MALAT1. Comparison of GRIP targets with databases of super-enhancers from the same cell lines showed that these have only limited overlap and that GRIP provides unique insights into the upstream drivers of cell growth. These observations elucidate the oncogenic potency of the gamma-retroviruses and support the wider application of GRIP to identify the genes and growth regulatory circuits that drive distinct cancer types.Retroviruses have been foundational in cancer research since early studies identified proto-oncogenes as targets for insertional mutagenesis. Integration of murine gamma-retroviruses into the host genome favours promoters and enhancers and entails interaction of viral integrase with host BET/bromodomain factors. We report that this integration pattern is conserved in feline leukaemia virus (FeLV), a gamma-retrovirus that infects many human cell types. Analysis of FeLV insertion sites in the MCF-7 mammary carcinoma cell line revealed strong bias towards active chromatin marks with no evidence of significant post-integration growth selection. The most prominent FeLV integration targets had little overlap with the most abundantly expressed transcripts, but were strongly enriched for annotated cancer genes. A meta-analysis based on several gamma-retrovirus integration profiling (GRIP) studies in human cells  revealed a similar cancer gene bias but also remarkable cell-type specificity, with prominent exceptions including a universal integration hotspot at the long non-coding RNA  Myc, Myb, Pim, Runx, Bmi1, Gfi1 and Notch [The ability of retroviruses to direct stable integration is inherently mutagenic and can perturb host cell genes at the proviral insertion site or even at a significant distance ,2. Whilend Notch –7. It wain vitro [The predilection of the lentivirus HIV to integrate into actively transcribed genes is a key feature of its pathogenesis where it transits between latent infection and cytopathic replication. For HIV, this process entails interaction between the viral integrase protein and LEDGF, a transcriptional co-activator that tethers the integration complex to chromatin and facilitates integration ,9. In coin vitro –15. Thisin vitro .in vitro prior to any significant growth selection. Large scale studies of MLV vector integration in human CD34 cells or MLV pseudotype infection of human cancer cell lines has revealed a remarkably selective process in which more than half of the integrations target less than 2% of the human genome [The full extent of departure from random of MLV integration has become clear only with the advent of large scale methods to capture and sequence integration sites in polyclonally infected cell populations n genome ,18. MoreIn this study we explored the integration preferences of another gamma-retrovirus, feline leukaemia virus (FeLV). We used FeLV-B, a common naturally occurring variant of FeLV that is capable of infecting virtually all cultured human cells without evident cytopathology  through in vitro, and this question was examined further by analysis of proviral orientation bias. While the integration process itself is random with regard to orientation, the propensity of gamma-retroviruses to activate host genes by enhancer insertion, a process which is strongly affected by orientation, leads to emergence of dominant clones with pronounced bias at key integration hot-spots that can be demonstrated statistically by a ‘heads-tails’ analysis [Cloning of FeLV-B integration sites in infected MCF-7 breast cancer cells by linker-mediated PCR and mapping to the human genome yielded 20,634 authentic virus-host junction fragments, corresponding to 8,052 unique insertions . The relanalysis . We applanalysis . Additioanalysis  Fig 1B)in vitro,To determine whether specific epigenetic marks were also targeted by the virus, data from the ENCODE consortium were analysed. ChIP-seq datasets for all available histone marks in MCF-7 cells were processed and peaks annotated as described in Materials and Methods. Available histone marks were H3K27ac, H3K9me3, H3K36me3, H3K27me3 and H3K4me3. The overlap of MCF-7 insertions with histone marks was determined and is summarised in HOTAIR, along with chromobox CBX2 and the long non-coding RNA MALAT1. In most cases the insertions coincided primarily with peaks of H3K27 acetylation, and to a lesser extent with the H3K4 trimethylation and H3K36 trimethylation marks, consistent with data above showing association with active chromatin marks .These observations encouraged us to carry out a more systematic analysis of the preferred insertion sites. Nearest gene analysis identified 6926 genes. The top 100 genes were determined by first selecting for statistical significance (p<0.05 using Fisher’s Exact test), and then ranking by order of the number of insertions. The top 100 MCF-7 integration targets by these criteria encompass 773 insertions , and are shown in www.qiagen.com/Ingenuity). We found that the top biological process defined for this gene set was ‘Cancer’ , providing further evidence of selectivity for cancer gene programmes , and the same criteria for known breast cancer annotation (direct association of the gene with breast cancer with subsequent verification by e.g. knockdown or expression studies). 