discussed above, intron losses were mainly mediated by reverse transcriptase. In the current models of intron gain, only the intron transposition model shares a similar mechanism (i.e. a requirement for reverse transcription) with intron loss. However, the intron transposition model requires similarities between newly gained introns and other extant introns, which was not observed in our study. As the selective constraint is much lower for introns than for coding regions, accumulated mutations in gained intron sequences might have resulted in them being significantly divergent from the source sequences. It is also possible that some of the identified intron gains might be intron losses in other taxa, considering the high intron loss rates in fission yeasts. False-positive results of intron gains would cause the IG genes share some false-positive similarities with IL genes.In fission yeasts, we found a higher frequency of intron loss than intron gain. Although a moderate number of putative intronization and de-intronization events were observed, most cases were filtered out using transcriptome data. Careful examination of the confident cases of intronization and de-intronization revealed them to be caused by mutations in splice sites and branch sites. Although at a low frequency, imprecise intron deletions were observed, supporting the genomic deletion model of intron loss. The characteristics of most intron losses are consistent with the RT model. Similar to previous studies Table S1Conserved introns across the four fission yeast species.(XLS)Click here for additional data file.Table S2Lost introns across the four fission yeast species.(XLS)Click here for additional data file.Table S3Gained introns across the four fission yeast species.(XLS)Click here for additional data file.Table S4Intron-lost genes and intron-gained genes in fission yeasts have higher expression levels.(DOC)Click here for additional data file.Figure S1Number of extant introns and intron loss rate are negatively correlated. The intron loss rate and the number of extant introns are negatively correlated. S. cryophilus has the largest number of extant introns and has experienced the lowest intron loss rate, while S. pombe has the fewest introns and had the highest intron loss rate. Species name abbreviations: S. cryophilus (Scry), S. octosporus (Soct), S. pombe (Spom), and S. japonicus (Sjap).(TIF)Click here for additional data file.Figure S2Large indels neighboring unique intron positions in fission yeasts. The alignments of DNA sequences around unique intron regions are shown. Exon sequences are shown in upper case while intron sequences are shown in lower case. Exonic sequence indels accompanying intron loss are marked in red. Species name abbreviations: S. cryophilus (Scry), S. octosporus (Soct), S. pombe (Spom), and S. japonicus (Sjap).(TIF)Click here for additional data file.Text S1The limitation of the Dollop program.(DOC)Click here for additional data file.


In recent years a number of studies have shown that whole grain products in particular have strong antioxidant activity. Primary cultures of rat hepatocytes were used to investigate whether and how a fermented powder of wheat (Lisosan G) is able to modulate antioxidant and detoxifying enzymes, and whether or not it can activate Nrf2 transcription factor or inhibit NF-kB activation. All of the antioxidant and detoxifying enzymes studied were significantly up-regulated by 0.7 mg/ml Lisosan G treatment. In particular, NAD(P)H:quinone oxidoreductase and heme oxygenase-1 were induced, although to different degrees, at the transcriptional, protein and/or activity levels by the treatment. As for the Nrf2 transcription factor, a partial translocation of its protein from the cytosol to the nucleus after 1 h of Lisosan G treatment was revealed by immunoblotting. Lisosan G was also observed to decrease HTaken together, these results show that this powder of wheat is an effective inducer of ARE/Nrf2-regulated antioxidant and detoxifying genes and has the potential to inhibit the translocation of NF-kB into the nucleus. In vivo, this vitamin acts as scavenger of oxygen radicals and also as competitive inhibitor of nitrosamine synthesis from nitrite and amines in vivoHumans are constantly exposed to factors causing oxidative stress including pollutants, radiation and oxidized food Several studies have shown that some phytochemicals can modulate antioxidant and phase II enzymes through the activation of nuclear factor E2-related protein (Nrf2) In recent years, many authors have suggested the existence of cross-talk between Nrf2/ARE and the nuclear factor-kappa B (NF-kB) signaling pathways in response to inflammation Triticum Sativum (wheat) and it is registered with the Italian Ministry of Health as a nutritional supplement. In the production process, the wholegrain is first ground to a rough powder. From this intermediary product, the bran and germ are separated and collected for further treatment which consists in the following: water is added to moisten the mix, then selected microbic starting cultures are inoculated to initiate fermentation. The starting cultures typically consist of a mix of lacto-bacillus and natural yeast strains. Once the product is sufficiently fermented, it is dried. The resulting dry powder is now Lisosan G, which is widely used in food production thanks to its rich nutritional content.Lisosan G is a powder obtained from In vivo, Lisosan G protects against cisplatin induced toxicity It contains vitamins, minerals and polyunsatured fatty acids as well as having significant antioxidant activity 2O2) were all supplied by Sigma-Aldrich . Rabbit polyclonal anti-Nrf2 (sc-13032), anti-heme oxygenase-1 (sc-10789), NFkB (sc-7178), β-actin (sc-130657), PARP-1 (sc-25780) and goat anti-rabbit (1∶2000 or 1∶5000) were from Santa Cruz Biotechnology Inc. . Collagen (type I) was prepared by the method of Beken et al. (1998).Lisosan G is registered as nutritional supplement by the Italian Minister of Health and was supplied by Agrisan Company, Larciano (PT), Italy. Collagenase; dexamethasone; insulin; glucagon; penicillin/streptomycin; ampicillin/kanamycin; fetal bovine serum; 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES); Tween 20; phenylmethylsulfonyl fluoride (PMSF); leupeptin; apoprotein; pepstatin; tunicamycin; Williams E medium; bovine serum albumin (BSA); epidermal growth factor (EGF); β-nicotinamide adenine dinucleotide reduced (NADPH); ethylenediaminetetraacetic acid (EDTA); ethylene glycol tetraacetic acid (EGTA); glutathione (GSH); Glutathione disulfide (GSSG); and hydrogen peroxide  and then subjected to midline laparotomy in order to exteriorize the liver and isolate the portal vein. A needle was inserted into the portal vein and then the liver was perfused as described previously 6 cells/8 ml on 100 mm cell culture dish pre-coated with 3 ml of collagen (type I) solution (1 mg/ml). The cultures were maintained at 37°C in 5% CO2 in a humidifier incubator. After 5 h, the medium was replaced with serum-free Williams E medium supplemented with 2% BSA, 7.5 µg/ml hydrocortisone 21-hemisuccinate sodium salt and EGF 20 ng/ml. Cultures were maintained in this medium at 37°C and 5% CO2 for 24 h. After this period, the medium was removed and a second layer of type I collagen was added to create a collagen-gel sandwich culture 2O2 200 µM and in the last group, the H2O2 200 µM was added after 1 h pre-treatment with Lisosan G 0.7 mg/ml (Lis+H2O2). We have used different concentrations of H2O2 (10–500 µM) and we chose 200 µM (good toxicity); we also used for Lisosan G different concentrations from 0.1 to 2.8 mg/ml and 0.7 mg/ml was the best concentration in terms of maximum protective effect and without toxicity. The cytotoxic effects compared with the vehicle-only controls, was also measured by lactate dehydrogenase assay (data not shown). We have also performed experiments in function of time of treatments of H2O2 and Lisosan G and we chose the time of treatment on the basis of the best results.After filtration and centrifugation, the cell viability was determined by trypan blue exclusion. The cells were dispersed in Williams E medium containing 39 ng/ml dexamethasone, 0.5 U/ml insulin, 0.007 µg/ml glucagon, 5 µg/ml penicillin and streptomycin, 5 µg/ml ampicillin and kanamycin and 10% fetal bovine serum. The cells were plated at a density of 4.5×102O2 decomposition at 240 nm, as described by Cao and Li After 24 h the end of treatment, the medium was removed and a collagenase solution was added. After 30 minutes, recovered cells were centrifuged (400× g) for 3 minutes at 4°C. The cell pellet was sonicated and used for the microsomal preparation Total cellular RNA was extracted from primary rat hepatocytes 4 h after ITC treatment, using the RNeasy Mini Kit , following the supplied protocol. RNA was quantified using NanoDrop ; its purity and integrity were evaluated by checking the absorbance ratio at 260–280 nm and assessing the sharpness of 18S and 28S ribosomal RNA bands on agarose gel stained with ethidium bromide. Genomic DNA elimination and reverse transcription of total RNA were performed using QuantiTect Reverse Transcription Kit (Qiagen).Two microliters of cDNA were added to a PCR Master Mix  for the amplification reaction (various cycles) performed using for each transcript 400 nM of forward–reverse primers for heme oxygenase-1 (GenBank accession no. NM_012580.2), NQO1 (GenBank accession no. NM_017000.3), β-actin, as housekeeping gene, (GenBank accession no. NM_031144.2) and the annealing temperature indicated in Nuclear and cytosolic extracts were prepared by previously established methods Nuclear and microsomal proteins from primary rat hepatocytes were separated according to Laemmli Results are reported as means ± SD. Statistical significance was determined by Student's t-test for comparison between control and treated groups or the one-way ANOVA and the Dunnet tests. The statistical program used was Graphpad Prism 4. P value<0.05 was considered to be significant.2O2 caused a decrease in reduced glutathione levels, whereas pretreatment with 0.7 mg/ml Lisosan G followed by H2O2, raised the amount of intracellular GSH to levels above control cells values. Lisosan G alone was able to increase reduced glutathione levels in hepatocytes treated for 24 h.To verify the ability of Lisosan G to protect hepatocytes from damage caused by oxidative stress, we treated a first group of cells with hydrogen peroxide, while a second group was pretreated with Lisosan G prior to exposure to hydrogen peroxide. As shown in 2O2 which had been pretreated for 1 h with Lisosan G. 2O2-induced lipid peroxidation. Exposure of cells to H2O2 for 24 h significantly increased lipid peroxidation while preincubation of cells for 1 h prevented this from occurring. Also, Lisosan G on its own reduced lipid peroxidation levels compared to control (CTR) cells.As a biomarker for lipid peroxidation, the concentration of malondialdehyde (MDA) in microsomes was measured in cells treated with H2O2 treatment reduced NQO1 activity, but NQO1 activity remained high when a 1 hour pre-treatment with . H2O2 also caused a decrease in HO-1 activity compared to control cells, but pretreatment with Lisosan G before H2O2 treatment elevated the activity above the level of CTR. HO-1 activity was significantly induced by Lisosan G treatment   and catalase. Hent with  Lisosan l value) . As for l values . This phl values , followi2O2 for 4 h. As shown in 2O2 didn't cause any significant change in NQO1 expression, but both the treatment with Lisosan G only and Lis+H2O2 increased its expression compared to the control levels.NQO1 and HO-1 were chosen to analysis of the response to Lisosan G at the transcriptional level. The expression of their transcripts in primary rat hepatocytes were analyzed by semi-quantitative RT-PCR, using the sets of primers listed in 2O2 caused a rise in HO-1 expression but in this case, H2O2 alone also induced the gene.A similar trend was found for HO-1 expression, although to different extents . For thi2O2 no effect was noticed after 24 h treatment. On the contrary, the pre-treatment with Lisosan G before H2O2, raised the HO-1 protein level 1.6 fold above the CTR value, in agreement with results from the activity assays and transcription analysis.The effect of Lisosan G on heme oxygenase-1 was also assessed at the protein level by western blot . In micr2O2 nuclei. On the contrary, Nrf2 was clearly visible in the nuclear extracts of hepatocytes treated with Lisosan G and with Lisosan G+H2O2. The results have been normalized to PARP-1 levels.Since a major component of cellular defense against oxidative or electrophilic stress is the activation of the Nrf2/ARE signaling pathway 2O2 treated hepatocytes. The signal in control cells was similar to that of cells treated with Lisosan G, both very weak. The signal from nuclear extracts of hepatocytes treated with Lisosan G+H2O2 is fainter than that obtained in hydrogen peroxide treated cells. The results have been normalized to β-actin levels.As cross talk between Nrf2 and NF-kB is an area of intense interest, we investigated whether Lisosan G treatment would prevent the NF-kB translocation to the nucleus, caused by hydrogen peroxide. We analyzed the nuclear fraction by western blot . NF-kB w2O2 induced oxidative stress and whether it was able to modulate phase 2 enzymes by activating the Nrf2 protein and causing its translocation into the nucleus. We also analyzed its ability to prevent NF-kB nuclear translocation. We used sandwich cultures of primary rat hepatocytes, a unique in vitro system that preserves hepatic cytomorphology, as well as its drug metabolism, deposition and toxicity, to allow close resemblance with in vivo parameters In the present study, we investigated whether a fermentated wheat powder, Lisosan G, was able to protect against H2O2, and increased NQO1, HO-1, GST and catalase activity, suggesting that Lisosan G has antioxidant properties and confirming earlier in vivo studies which had demonstrated the ability of Lisosan G to elevate GST, NQO1, catalase and GSH peroxidase activity 2O2 induced oxidative stress. Several classes of phytochemicals such as phenols, flavonoids, isothiocyanates, organosulfurs, and indoles can induce detoxifying enzymes such as NQO1 and HO-1 2O2. GSH is a multifunctional intracellular non-enzymatic antioxidant and it is considered to be the major thiol-disulphide redox buffer of the cell Lisosan G defended cells against damage induced by HNQO1, HO-1 and GST gene expression is regulated by several key transcriptional factors located in the upstream region It is important to note that the Nrf2 and NF-kB signaling pathways interface at several points to control the transcription or function of downstream target proteins. We therefore tried to understand if Lisosan G was able to regulate NF-kB. It is known that released NF-kB translocates into the nucleus where it regulates the transcription of genes for chemokines, cytokines, immunoreceptors, cell-adhesion molecules, growth factors, tumor necrosis factor α (TNFα), inducible NOS (iNOS), interleukin-1 (IL-1), interleukin-6 (IL-6) and cyclooxygenase (COX-2) The ability of Lisosan G to induce phase II enzymes via Nrf2 and inhibit NF-kB activation, may be linked to its composition Lisosan G also contains minerals such as iron, zinc, copper and it is rich in vitamins . Vitamin E is an important natural antioxidant, and its most common and biologically active form is α-tocopherol. Unpublished results have shown that α-lipoic acid (ALA) is among the various components in Lisosan G, and that it is present in a concentration of 66 mg/kg.in vitroALA is a thiol antioxidant found in vegetables, including broccoli, spinach and tomatoes 2O2-induced translocation of NF-kB to the nucleus. It seems likely that the beneficial effects of Lisosan G derive from its high phytochemical and vitamin content.This study has established for the first time in primary rat hepatocytes that Lisosan G can modulate phase 2 enzymes through the activation of Nrf2 pathway. We have demonstrated the Lisosan G decreases the H


Furthermore, we found increased PD-L1 expression on DCs and T cells from influenza-infected patients from the first and second 2009 pandemic waves in Mexico City. PD-L1 expression on CD8+ T cells correlated inversely with T cell proportions in patients infected with A(H1N1)pdm09. Therefore, PD-L1 expression on DCs and T cells could be associated with an impaired T cell response during acute infection with A(H1N1)pdm09 virus.PD-L1 expression plays a critical role in the impairment of T cell responses during chronic infections; however, the expression of PD-L1 on T cells during acute viral infections, particularly during the pandemic influenza virus (A(H1N1)pdm09), and its effects on the T cell response have not been widely explored. We found that A(H1N1)pdm09 virus induced PD-L1 expression on human dendritic cells (DCs) and T cells, as well as PD-1 expression on T cells. PD-L1 expression impaired the T cell response against A(H1N1)pdm09 by promoting CD8 Programmed death-ligand 1  is a coinhibitory molecule that has been associated with impairment of the T cell response. PD-L1 is one of the ligands that interact with the inhibitory PD-1 receptor, which is expressed on activated T cells , and cytInfluenza virus infection may trigger an exacerbated immune response, which has been correlated with illness severity and sometimes death –14. Lymp+ T cell-mediated immune response against influenza plays a role in limiting the severity of infection in the absence of previous antibodies [Cellular immunity, may contribute to virus clearance, reduction of symptoms and prevention of secondary infections , 23. Thetibodies . Howevertibodies . TherefoThirteen patients from two hospitals from the Mexican Social Security Institute (IMSS) with RT-PCR-confirmed pandemic influenza infection (pH1N1+), 12 PCR negative patients with influenza-like illness (ILI) (pH1N1−), and 10 healthy controls (HC) were included in this report. Patients were recruited during the first and second pandemic waves in Mexico City. Informed