29 of the top 100 MCF-7 integration targets had known breast cancer annotation, compared to 5 of the random 100 genes, a difference that was highly significant  detected increasing overlap with the most highly expressed genes, suggesting a bimodal selection process.The top 150 retroviral target genes showed no greater overlap with the top 150 highly expressed genes than expected by chance in MCF-7 cells . The loHaving shown that FeLV selectively targets cancer driver genes in a human breast cancer cell line, we applied the same approach to published datasets of ‘unselected’ gamma retrovirus integration sites in human cells to establish the generality of these observations. For this meta-analysis we used published insertion data from two human cancer cell lines, K562 and HepG2  and normDetails of the datasets used in the meta-analysis are summarised in The epigenetic marks targeted by the virus in each cellular context were examined as described above using ChIP-seq data from ENCODE (for K562 and HepG2 cells) or the Human Epigenetics Roadmap (for CD34 cells). The same histone modifications were considered as for MCF-7 cells, these being H3K27ac, H3K4me3, H3K36me3, H3K9me3 and H3K27me3. In accord with the previously published studies, there was a general enrichment for viral insertions at active chromatin marks  while repressive marks (H3K9me3 and H3K27me3) were disfavoured by the virus ,18. The 6), due to scoring of genuinely independent insertions at the major hotpots as duplicates. Evidence in support of this interpretation is provided by inspection of the distribution of insertions around transcriptional start sites. When these are normalised by peak height, the HepG2 shows a much larger basal level of non-TSS insertions, consistent with the saturation hypothesis , they yielded predicted upstream regulators with similar statistical scores. Again these upstream regulators revealed relatively little overlap between GRIP and super-enhancer regulated genes, illustrating the distinct perspectives on underlying growth programmes uncovered by these approaches. A further difference is that retroviral integration was significantly more likely to target the GRIP upstream regulators compared to the super-enhancer set .in vitro culture followed by deep sequencing of integration sites and computational analysis can be used to mine human cancer cells of many types directly for active gene programmes and potential therapeutic targets. Additionally, the value of this approach is likely to increase over time as knowledge of cancer genes and networks increases and databases are more comprehensively annotated. This study shows that gamma-retrovirus integration in human cells is skewed towards cancer driver genes in a highly cell-type specific manner. While gamma-retroviruses have been widely used as cancer gene discovery tools due to their insertional mutagenic potential in their natural hosts, this approach required the onerous task of collecting multiple end-stage tumours from animal models and comparative genomic analyses to confirm the relevance of findings to human cancer –7,11,30.Infection of human cells with gamma retroviruses requires the use of amphotropic variants or pseudotypes to overcome the lack of receptor for common strains ,18. FeLVin silico prediction of upstream regulators using Ingenuity Pathway Analysis, which strongly inferred a series of master oncogenes controlling the growth of the cancer cell lines. Moreover, the upstream regulators were themselves targets for retroviral insertions, showing that these gene loci had significant transcriptional activity in the cancer cells. Although cancer genes also emerged from GRIP targets and upstream regulators in normal CD34+ cells, predicted upstream regulators included cytokines that were not targeted by proviral insertion. While further analyses will be required to address this question, an interesting