consent was obtained from participants. Study approval was obtained from the IMSS through the National Commission of Scientific Research, which comprises the Scientific, Ethics, and Biosafety Committees, in accordance with Good Clinical Practice. The project's ethics authorization number is: CNIC 2010-785-002.Blood samples from patients and controls were collected in EDTA tubes. Peripheral blood mononuclear cells (PBMCs) were separated by gradient centrifugation using Lymphoprep  and cryopreserved until use. PBMCs from buffy coats were obtained from healthy volunteer donors according to institutional guidelines.6) from buffy coats were placed in 24-well plates  with RPMI-1640 . They were stimulated with 10 pgmL−1 staphylococcal enterotoxin B , 10 µgmL−1 of the TLR7 synthetic agonist CL264 , 80 HAUmL−1 (hemagglutination units) of live and UV-inactivated influenza A/Mexico/4482/2009(H1N1) virus and A/Panama/2007/1999(H3N2) virus provided by the Instituto Nacional de Referencia Epidemiológica (INDRE), or 10 μgmL−1of recombinant A(H1N1)pdm09 virus hemagglutinin (HA), kindly provided by Dr. Clara Espitia from the Instituto de Investigaciones Biomédicas, UNAM. The PBMCs were incubated for 18 h, 3 or 7 days at 37°C/5% CO2 prior to flow cytometry analysis of PD-1 and PD-L1 expression on DCs and T cells, respectively. For de novo protein synthesis analysis, PBMCs were stimulated with A(H1N1)pdm09 for 2 h, then cycloheximide  was added to the culture for another 16 h.PBMCs (1 × 107) from buffy coats were incubated in supplemented RPMI-1640 at 37°C/5% CO2 for 1.5 h, in Petri dishes . Nonadherent cells were removed, washed, and quantified. T cells were then isolated by negative selection using a cocktail of PE-conjugated anti-CD19, anti-CD14 , anti-CD56, and anti-HLA-DR antibodies , with anti-PE magnetic microbeads in a MidiMACS system with LD columns . Dendritic cells were isolated the same way, but instead of anti-HLA-DR, PE-conjugated anti-CD3 (eBioscience) was used.PBMCs (2 × 105 cells/well) were placed into 48-well plates (Corning) with supplemented RPMI-1640 and stimulated with 10 pgmL−1 SEB and 80 HAU/mL A(H1N1)pdm09 virus and incubated for 48 h at 37°C/5% CO2. Enriched DCs (1.5 × 106) were placed in 24-well plates (Corning) with supplemented RPMI-1640 and stimulated with CL264 or A(H1N1)pdm09 virus. The cells were incubated for 18 h at 37°C/5% CO2, collected, and labeled for flow cytometric analysis.The enriched T cells (5 × 106 cells/well) were left untreated or stimulated with influenza virus and incubated for 18 h. Next, the cells were labeled with CFSE , and 5 × 105 cells/well were seeded into plates. These cells were left untreated or treated with 25 μgmL−1 anti-PD-L1 antibody 29E.2A3  or an isotype control MPC-11 (BioLegend) on days 0, 3, and 5, or with SEB (10 pgmL−1) on day 0. The cells were incubated for 7 days at 37°C/5% CO2, collected, and proliferation was measured by flow cytometry. The proportion of apoptotic cells was detected by flow cytometry with Annexin V/Pacific Blue and 7-AAD staining (both from BioLegend).Buffy coat PBMCs (5 × 10Supernatants from the T cell proliferation culture were cryopreserved until use. Cytokine levels were measured using the human Th1/Th2/Th17 cytometric bead array kit (CBA) according to the manufacturer's instructions (BD Biosciences).+ T cells (Tm) were isolated with the human memory CD4+ T cell isolation kit (Miltenyi Biotec). For co-culture assays, a 1 : 3 ratio of cDCs : T cells (1.5 × 104 cDCs and 4.5 × 104 T cells) were placed in 96-well plates with A(H1N1)pdm09 virus and incubated for 7 days at 37°C/5% CO2, with or without PD-L1 blocking; the supernatant was collected to conduct CBA's analysis. Representative plots of cDCs and CD4+ T cell purity are shown in Figure S1 .Sorting of cDCs from enriched DCs was performed in a FACSAria cell sorter (BD Biosciences). After the preenrichment previously described, the negative cell fraction was labeled with anti-CD123-/PE-Cy5 and anti-HLA-DR/APC-Cy7 (BioLegend), to identify the cDC population; this population was isolated with a purity of about 90%. Memory CD46 cells were evaluated for PD-L1 expression on both DCs and T cells. When the number of cells was inferior, only DCs or T cells were analyzed. Hence, PD-L1 expression was analyzed on T cells of 9/13 pH1N1+ patients and 6/12 pH1N1− patients. In the case of DCs, PD-L1 expression was evaluated in 11/13 pH1N1+ patients and 10/12 in the pH1N1− group. The PBMCs from patients were labeled with the fluorochrome-conjugated antibodies, PD-L1/PE-Cy7, CD8/APC-Cy7, HLA-DR/APC-H7, CD4/PE-Cy7, CD123/PE-Cy5, and the lineage cocktail  (BD Biosciences) and fixed with paraformaldehyde (4%). The T cells and DCs from buffy coats used for in vitro PD-L1 expression, proliferation, and cell death assays were labeled with all of the former antibodies, in addition to PD-1/FITC, CD4/APC-Cy7, CD2/PE, (BD Biosciences), and CD8/APC (Invitrogen). The cells were fixed with FACS Lysing Solution 1x (BD Biosciences) and cell viability was determined with Hoechst 33258 staining (Invitrogen). All samples were analyzed in either a FACSAria II or a FACSCanto II (BD Biosciences) using FlowJo (version 8.7) software . A minimum of 1 × 104 CD2+ events were collected for T cell samples and 5 × 104 Lin-events for DCs samples.The cryopreserved PBMCs from patients and controls were thawed and counted. Only the samples with total PBMCs above 1 × 10t-test with a two-tailed P value was performed. In the case of patients, a nonparametric Student's t-test was performed (Mann-Whitney test). Correlations were established with Spearman's test. Statistical significance was established at P < 0.05.Statistics were calculated with Prism, version 5.0, from GraphPad Software . To test for significant differences in PD-L1 expression between treatments, MFI values were normalized and a parametric Student's P < 0.01) and plasmacytoid dendritic cells (pDCs) (P < 0.001) (+ (P < 0.001) and CD8+ T cells (P < 0.001) similar to SEB (+ and CD8+ T cells was detected after 18 h and decreased over time (P < 0.001) (+ T cells and after 7 days in CD8+ T cells (P < 0.05) (+ cell-depleted T cells with A(H1N1)pdm09. We found that PD-L1 expression on DCs was induced after interaction with the virus and was dependent on de novo protein synthesis pdm09 later in time.To test whether A(H1N1)pdm09 could induce PD-L1 and PD-1 expression on DCs and T cells, we stimulated human PBMCs with A(H1N1)pdm09. After 18 h of contact with the virus, we detected PD-L1 expression on conventional (cDCs) (< 0.001) . This war to SEB . We did r to SEB , althoug< 0.001) . In the  < 0.05) . To eluc Figures ; however Figures . When we Figures . These r+ 7-AAD−) CD8+ T cells, 7 days after exposure to the H1N1 virus . CD4+ T cells did not show any significant differences in apoptosis after PD-L1 blocking  of the A(H1N1)pdm09 virus and measured cytokine production. We observed lower levels of IFN-γ, IL-10, and TNF when the whole A(H1N1)pdm09 virus was added, compared to HA stimulation. Moreover, when PD-L1 signaling was blocked, the cytokine levels induced by the virus were higher than those induced by the hemagglutinin. HA cytokine production was not affected by PD-L1 blockade, suggesting that this effect is A(H1N1)pdm09 virus-dependent . To evaluate if IFN-γ, IL-10, and TNF were mainly expressed by CD4+ T cells, we co-cultured isolated memory CD4+ T cells with sorted cDCs with or without PD-L1 blocking, and found that cytokine production by CD4+ T cells was dependent on the presence of cDCs and increased when PD-L1 was blocked . Neither in bulk PBMCs nor in co-cultures of purified cells did we observe an effect of PD-L1 blocking on IL-4, IL-17A, or IL-6 production (data not shown). In addition, we found that after 7 days of culture, enriched cDCs still expressed PD-L1 after stimulation with A(H1N1)pdm09 virus, in contrast, high expression of PD-L1 was observed in memory CD4+ T cells even in the absence of virus stimulation . Together, these results indicate that blocking PD-L1 on PBMCs had no effect on T cell proliferation but significantly decreased CD8+ T cell apoptosis and increased IFN-γ, IL-10, and TNF production by CD4+ T cells.We analyzed if PD-L1 expression induced by A(H1N1)pdm09 could impair the T cell response against the virus. We blocked PD-L1 signaling during virus-induced T cell activation and established that blocking PD-L1 did not compromise T cell proliferation induced by the A(H1N1)pdm09 virus Figures . Howeverblocking . We also3) and in half of the pH1N1− patients (1913.0 ± 1243.49 cells/mm3). We evaluated the frequencies and phenotype of DCs and T cells by flow cytometry; the gating strategy and representative plots are shown in Figure S4.We evaluated PD-L1 expression in A(H1N1)pdm09 infection. We analyzed PD-L1 expression on T cells and DCs from cryopreserved PBMCs collected from patients during the 2009 influenza pandemic. Our study included 25 patients with influenza-like illness and 10 HCs, as shown in P < 0.05). No differences in the pDCs proportions between both groups were detected . CD4+ T cells proportion tended to decrease in both groups of patients compared to that of HCs . Relative PD-L1 expression was increased on CD4+ T cells in both groups of patients compared to that in HCs , while in CD8+ T cells, it was only increased in pH1N1+ patients (P < 0.05).We found that pH1N1+ patients had a lower proportion of cDCs compared to the HCs ; we did not find a significant correlation between PD-L1 expression and cell proportion in pH1N1− subjects or in DCs subsets (data not shown). As a whole, these results suggest that PD-L1 expression on T cells could be one of the factors mediating the decrease in the T cell proportion in pHN1+ patients.Finally, to establish if PD-L1 expression in PBMCs from patients could be associated with the T cell proportion during infection, we performed a series of correlations of DCs and T cell proportions and determined PD-L1 expression in pH1N1+ and pH1N1− subjects. We detected an inverse correlation between PD-L1 expression on CD8in vitro. We also suggest that PD-L1 expression could have implications during the acute natural infection.PD-L1 expression plays a critical role in chronic infections by impairing T cell function . We repode novo protein synthesis. Additionally, we show that H3N2 seasonal virus failed to induce PD-L1 expression on either DCs or T cells; considering UV-inactivated pH1N1 induced PD-L1, it is possible that the expression of PD-L1 observed is independent of the infection capacity of the viruses. According to these results, we conclude that in vitro, there is an important difference between the pandemic and the seasonal influenza viruses in terms of their ability to induce PD-L1 expression. Furthermore, we found that PD-1 up regulation was detected at late time points in CD4+ T and CD8+ T cells cultures, after contact with the A(H1N1)pdm09 virus. This result is concordant with those observed in a recent mouse infection model, in which cognate viral antigen was necessary and sufficient to induce PD-1 expression on T cells, and that PD-1 was expressed by lymphocytes in the lower airways during acute influenza infection in humans [A(H1N1)pdm09 was able to induce PD-L1 expression on DCs in a similar manner to a TLR7 ligand. It has been documented that TLR7 and retinoid-induced gene receptor 1 (RIG-1) mediate the recognition of influenza virus in DCs . Therefon humans .in vitro and in vivo models [+ T cells, whereas cytokine production was increased; however, no impact was observed on T cell proliferation. One explanation for these results could be the late up regulation of PD-1 expression on T cells. Therefore, T cell proliferation may not be affected by the PD-L1 blockade because PD-1 expression was not apparent until day 3; however, the effects that we observed on CD4+ T cell differentiation (cytokine production) when PD-L1 signaling was blocked, could be attributed to their expression of PD-1 until day 3. In addition, PD-L1 expression is maintained over time (7 days) in A(H1N1)pdm09 stimulated cDCs and is highly expressed on memory CD4+ T cells, indicating that these cells could be a source of PD-L1 during the late phase of T cell differentiation. Moreover, PD-1 is expressed on CD8+ T cells 7 days after A(H1N1)pdm09 stimulation, which correlates with decreased T cell death. There are previous reports suggesting that after direct virus exposure, human CD8+ T cells are more susceptible to apoptosis than CD4+ T cells [+ but not CD4+ T cell death.PD-L1 expression has been associated with T cell exhaustion and dysfunction during chronic viral infections and in some acute infections in both o models , 27. We  T cells . In agreγ, and TNF that may be associated with impairment of T cell differentiation induced by the virus, because when we stimulated cells with HA and blocked the PD-1/PD-L1 interaction, we did not observe any effects on cytokine production. The A(H1N1)pdm09 virus has also been reported to induce a decrease in cytokine levels in human DCs when compared with seasonal viruses in vitro [+ T cells. It has been established that T cells from patients infected with A(H1N1)pdm09 cannot differentiate into effector cells, do not respond to mitogens, and highly express CD95 (Fas), suggesting an apoptosis-related mechanism for the lymphopenia reported in A(H1N1)pdm09 infection [Interestingly, we also observed that blocking PD-L1 caused an increase in the production of IL-10, IFN-in vitro . Our stunfection . Furthernfection .+ T cells in influenza-infected patients; thus, CD8+ T cells may also have been redistributed to the lungs. However, we found an increase in PD-L1 expression in DCs and T cells of pH1N1+ patients; it is possible that PD-1/PD-L1 signaling enhanced CD8+ T cell apoptosis as reflected in the decreased T cell proportion and as we showed in the in vitro assays results. We observed low frequency of cDCs in the blood of patients infected with A(H1N1)pdm09 during the first and second pandemic waves in Mexico City. It has been reported that in influenza-infected patients, DCs are recruited in the lung, suggesting that the low proportions that we observed may be caused by the redistribution of the DC population from the blood to the lung . Moreovein vitro results showed that unlike the A(H1N1)pdm09 virus, the H3N2 virus did not induce PD-L1 expression either in DCs or T cells; however, in addition to the seasonal H3N2 virus, seasonal H1N1 viruses were also circulating at that time in Mexico, so we cannot rule out that seasonal H1N1 virus could have also induced PD-L1 expression. Considering that our ex vivo results showed that PD-L1 up regulation may not be strain specific, and that in the natural infection additional immune mediators may contribute to PD-L1 up regulation, we do not discard the possibility that different types of influenza A virus could induce PD-L1 expression on DCs and T cells during acute infection. Our PD-1 and PD-L1 have been recently reported to be expressed in the lungs of A(H1N1)pdm09 patients . Since w+ T cells is inversely correlated with CD4+ and CD8+ T cell proportions, but this correlation was only observed in pH1N1+ patients; this finding could be explained by the fact that the lymphopenia induced by A(H1N1)pdm09 has been reported to be more severe and refractory than that associated with seasonal infection, which is modest during the first days and resolves earlier [+ T cells and the proportion of T cells observed only in pH1N1+ patients [In A(H1N1)pdm09 infected patients, PD-L1 expression on CD8 earlier . Since wpatients , 33.Our data suggest that viral infection may impair the induction of an efficient adaptive immune response in the early stages of infection by promoting PD-L1 expression on DCs and T cells; this could be a mechanism of immune evasion by the A(H1N1)pdm09 virus, similar to that reported in chronic and acute viral infections , 34–36. The 2009 pandemic influenza A(H1N1) virus is able to impair T cell responses through PD-L1 expression, suggesting that the virus could modulate host immune responses during infection by this mechanism.+ T cells (Tm). (a) Gating strategy reflecting the purity of isolated memory CD4+T cells (Tm) characterized by the CD3+CD4+CD45RO+CD45RA− phenotype (purity of 99.6%). (b) Gating strategy for sorting cDCs. (c) Gating strategy to phenotype cDCs, purity of 90.0% of lineage negative cDC population , HLA-DR+, and CD123dim.Figure S1. Representative plots of sorted conventional dendritic cells (cDCs) and isolated memory CD4+ and CD8+ T cells after 18 h of stimulus. Enriched (HLA-DR+ cell-depleted)T cells and DCs (b) were stimulated with pH1N1, SEB or CL264; PD-L1 expression in DCs and T cells was analyzed by flow cytometry and representative histograms are shown. M: medium.Figure S2. The A(H1N1)pdm09 virus does not induce PD-1 expression;whereas PD-L1 expression on DCs is induced directly by the A(H1N1)pdm09 virus whilst PD-L1 expression on T cells is dependent on the presence of antigen-presenting cells. (a) PBMCs were stimulated with A(H1N1)pdm09 virus (pH1N1), seasonal influenza virus (H3N2), staphylococcal enterotoxin B or synthetic TLR7 agonist (CL264); PD-1 expression in DCs and T cells was analyzed by flow cytometry. Fold increase in PD-1 expression in cDCs and pDCs, CD4+ T cells after 5 and 7 days of culture with A(H1N1)pdm09. (a) PD-L1 expression on isolated memory CD4+ T cells, 7 days after co-culture with sorted cDCs in the presence (blue) or absence (red) of pH1N1 virus. (b) PD-L1 expression on cDCs cultured for 5 days in the presence (blue) or absence (red) of pH1N1.Figure S3. PD-L1 is expressed in cDCs and memory CD4−HLA-DR+CD123dim) and pDCs . Gating strategy and representative histograms of PD-L1 expression in CD4+ T cells  and CD8+ T cells . The shaded histogram represents PD-L1expression in a healthy control, whereas the blue and red histograms are representative of two pH1N1+patients.Figure S4. Gating strategy and representative plots of analyzed dendritic (DCs) and T cells from patients and healthy controls. Gating strategy and representative histograms of PD-L1 expression in cDCs (a, LinClick here for additional data file.