possibility is that GRIP in combination with upstream regulator prediction may be capable of discriminating normal cell growth pathways controlled by exogenous growth factors and cancer cell growth orchestrated by internal, constitutively active programmes.A meta-analysis was conducted to compare the FeLV GRIP dataset with previous large scale MLV and vector integration sets from normal and malignant human cells. A notable outcome was the selection for cancer genes in all cases, particularly in the top target genes for each cell type. This phenomenon was not fully replicated in the HepG2 dataset, but we note that this was by far the largest dataset where there was evidence of loss of definition due to saturation at the major hotspots. These observations should guide future application of GRIP, as the need for multiple insertions to ensure robust statistics has to be set against the risk of saturation. Meta-analysis also revealed profound cell type specific differences in the top target genes identified by GRIP in the four cell backgrounds, distinguishing these clearly from abundantly expressed ‘house-keeping’ genes. The observed integration site preference of gamma-retroviruses is clearly more selective than a simple preference for open chromatin, suggesting that GRIP will have utility alongside methods such as ATAC-Seq and FAIRE-Seq, where respectively, transposon tagging or cross-linking and fractionation are used to analyse open chromatin regions ,33. AnotMALAT1, with up to 0.17% of all gamma-retrovirus insertions mapping in or near this element. There is a bourgeoning literature on the role of MALAT1 over-expression in human cancer  or in progressing lymphomas analysed by deep sequencing [MALAT1 e.g. in human-mouse cancer xenografts adventitiously infected in vivo by murine gamma-retroviruses [However, not all GRIP targets were cell-type specific and notable exceptions included the long non-coding RNA iewed in ). Paradoby genes . It is uquencing , suggestoviruses .LMO2, BMI1, CCND2) in gene therapy trials indicates the translational relevance of these observations [The predilection of gRV integration for cancer genes is also relevant to viral biology. The ability to ‘seek out’ active chromatin ensures a favourable environment for proviral transcription. Moreover, by selectively targeting cancer drivers and growth effectors, an additional advantage may be gained by promoting the proliferation and/or survival of the infected cell. GRIP carried out early after infection allows the broad spectrum of preferred integration domains sites to be sampled, while continued cell passage would be expected to select for the most efficient mutagenic events. In MCF-7 cells we found no evidence for significant clonal selection due to proliferation or enhanced survival, as we observed no proviral orientation bias or increased copy number of insertions at the most highly targeted genes. However, we cannot exclude the possibility that insertions that have a strongly deleterious effect are counter-selected even in short-term culture. In this respect it is interesting to consider that gamma retroviral preference for cancer genes is operative in multiple cell types, while these viruses are prone to induce mainly haematopoietic malignancies in their natural hosts ,36 A likrvations ,38,39. TThe term super-enhancer was coined to denote a large cluster of enhancers that displays dense binding of master regulators and plays a role in tissue-specific cell identity . The claIt was therefore of great interest to compare the loci marked by GRIP with databases of super-enhancers identified in the same cell lines or cell types. While we found that super-enhancers and GRIP targets had similar levels of enrichment for cancer driver genes, there was relatively little overlap between the genes identified, and between GRIP and super-enhancer top hits generally. This is perhaps not surprising, as GRIP strongly targets TSSs as well as distal enhancers. However, there was also a significantly greater chance of marking the putative upstream regulators of GRIP targets by proviral insertion than for the upstream regulators predicted by the super-enhancer set. This finding implies that the datasets are non-redundant and it is conceivable that the ability of GRIP to target promoters as well as enhancers provides a distinctive and possibly unique window on the regulatory circuits in operation. We further hypothesise that the gamma-retroviral integration machinery recognises an as yet undefined chromatin state that is highly enriched for cell type-specific, cancer-relevant