The observed loss of ORF2p function is independent of the endonuclease or reverse transcriptase activities of ORF2p and RNA interaction required for reverse transcription. In addition, the loss of function is physically separate from the cysteine-rich motif sequence previously shown to be required for RNP formation. Our data suggest an additional role of the less characterized carboxy-terminus of the L1 ORF2 protein by demonstrating that this domain, in addition to mediating RNP interaction(s), provides an independent and required function for the retroelement amplification process. Our experiments show a functional modularity of most of the LINE sequences. However, divergent evolution of interactions within L1 has led to non-reciprocal incompatibilities between human and mouse ORF2 cys-domain sequences.LINE-1 (L1) retroelements emerged in mammalian genomes over 80 million years ago with a few dominant subfamilies amplifying over discrete time periods that led to distinct human and mouse L1 lineages. We evaluated the functional conservation of L1 sequences by comparing retrotransposition rates of chimeric human-rodent L1 constructs to their parental L1 counterparts. Although amino acid conservation varies from ∼35% to 63% for the L1 ORF1p and ORF2p, most human and mouse L1 sequences can be functionally exchanged. Replacing either ORF1 or ORF2 to create chimeric human-mouse L1 elements did not adversely affect retrotransposition. The mouse ORF2p retains retrotransposition-competency to support both Alu and L1 mobilization when any of the domain sequences we evaluated were substituted with human counterparts. However, the substitution of portions of the mouse cys-domain into the human ORF2p reduces both L1 retrotransposition and Alu  The activity of LINE-1 (L1) elements has contributed both directly and indirectly to almost a third of the human genome mass A full-length human L1 is about 6 kb, consisting of a 5′ untranslated region (UTR), two open reading frames separated by an intergenic sequence, and a 3′ UTR which ends in a poly(A) signal and an A-tail Comparison of the consensus sequences of the currently active young human and mouse L1 elements reveals different lineage-specific areas of amino acid conservation. In this manuscript, we present data from an array of chimeric human-mouse L1 elements and chimeric ORF2p that allowed us to determine species-specific differences that influence retrotransposition. In addition, these data provide insight into the less characterized cys-domain and suggest an important functional role for a less conserved region of the cys-domain that is distinct from the RNA binding function of the highly conserved cysteine-rich motif.ex vivo assays RP  contain multiple premature polyadenylation signals and splice donor and acceptor sites within their sequences The human and rodent L1 lineages have evolved independently for approximately 80 million years. The ORF1p and ORF2p of the currently active human and mouse elements share approximately 35% and 63% amino acid identity, respectively. To test for a species-specific interaction between L1 ORF1–ORF2 proteins, we individually substituted the ORF1 or ORF2 sequences of a tagged mouse (psmL1) and human (L1PA1) L1 construct with the same ORF from the opposite species and used these chimeric L1 elements in the retrotransposition assay. All of the chimeric ORF1–ORF2 L1 constructs maintain retrocompetence ; howeverWe generated 12 human-mouse ORF2 chimeric proteins by swapping homologous sequences between species and selected sequence breakpoints to preserve distinct functional domains . We subdChimera breakpoints were introduced in areas of conserved amino acid sequence ranging from 3–9 identical residues surrounding the breakpoints . The proin trans in the human HeLa cell line. Because ORF2p alone is sufficient to drive Alu retrotransposition in the assay HMM; and human with mouse endo: MHH) and RT domains  were all functional, with only MHM showing more than a 35% drop in retrocompetence (30.9% activity relative to mouse: MMM)  .H and MMmhh, and MMmmh). The same is not true for the human ORF2 protein. The only human chimera with a portion of the mouse cys domain that effectively supported Alu retrotransposition in trans was HHhhm  did not support Alu retrotransposition. Transfections with HHhmm and HHhmh chimeras produced significantly fewer colonies than the negative empty vector control  and HHM transfections showed a similar trend but were not significantly different than empty vector. We therefore considered that the colonies from these three sets of transfections likely represent background colonies derived from endogenous L1 proteins in HeLa cells i.e., ∼1000 fold reduction), a decrease in retrotransposition rate of this magnitude compares to that observed for the endonuclease and reverse transcriptase mutant L1 constructs, which are considered to be essentially “non-functional” i.e., not significantly different from the empty vector control; two sample T-test, p≥0.238) irrespective of the cell line utilized, while HHhhm was not significantly different from the single species ORF2p positive control .The mouse ORF2p is functional when any portion of its cys domain is substituted with human counterparts  for evaluation in the context of our tagged human L1 construct. As with periment , suggestInefficient expression could explain the reduced retrotransposition activity observed for the ORF2 chimeric protein. We assessed protein expression using western blot analysis of extracts from cells transfected with all the chimeric ORF2 constructs. A band with an apparent molecular weight (∼150 kDa) corresponding to ORF2p was detected for human and chimeric ORF2p transfections . These r−RT− ORF2p control  compared to the empty vector and the EN control . We nextin vivo work demonstrated that the L1 5′UTR can be completely removed and replaced by another unrelated promoter, such as the composite CMV enhancer modified chicken β-actin promoter RPRP retrotransposition rate relative to L1.3, an increased amount of the full length L1 transcript, and possibly an increase in translation efficiency due to the codon optimization of the construct.L1 elements have been replicating and evolving in mammals for approximately 100 million years. Because a retroelement is generally parasitic upon its host genome, the potential for parasite-host coevolution may have contributed to some independent evolution of L1 sequences and host factors in different lineages. The basic organizational structure of L1 is the same for all mammalian elements, consisting of the 5′UTR-ORF1–ORF2-3′UTR and poly(A). Previous The modularity of L1 sequences becomes more evident with our observation that chimeric L1 human-mouse ORF1–ORF2 constructs maintain retrotransposition competence. Of particular interest, the ORF1 sequence was subjected to intense positive selection during a brief period of primate evolution, resulting in a high rate of amino acid replacement in the coiled-coil domain, which is currently conserved in the human ORF1p in trans was independent of the species (mouse or human) of the transfected cell line. We previously demonstrated that ORF2p from both mouse and human L1 sources mobilizes tagged Alu transcripts in chicken cells in vitro work demonstrated that this domain maintains endonuclease activity independent of the rest of the ORF2 protein The ability of chimeric human-mouse ORF2 proteins to mobilize Alu The simplest interpretation of our data is that predominantly human ORF2 protein is incompatible with the middle subsection of the mouse cys domain. All three of our chimeras that fit this description were essentially non-functional, while reciprocal mouse ORF2 chimeras maintained retrotransposition competence. Functionality of these chimeras could not be rescued by the use of cells from either species or by the addition of ORF1p in the context of the bicistronic L1 constructs. Given the important role that the cys-domain appears to play in functionality, it is notable that the highly conserved cys-motif is not associated with the observed species-specific difference. Reciprocal chimeras that only affected the third subsection of the cys-domain containing the conserved cys-motif (HHhhm and MMmmh) were both highly functional.i.e., HHM→MHM). However, the MHM ORF2 is only ∼30% active relative to the parental HHH or MMM constructs. We observe a higher recovery effect with the RT domain, as functionality is also restored when the non-functional HHM is compared to the functional HMM construct with ∼66% activity of HHH or MMM. Because activity is not fully restored in either of the two chimeric proteins, it is possible that the decrease in retrocompetence is due to negative sequence interactions and/or inefficient structural folding of the ORF2 protein.The inhibitory effect caused by one or more sequence stretches of the mouse cys-domain in the context of the human protein appears to be independent of the endonuclease or reverse transcriptase activities of ORF2p, as demonstrated by our functionality assays. Previous work demonstrated that substitutions of the cysteines in the cysteine rich motif lowered the amount of ORF2p in the RNP fraction and reduced LEAP activity, suggesting a possible reduction in the ORF2p-RNA interaction Interestingly, the smallest tested sequence with negative impact (165 aa of the middle sub-section of the cys-domain) is also the one with the lowest amino acid identity , which cCHmneo, contains the fully codon optimized ORF1 and ORF2 of the human L1RP cloned in the pBluescriptII vector (Stratagene). A schematic of the synthetic L1 vector is shown in http://www.changbioscience.com/primo/primoo.html), which makes synonymous changes to optimal codons within the nucleotide sequence. A 63 bp inter-ORF with 92% sequence