genes. As databases such as IPA grow in information content, it is likely that the utility of such analyses for the identification of the key drivers will increase.Overall, GRIP is a novel and potentially powerful technique that can add significantly to existing approaches to the elucidation of driver genes in human cancers.MCF-7 cells were a gift from K. Blyth  and cultured in MEM  containing 10% FCS , 100 μg/ml Penicillin/Streptomycin (15140 Gibco), and 2nM Glutamine (25030 Gibco). Additionally, 0.01mg/ml Insulin  and 1mM Sodium pyruvate (11360 Gibco) were used to supplement media. Cells were infected with 0.45μm-filtered supernatant harvested from sub-confluent FeLV-B-infected AH927 cells (feline fibroblasts) at a multiplicity of infection of 1. The medium was replaced after 2 hours of infection, and cells cultured for a further 14 days.DNA was extracted from cells using a DNeasy kit  according to the manufacturer’s instructions. Genomic DNA was sheared down to average size of 400bp using Covaris-S2 , and end repair performed using NEB end repair mix .GCTTGATCGAGCAGTTGACCCGGGAGATCTGtcttatAATTC) and Linker-2 (PO4-GAATTataagaCAGATCTCCC-NH2) were ligated with unique barcodes  onto the DNA. The ligation products were then amplified using the FeLV LTR Outer Primer (CAAGTCTTTGTTGAGACTTGAC) and the Linker Outer Primer (GCTTGATCGAGCAGTTGACC). Nested PCR was then performed with the FeLV-LTR Inner Primer (GTACCCGTGTACGAATAAAGC) and the Linker Inner Primer (AGTTGACCCGGGAGATCTG). LM-PCR products were then built into an Illumina library using the NEBNext library construction kit (New England Biolabs) and sequenced on an Illumina MiSeq platform  to generate pair end reads with length of 2 x 250bp.Double stranded DNA adaptors Linker-1 .Insertions were grouped by proximity to their nearest gene, using a 25kb window upstream and downstream of genes of interest, thus incorporating local regulatory elements. For each gene, enrichment of insertions in the window was calculated using a hypergeometric test. The mappable genome was estimated as the union of all gene windows. FDR correction was applied to address multiple testing . Refseq MCF7 insertions were uploaded as a .bed file into RStudio and then analysed using the Bioconductor package ‘ChippeakAnno’ for proximity to transcriptional start site (TSS) and other features as described . InsertiAssessment of orientation bias was performed as described  with thehttps://bioconductor.org/packages/release/bioc/html/AnnotationHub.html). In the case of H3K27ac, H3K9me3, H3K27me3 and H3K36me3 narrow peak files were all that was available. In the case of H3K4me3 both broadpeak and narrowpeak files were available, however in this case broadpeak annotation was used as the spread of peak widths was more similar to the narrowpeak widths of the other files. All peaks for all histone marks, as well as MCF7 insertion site data, were annotated using TSS.human.GRCh37, and gene IDs added using the ‘org.Hs.eg.db’ dataset. Annotated ChIP and MCF7 insertion data were then converted to GRanges objects and overlap assessed using the ‘makeVennDiagram’ function from the ChippeakAnno package. P values (from hypergeometric testing) and overlap counts were exported as separate dataframes. The same process was followed for K562 and HepG2 cells. In the case of CD34 primary cells, data for patient RO_01536 were obtained from the Human Epigenetic Roadmap repository, accessed via AnnotationHub, and the same analysis applied.All available histone modification data for MCF7 was imported into RStudio from the ENCODE ChIP repository using the AnnotationHub package from Bioconductor  or .fastq files (for H3K4me3). Preprocessing was performed using the Galaxy Platform. Where .fastq files were used as a starting point, data were screened using FastQC and then trimmed as appropriate using FastQTrimmer. Data were aligned to the hg19 assembly of the human genome using Bowtie2 to obtain .bam files. ChIP peaks were then called using MACS with the Model Fold (MF) parameter set to 10, and peaks annotated with gene names using the Galaxy/Cistrome platform with the peak2gene algorithm. The top 500 hits were selected by pscore; where this was tied, all peaks with that score were included and the difference in sample size accounted for statistically.6 simulations were done for H3K4me3, H3K9me3, H3K36me3 and H3K27me3. 60 x 106 simulations were done for H3K27ac, after which still no overlap of the size observed occurred by chance.Overlap between the top ChIP peaks and the top MCF7 insertion target sites was determined using custom R scripts. The likelihood of such overlap occurring by chance was then determined by Monte-Carlo simulation that randomly sampled the full Refseq gene list for a given sample size and then calculated the overlap with the top hits list. 1x10Expression data from MCF7 cells was obtained from Microarray analysis, and ranked according to average intensity in cells in the basal state. Custom R code was written to then take equal sized samples  of top GRIP targets and most highly expressed genes, and assess the level of overlap. The level expected by chance was determined using Monte-Carlo simulation as described above, and p values calculated.www.qiagen.com/Ingenuity). Pathway annotation and upstream regulator analysis were exported and summarised.Ontological analysis was performed on gene lists using commercially available Ingenuity Pathway Analysis (IPA) software  and the super-enhancer set (2 vs 16) was performed using Fisher’s exact test in order to obtain a p value.S1 Dataset(BED)Click here for additional data file.S1 FigInsertions are shown in purple at the top, with gene structure and then ChIP-seq signal tracks below.(TIF)Click here for additional data file.S1 TableGenes that are known cancer drivers are highlighted in green.(XLSX)Click here for additional data file.S2 TableShown are lists for the GRIP technique, and those from data found in the dbSuper superenhancer database.(TIF)Click here for additional data file.


In the vertebrate central nervous system, exploratory filopodia transiently form ondendritic branches to sample the neuronal environment and initiate new trans-neuronalcontacts. While much is known about the molecules that control filopodia extensionand subsequent maturation into functional synapses, the mechanisms that regulateinitiation of these dynamic, actin-rich structures have remained elusive. Here, wefind that filopodia initiation is suppressed by recruitment of ArhGAP44 toactin-patches that seed filopodia. Recruitment is mediated by binding of a membranecurvature-sensing ArhGAP44 N-BAR domain to plasma membrane sections that weredeformed inward by acto-myosin mediated contractile forces. A GAP domain in ArhGAP44triggers local Rac-GTP hydrolysis, thus reducing actin polymerization required forfilopodia formation. Additionally, ArhGAP44 expression increases during neuronaldevelopment, concurrent with a decrease in the rate of filopodia formation. Together,our data reveals a local auto-regulatory mechanism that limits initiation offilopodia via protein recruitment to nanoscale membrane deformations.DOI:http://dx.doi.org/10.7554/eLife.03116.001 Our brains contain a vast network of many billions of cells that communicate with,and are connected to, each other. Each brain cell, or neuron, can form connectionswith as many as 10,000 other neurons—and signals pass from one neuron to thenext at sites known as synapses.A neuron's surface has numerous finger-like protrusions known as filopodia that areimportant for sensing the environment around the cells. Filopodia are highlychangeable and constantly extend and retract as the filaments that supportthem—which are made up of a protein called actin—grow and shrink back.Neurons use their filopodia to explore and seek out other neurons in the brain, andwhen they make contact with the right neuron, it leads to the formation of a synapse.However, how filopodial extensions begin to grow—and what stops a neuron fromforming too many filopodia—is not fully understood.Galic et al. now show that a protein called ArhGAP44 limits the formation of newfilopodia in neurons. The ArhGAP44 protein is recruited to patches of the surfacemembrane that have a lot of actin and that curve inwards. ArhGAP44 then locallyinhibits other proteins that are normally required to extend the actin filaments anddrive the growth of filopodia out from the surface of the cell.Galic et al. also show that more ArhGAP44 is produced with age—levels are lowin embryos and high in adults—and this increase in the amount of proteincorrelates with a decrease in the number of filopodia formed. When Galic et al.engineered rat neurons to produce more of the ArhGAP44 protein, fewer filopodiaformed on the surface of the neurons. Decreasing the amount of this protein had theopposite effect. Moreover, ArhGAP44 was shown to mainly stop new filopodia fromforming and had little effect on existing filopodia. Together, these findings suggestthat ArhGAP44 may help neurons transition from a dynamic exploratory mode to amature, more static, state; this is a characteristic of the development of thenervous