identity to L1RP was utilized for all constructs. Sequence changes to this region were generated to introduce restriction enzyme sites used for plasmid construction. The optimized nucleotide sequence is 74.9% identical to the sequence of L1RP. The “notag” constructs contain an SV40 polyadenylation signal at the 3′ end, and the others contain the mneoI cassette including the SV40 polyadenylation signal from JM101/L1.3 pBS-L1PA1CHrescue, contains the L1RP ColE1-mneo cassette of SynL1neo mneoI cassette. The ColE1-mneo cassette was amplified with the FseI-NeoTag: 5′-TCCTAGCTGGCCGGCCTTTTATTGCCGATCCCCT-3′ and Not-NeoTag: 5′ –TCCTAGCTGCGGCCGCAACGCGCGAGGCAGCCGGATCATA-3′ and cloned into the FseI and NotI sites of pBS-L1PA1CHmneo (pBS-L1PA11CHmneo .spa (a kind gift from Dr. Jef Boeke) RP ORF2 protein sequence and was codon optimized by Blue Heron Biotechnology, Inc . The open reading frames are cloned into the expression vector pBudCE4.1 (Invitrogen), under control of the CMV promoter. The nomenclature and break points of the chimeric ORF2 constructs are described in pBudORF2 human-mouse chimera were generated by a PCR approach using overlapping primers mneo and pBS-L1-1M,2Hmneo were derived from pBS-L1PA1CHmneo and contain either the mouse ORF1 or ORF2 of psmL1 pBS-L1-1H,2MRP) in psmL1 pCEP-L1synM-1H and pCEP-L1synM-2H contain the codon optimized human ORF1 or ORF2  JM101/L1.3, referred to as “wildtype” L1, contains a full-length copy of the L1.3 element and the All plasmid DNA was purified by alkaline lysis and twice purified by cesium chloride buoyant density centrifugation and quality assessed by the visualization of agarose gel electrophoresed aliquots stained with ethidium bromide. Constructs were confirmed by sequencing .5/T75 flask or 2.5×105 cells/T25 flask. Transient transfections were performed the following day with Lipofectamine Plus (Invitrogen) following the manufacturer's protocol using 1 µg of Alu-neoTET vector plus 0.33 µg of the ORF2 construct or empty vector (T25 flask). For L1 retrotransposition assays between 1 or 3 µg of the tagged L1 plasmids was used for transfection (T75 flask). The next day, the cells were treated with the appropriate media containing 400 µg/ml for HeLa and 800 µg/ml for NIH3T3 cells of Geneticin/G418 (Fisher Scientific). After 14 days, cells were fixed and stained for at least 30 minutes with crystal violet . Retrotransposition rate was then determined as the number of visible G418R-resistant colonies/106 cells seeded. Both L1 and Alu G418 resistant colonies were validated as authentic retrotransposition events (Transient L1 or Alu retrotransposition assays were performed as previously described with some minor modifications n events .6 for transfection with 5 µg of the L1 plasmid constructs. Cells were harvested 48 hours post-transfection for RNA extraction. RNA extraction and poly(A) selection was performed as previously described 5′-TAATACGACTCACTATAAGGACGAGGCAGCG-3′ and Neo northern(+): 5″- GAAGAACTCGTCAAGAAGG-3′. The cyclophilin riboprobe was generated from pTRI-cyclophilin template (Ambion) to use as a control for transcript quantitation analyses. Riboprobes were radioactively labeled by incorporating 32P-UTP (Amersham Biosciences) using the MAXIscript T7 kit (Ambion) following the manufacturer's recommended protocol. The radiolabeled probes were purified by filtration through NucAway Spin columns (Ambion). Separate hybridizations were performed overnight with 4–12×106 cpm/ml of each individual probe at 60°C. The membrane was washed twice at high stringency  at 60°C before analysis using a Typhoon Phosphorimager (Amersham Biosciences) and the ImageQuant software.HeLa cells were seeded at a density of 4×10Protein was extracted from trypsinized and PBS-washed transiently transfected NIH3T3 or HeLa cells using standard Tris SDS glycerol buffer plus 2-mercaptoethanol and boiled for 15 min. Extracts were electrophoresed on 4–12% NuPage Bis-Tris gels (Invitrogen). Proteins were transferred to a nitrocellulose membrane using the iBlot gel transfer system (Invitrogen). The goat anti-LINE  and secondary HRP-conjugated antibodies were purchased from Santa Cruz Biotechnology Inc. The membrane was incubated for 1 h at room temperature with the primary or secondary antibody diluted 1∶500 and 1∶5000 in PBS pH 7.4 plus 0.05% Tween 20 and 3% non-fat dry milk (Biorad), respectively. Signals were detected using the SuperSignalWest Pico Chemiluminescent Substrate  and Amersham ECL hyperfilm .5 cells/ml. Gamma-irradiated (5 Gy) cells were used as positive control, untreated cells as background and the empty vector plus an endonuclease and reverse transcriptase ORF2 mutant as negative controls. The alkaline comet assay was performed by using the CometAssay Reagent Kit for Single Cell Gel Electrophoresis  following the manufacturer's instructions. Cells were mixed with low melting agarose  and embedded on a slide . Slides were incubated in cold lysis solution (Trevigen) for 45 minutes at 4°C. Slides were then incubated in Alkaline Solution, pH>13  for 40 minutes at room temperature in the dark. Cells were then electrophoresed in Alkaline Electrophoresis Solution, pH>13  in a horizontal gel apparatus  at 1 V/cm for 30 minutes at 4°C. Slides were rinsed briefly in dH2O and then incubated in 70% ethanol for 5 minutes. Slides were air-dried overnight and DNA stained with SYBR Green I (Trevigen). Cells were analyzed using an epifluorescence microscope . Digital images were acquired with a SensiCam QE digital camera . Tail moments were determined for at least 75 random cells per sample by using Comet Assay IV  comet scoring software. Tail moment is defined as the ratio of DNA in the comet tail to total DNA.HeLa cells were transfected with 1 µg of plasmid expressing the different ORF2 expression plasmids and controls. After 24 hours, cells were harvested and gently resuspended in phosphate buffer saline (PBS pH 7.4) to a concentration of 106/T75 flask) were transfected with 10 µg of the ORF2-expressing constructs. Cells from two transfected flasks were pooled and harvested 48 h post-transfection following the previously described protocol 5′-GAGCACAGAATTAATACGACTCACTATAGGTTTTTTTTTTTTVN-3′ and LEAP NA: 5′-GCGAGCACAGAATTAATACGACTCACTATAGGTTTTTTTTTTTT-3′. No LEAP primer was added to the negative control. The LEAP cDNA product was PCR amplified using the anchor primer (5′- GCGAGCACAGAATTAATACGACT-3′) and a primer specific to a sequence shared by both the mouse and human ORF2p constructs near the 3′end of the gene (ORF2-3′B: 5′-ACGGATCCGAACAAAAACTCATC-3′). A 3 µl aliquot of the LEAP product was PCR amplified for 35 cycles of 20 s at 94°C, 30 s at 62°C and 30 s at 72°C, with a final cycle of 5 min at 72°C and visualized on a 2.5% agarose gel.HeLa cells  to align ORF2 protein sequences from human (L1RP), mouse (L1spa), rat, dog, rabbit, cow, tree shrew, and horse. Sequences other than human and mouse were collected from the Repbase Update database (TIF)Click here for additional data file.Figure S2The human and mouse chimeric HHM, HHhmm, and HHhmh chimeric L1 ORF2ps are unable to support Alu retrotransposition in a rodent cell line. NIH3T3 (mouse) cell line was transiently transfected with the three ORF2p chimeric proteins that were non-functional in HeLa and a tagged Alu vector. All three constructs  generated Alu colonies at a rate that was not significantly different than the empty vector control. The results in rodent cells NIH3T3 emulate the results obtained in the human HeLa cells, the other cys-domain mutant (HHhhm) was functional. The functionality of these ORF2 human-mouse chimera appears to be independent of cell line species (mouse or human). Two sample T-test analysis showed: * Significantly different than HHM, HHM2, HHhmh, and empty vector p<0.01; $ Not significantly different than empty vector p≥0.238; # not significantly different than MMM p = 0.210.(TIF)Click here for additional data file.Figure S3Amino acid alignment between the non-exchangeable region of the L1 ORF2 cys domain of mouse and human shows low and dispersed amino acid identity. The region encompassing 160 amino acids of the mid-section of the cys domain of the L1 ORF2p is shown. The consensus is shown at the top; identical amino acids are shown and non-identical amino are shaded in gray.(TIF)Click here for additional data file.Figure S4The L1 and Alu inserts recovered present the features of bona fide retrotransposed elements. L1 and Alu G418R colonies were selected and grown to confluency. DNA was extracted from the cells using the DNA Easy kit (Qiagen) following the manufacturer's recommended protocol. A. L1 inserts were recovered and analyzed using a previously published method CH tagged with the mneoI/ColE1 cassette B. The extracted DNA from cells transfected with the tagged Alu vector plus the different ORF2 expression constructs was evaluated by performing nested PCR with primers designed to amplify the sequences flanking the self splicing intron, F1: 5′-GGGCGCCTGTAGTCCCAGCTA -3′; F2: 5′-TAGCAGCCAGTCCCTTCCCGCTTCA-3′; R1: 5′-GTCAGCGCAGGGGCGCCCGGTTC-3′ and R2: 5′-ACTGGGCACAACAGACAATCGGC-3′ ). The annealing location of the primers are shown in the schematic of the Alu construct. An open arrowhead indicates the PCR product corresponding to an insert containing the spliced version (open arrowhead) of the Alu expression vector. An open arrowhead indicates the nested 217 bp PCR product corresponding to an insert containing the spliced version (open arrowhead) of the Alu expression vector. Lanes correspond to the following chimeric ORF2 constructs: 1- MMmhh, 2- MMH, 3-HHmhh, 4-HHhhm, 5- MMmmh, 6- HMM, 7- MHH, 8- MHM, and 9- HMH. M denotes the DNA, and the Alu expression plasmid (P lane) was used as the control for 625 bp unspliced product (black arrow).(TIF)Click here for additional data file.


In the majority of countries, surnames represent a ubiquitous cultural attribute inherited from an individual's ancestors and predominantly only altered through marriage. This paper utilises an innovative method, taken from economics, to offer unprecedented insights into the “surname space” of the Czech Republic. We construct this space as a network based on the pairwise probabilities of co-occurrence of surnames and find that the network representation has clear parallels with various ethno-cultural boundaries in the country. Our inductive approach therefore formalizes a simple assumption that the more frequently the bearers of two surnames concentrate in the same locations the higher the probability that these two surnames can be related . Using the Czech Republic as a case study this paper offers a fresh perspective on surnames as a quantitative data source and provides a methodology that can be easily incorporated within wider cultural, ethnic, geographic and population genetics studies already utilizing surnames. The spatial distribution of surnames is far from random. Differences in early naming practices and unique regional, geographic, demographic, or migratory influences have led to considerable specificity with regard to mix of surnames that can be found in a particular place. Such specificity has been shown to capture a great deal of ethno-cultural variation that is often intertwined with the characteristics of an area Such applications of surname research are based on the key assumption that the spatial structure of surnames can, at least to some extent, mirror other aspects of population structure. To extract information from surnames, the challenge is to discern meaningful patterns from complex spatial distributions with little a priori information . To our knowledge there has so far not been any attempt to capture the entire surname structure of a country through the pairwise comparison of geographic distributions of individual names. Previous research has ignored the spatial component altogether This paper seeks to examine the surname structure of the Czech Republic (Czechia) by employing a suitable pairwise measure of relatedness between individual surnames based on their frequency of spatial co-occurrence in terms of their joint spatial concentration. This measure formalizes a simple assumption that the more frequently the bearers of two different surnames concentrate in the same locations the higher is the probability that these two surnames can be “related”. In this context, relatedness corresponds to surnames formed within the same community and those informed by similar cultural, ethno-linguistic or other factors. Using this measure, we depict the aggregate surname structure of Czechia as an undirected network of surnames linked by the degree of their relatedness. This representation can be conceptualised as “Czech Surname Space” and offers a template for similar research in other countries. Our inductive approach focuses on the revealed relatedness; only after the Czech surname space is determined do we map its structure and examine possible coincidences with other aspects of the Czech population differentiation.A focus on the spatial co-occurrence of surnames makes this paper distinct from previous studies. The bulk of the literature typically concerns pairwise comparisons between spatially defined populations based on the (di)similarity of their respective surname compositions The approach adopted here is a departure from previous research in the sense that the spatial distributions of individual surnames are the key input; regional patterns emerge as groupings in the surname space. Such approaches seek to establish the extent to which two or more geographic areas share the same pool of surnames and therefore offer comparisons between spatial units rather than the surnames themselves. With traditional methods, broad surname regions can be reliably produced but at the risk of subsuming some of the smaller groups of surnames with non-contiguous spatial patterning. Migrant surnames may, for example, be well-represented in these smaller groups and therefore more easily isolated than when using a traditional measure to produce more aggregate results. Improved granularity comes at the expense of increased computing overheads and a far more complex result (due to its larger number of comparisons), but we feel that capability to handle and interpret such outputs is increasing all the time and, as such, the methodology will become more widely applicable.LQ). For individual surnames (i) and regions (r), respectively, it can be expressed as:i,rF stands for the absolute number of bearers of the surname i in the region r. The i,rLQ compares the relative share of people with the surname i in the population of the region r relative to the share of this surname in the whole population at a more aggregate level. An i,rLQ >1 indicates that the surname in question is more prevalent in the region r than in the whole population (below we simply say that the surname concentrates in the region r).The first step in defining a surname spatial similarity measure is the selection of an appropriate form of input data for describing the occurrence of individual surnames in particular regions. A simple consideration of the absolute numbers of bearers would be inappropriate in the present context because the size of subpopulations of individual surnames varies immensely. A better metric that accounts for both the spatial concentration and the ubiquity of individual surnames is the location quotient . As stated above, we consider this network as an appealing representation of the Czech surname space. It can be examined both globally in terms of its aggregate patterns, its shape, or the number of communities, and locally through extracting the positions of individual surnames or their groups. Both of these aspects are important with respect to our inductive analysis that is driven by an expectation of detectable clusters or communities and surnames with strong internal and relatively weak external relatedness.Given the large sample of surnames analyzed it was necessary to run a series of computationally intensive calculations to obtain an extensive matrix of surname-surname proximity observations . Such a matrix tends to be very sparse with a large number of zero or negligible observations and very few more significant observations. It is therefore conducive to data mining through network analysis . With this the network can be conceptualised as a physical system where nodes (surnames) influence each other via attracting forces with strengths proportional to their revealed relatedness. The algorithm minimizes the energy of the physical system and assigns the nodes with positions in two-dimensional space accordingly.For the network visualization we used Cytoscape, open source software suitable for handling large complex networks i,jD (denoted as d) determined by, for example, inspecting the frequency distribution of the proximity observations provides a logical criterion. Considering a certain d, a surname space visualisation consists of n surnames and m surname-surname relatedness links, when:For the network visualisation to be interpretable, the majority of negligable links should be removed. A threshold of i is denoted as ik and it corresponds to the number of its revealed relatedness links to other surnames equal or above chosen d:This provides the basis to defining some simple local and global characteristics of the surname network, similarly to basic measures used in the network analysis ik implies that surname in question co-occurs (concentrates in similar regions) with many other surnames within a given surname space or its sub-space. In other words, a high ik indicates that a surname i is highly embedded in the surname space or its sub-space  and that it can be considered an examplar of a local population.This measure is particularly interesting in the present context because it can be considered as a simple measure of the node centrality. A high c) expressed as:ρ): that is the proportion of actual number of links in the surname space relative to the maximum possible number of links:In addition, two basic global parameters of a surname space can be introduced in terms of the mean surname degree . As such, they provide interesting information about the extent of internal population homogeneity.This paper draws on a unique dataset containing the occurrence of individual surnames in each of the 6,253 Czech municipalities derived from the 2009 Central Population Register (produced by the Czech Ministry of the Interior). The data cover all those with permanent residence; that is Czech nationals and foreigners staying on a long-term basis. The 10,705,763 individuals listed share 362,125 unique surnames.It is conventional in Czechia to have male and female variants of the same surname. Both exhibit almost identical spatial distributions negating the need to include both forms and so the female variants were omitted. This dramatically reduced the volume of data. Fortunately, nearly all Czech feminine derivatives are easily distinguishable by the suffix “–á”. The exceptions are comparatively more frequent for certain surnames typical of eastern Moravia and Silesia For the analysis of co-occurrence we decided to work with male surnames with a frequency exceeding 49 bearers in the whole country. With this filter applied the data comprised 15,487 most frequent male surnames and 4,347,283 individuals corresponding to 83% of total male population. The cut-off was chosen in the light of the following: firstly, the size distribution of surnames is heavily right skewed and the inclusion of less frequent names would make our analysis excessively computationally intensive (as described below); secondly, and more importantly, the consideration of less frequent surnames would considerably increase a risk of contamination of results by random co-occurrences of rare surnames; thirdly, we also noted that the spatial distribution of rare surnames in Czechia is quite uneven with significantly higher shares of such surnames in peripheral areas and especially in the region of Silesia . i,j,regD observations attaining a value exceeding 50% of the maximum observation. In other words, while an overwhelming majority from all of analyzed pairs of surnames reveal a negligible relatedness, there is also a tiny proportion of those pairs that are interesting in the present context because of their high mutual proximity.We first examined the co-occurrence of 15,487 unique male surnames over 206 Czech micro-regions. The calculations for all possible pairs of these surnames produced a matrix of 119,915,841 proximity observations , Hrubý , Liška (a fox), Toman (after Thomas the Apostle), Kočí (a coachman), Prokop . The ubiquity of these and similar surnames is determined by a meaning independent of regionally specific naming practices (a common naming practice in many countries). Of the 675 male surnames with the frequency above 1,000 bearers only 17% of them fall into this group of spatially ubiquitous surnames. Importantly, it implies that the majority of the most frequent surnames exhibit some kind of spatial concentration. Arguably, the tendency towards spatial concentration is expected to be even higher for the less popular names.Before examining the most significant links it is worth discussing some of the highly ubiquitous surnames in terms of greater prevalence but low spatial concentration. The following six surnames have more than 4,000 bearers but have no observation exceeding i,j,regD  = 0.525 (or 60% of the maximum) offers a good threshold for distinguishing these important observations as it lies in the area beyond which the rank-size curve rapidly flattens. Using the conditional probability interpretation of the Dice coefficient, we can say that two surnames connected by a link satisfying i,j,regD ≥0.525 have at least 52.5% probability that one of these surnames concentrates in a region where another is concentrated.For this paper, the most interesting information is contained in observations pertaining to the steep left part of the rank-size plot in Unfortunately, despite this cut-off, the surname space still contained too many nodes to be reasonably visualised. We thus further limited the displayed results to surnames with at least 100 bearers. This value is based on insights from a number of preliminary experiments examining the trade-off between the number of surnames displayed (complexity of displayed surname network) and graphical limitations of our network visualisations (readability of the network). As a result, we obtained a set of 8,405 proximity links connecting 2,429 unique male surnames. After applying the weighted force-directed layout algorithm, the aggregate version of the Czech surname space was generated and visualized in The Czech surname space illustrated in The key feature of each network graph is its degree distribution. In a random graph, nodes have a similar probability of being connected and therefore the degree distribution tends to be homogenous as signified by a binomial shape. By contrast, real world networks of various complex phenomena are typically hierarchically organized, with an inhomogeneous, considerably right skewed degree distribution. Here, a highly inhomogeneous degree distribution has been found  suggestii, the region of its prevailing concentration refers to a region with the maximum i,rLQ . The three distinguished groups within the main Bohemian core were indicatively marked as A1, A2, and A3 in The most extensive cluster of surnames in the Bohemian part of the Czech surname network in The group A1 includes typical Bohemian surnames in terms of the most frequent  in addition to other lower frequency, and traditional, Bohemian surnames. Although the more populous of these surnames are widely found across the country, all of the surnames from this group tend to be concentrated in the south and west regions of Bohemia see also . The secIn addition to the main core, there is another dense cluster in the Bohemian part of the Czech surname network. Labelled as A4 in The second Moravian-Silesian part of the Czech surname space has two dense cores in terms of the main Moravian cluster (B1) and Silesian cluster (B2). In addition, there is also a relatively dense area between these main cores consisting of names typical for various more specific regions in the north, central, eastern Moravia. In the case of Moravian surnames, linguistic differentiation of surnames and spatially specific naming practices are clearly recognizable. For example, a majority of names that apparently originated from verbs  are located in the lower left and upper parts of the main Moravian cluster (B1). Some notable examples of these names, with a quite central position in our surname network (see below), are Zapletal , Přikryl (from “to cover”), or Hradil (from “to block”). In In addition, An intriguing exception to these explanations is a typical Czech surname Vlček that can also be found in i,j,munD) among 5,660 surnames selected on the basis of the highest revealed relatedness at more aggregate spatial level. This sample of surnames covers almost a half of the Czech male population. Given the significantly higher number of spatial units considered for this second stage of our analysis, the values of i,j,munD are generally lower than i,j,regD in the first stage which focused on co-occurrence in 206 micro-regions only. At the same time, the size distribution of these second stage results is even more skewed to the right; the maximum i,j,munD  corresponds to 0.687, while only 0.011% of all observations exceed 50% of the maximum value. These differences between the first and second stage results are understandable and go hand in hand with the expectation that the surname network based on the municipality level calculations will be more fragmented.In the second stage of our analysis we examined the co-occurrence of Czech surnames at the finest spatial level of 6,244 municipalities. We began with the calculation of the pairwise indices of revealed relatedness . Regarding Moravian surnames, again the commonality of verb-derived surnames emerges, as they form the majority of names in the B1 area of the network. The Bohemian part of the surname space (A) is structured into three main groups of surnames. The A1 cluster comprises some of the most frequent surnames and those prevalent across most of Bohemian regions, whilst the separation from the secondary cluster (A2) is hardly discernible. By contrast, two other core areas are well recognizable and represent northern and eastern Bohemian names (A3) more specifically and surnames concentrated mainly in municipalities in the north-west and west of Bohemia (A4).i,j,regJ and i,j,munJ were considered instead of the i,j,regD and i,j,munD, respectively). However, the main value of this second stage municipality level exercise should be seen in individual details uncovered with respect to local parts of the surname network. A number of interesting examples of pairs of surnames that have been found as potentially closely related, regionally specific offshoots of the surname network, or specific groups of surnames determined in various ways, could be identified, mapped, and examined in greater depth.The general congruence in macro-structure of the surname networks constructed here and in the first stage of our analysis is an important finding  than those with very different distributions. Through the application of suitable measures of spatial co-occurrence, the extent of revealed relatedness between individual pairs of surnames was quantified. The focus here was not an intensive examination of the proximities between particular surnames; instead, the ultimate goal was to understand the aggregate pattern of the Czech surname space, anticipating that some innovative insights about the Czech population structure can be gathered in this way too.We conceptualized and represented the Czech surname space as an undirected network of surnames linked by their pairwise revealed relatedness. This approach demonstrated the utility of network representations and techniques in the context of surname data that appears to share several properties often attributed to other complex networks. These include a relatively inhomogeneous structure, considerably skewed degree distribution, and multi-layered composition determined by a highly right skewed frequency distribution of surnames. This falls hand in hand with a pronounced hierarchy regarding spatial scales on which the concentrations of these surnames occur.Indeed, the results confirmed a great deal of correspondence between the macro-structure of the Czech surname space and the main cultural and historical macro-divisions of the Czech population. The more detailed analysis has proved useful in offering numerous more nuanced insights about Czech population structure such as the identification of less known secondary divisions or specific clusters of surnames. It has also been shown that the inspection of network parameters such as density or the mean degree between particular parts of the surname space can be used for comparing the extent of homogeneity and stability between different populations or their parts.This work represents an initial foray with a wide range of further applications. Importantly, most of the methods presented here are scalable so that they can be analogously used for analyzing different spatial systems or different parts or regions within one spatial system.Another possible application is related to the identification of the clusters of high degree surnames found in the cores of the Czech surname network. These “hub” surnames can be regarded as the most typical and stable exemplars of their respective parts of the surname space, and together, can be considered a backbone of the Czech surname space. The identification of these most typical and stable surnames (and mapping of the main areas of their concentrations) offers a valuable tool for population geneticists, who for example are seeking to optimise their sampling design. Such names can indicate aspects of population structure, such as rates of population turnover, that may be more or less conducive to genetic sampling. For example, it would be ineffective to target a population group comprising large numbers of migrants if trying to characterise the genetic attributes of the historic population of the specific area in which the migrants reside. In this sense, our study provides another example of promising potential for integration of geography and genetics Although our analysis utilized current cross-sectional data, there exists a potential for insights into long-term population processes. This is most evident in relation to the enduring spatial stability of a majority of Czech surnames in spite of a long history of population movements. Such movements, therefore, appear to have only marginal impacts on regional surname structure. The exceptions are rare but notable as they point to the radical population changes associated with the expulsion of Germans from the post-war Czechoslovakia and subsequent resettlement of the formerly largely German speaking areas. This presents further avenues for research that could, for example, focus on the separate surname network for the former German areas and compare its parameters with the rest of the country. If an appropriate theoretical framework is applied, this one-time population shock can be considered as a kind of “natural experiment” and the persistence and resilience of the affected surname system may be examined using the methodology described above.Another notable feature perturbing the stability of the Czech surname space is the specific spatial behaviour of various minority population groups including international migrants. Although, in quantitative terms, these groups still represent a minor part of the Czech population, this study has shown that they are a well-delineated segment. On this basis, our analysis may be considered a tool for the classification of surnames into ethnic groups based solely on their spatial characteristics. It can be thus considered as an alternative to existing approaches to name-based ethnicity classifications that harness pre-existing ethnic categories of surnames In summary, this study sought to demonstrate the applicability of a new approach to surname research as a means of revealing the underlying surname structure of a country, in this case the Czech Republic. It is our hope that the perspective and methodology adopted here can serve as a template for similar studies in other countries and facilitate further interdisciplinary research in this area.Figure S1Ethno cultural differentiation of Czechia.(TIF)Click here for additional data file.Figure S2High resolution version of Czech surname space based on surnames co-occurrence in micro-regions.(PDF)Click here for additional data file.Figure S3The Czech surname space based on surnames co-occurrence in micro-regions: node size proportional to the degree of particular surnames.(PDF)Click here for additional data file.Figure S4Surname degree versus surname population size.(PDF)Click here for additional data file.Figure S5High resolution version of Czech surname space based on surnames co-occurrence in municipalities.(PDF)Click here for additional data file.Text S1Tests of behaviour of i,jJ and i,jD with respect to differing population size.(PDF)Click here for additional data file.Text S2th century.Ethno-cultural differentiation of Czechia and main migratory trends over the second half of 20(PDF)Click here for additional data file.