system.DOI:http://dx.doi.org/10.7554/eLife.03116.002 During the development of the central nervous system, neuronal progenitor cellsproliferate, migrate, and finally differentiate into functional units to form amulti-cellular neuronal network . In cultHere, we provide evidence that ArhGAP44 limits the initiation of exploratory dendriticfilopodia. Consistent with previous reports, we observe that formation of actin patchesprecedes filopodia extension. Notably, we find that within actin patches MyosinII-mediated pulling on plasma membrane (PM)-associated actin cables induces highlycurved membrane sections that trigger ArhGAP44 recruitment. The resulting enrichment ofArhGAP44 then reduces local actin polymerization due to the Rac GAP activity ofArhGAP44, preventing the formation of filopodia. ArhGAP44 expression increases as theneuronal network is established and the frequency of exploratory filopodia formation isdiminished, suggesting that ArhGAP44 may facilitate the transition of neurons from adynamic exploratory mode to a mature more static state, a hallmark of nervous systemdevelopment.Formation of filopodia depends on proteins that regulate polymerization of actinfilaments . To idenTo validate the microarray expression pattern of ArhGAP44, we first isolated variousorgans and brain regions and probed protein levels with an antibody directed againstArhGAP44. Consistent with previous work , westernWe then explored the expression of ArhGAP44 in the brain during development and foundArhGAP44 among the genes with the highest adult-to-fetal ratio .10.7554/As N-BAR domains present in ArhGAP44 and other proteins can bind to inward-curvedplasma membranes , weusedTo determine the function of ArhGAP44 in neurons, we transfected primary rathippocampal neurons. Compared to control cells, overexpression of ArhGAP44 caused asignificant reduction in the density of dendritic filopodia at DIV12 . To decrTo further validate the observed effect on filopodia density, neurons weretransfected with siRNA directed against ArhGAP44. Markedly, knockdown of ArhGAP44augmented the density of dendritic filopodia , yellow.As dendritic filopodia frequently extend, reorient, and collapse , filopodFor both, overexpression and knockdown of ArhGAP44, we further observed an increasein the number of transiently formed nodes along dendritic arbors . IntriguConsistent with previous reports , we findTo test this hypothesis, we performed a synthetic compensation experiment,co-expressing Rac1 with ArhGAP44(R291M). Although the resulting protrusions wereshorter than control filopodia, co-expression of ArhGAP44(R291M) together with Rac1reversed the observed decrease in the frequency of protrusion formation and nodeformation caused by expression of ArhGAP44(R291M) .Video 11To analyze the subcellular protein localization, we cultured hippocampal neurons andstained against endogenous ArhGAP44. We found ArhGAP44 to be absent from the nucleusand present in patches along dendrites . We thenTo investigate the molecular mechanism that caused ArhGAP44 patch formation, wecompared the subcellular localization of various deletion mutants of ArhGAP44 . ArhGAP4Next, we aimed to investigate what caused the convoluted membrane surface atdendritic nodes. Consistent with previous reports , ratio-iTo test the hypothesis that ArhGAP44 enrichment relies on acto-myosin-dependentpulling forces to the PM, we altered either actin integrity or myosin-dependent actincontraction. Notably, both treatments led to a significant reduction of ArhGAP44concentration in actin patches ,suggestFinally, we investigated whether ArhGAP44 is also enriched to other actin-richstructures in neurons. We find the N-BAR domain enriched at the end of dendrites limits initiation of exploratoryfilopodia. We show that ArhGAP44 can hydrolize the small GTPase Rac and Cdc42 . Thus, aCdc42 acts as an activator of Irsp53 , promotiTaken together, we propose that ArhGAP44 mediates a localized negative feedback thatbecomes upregulated as neurons mature to reduce the frequency with which neuronsinitiate new exploratory filopodia. Considering that acto-myosin initiated PMdeformation is a ubiquitous process and that a high number of curvature-sensing proteinsare known to modify actin dynamics, this suggests that the local feedback mechanism forArhGAP44 described in our study likely exemplifies a more general principle forreceptor-independent signaling whereby signal transduction is initiated by the transientrecruitment of regulatory proteins to actin- and force-dependent nanoscale PMindentations.biogps.org website  was downloaded from the  website  . In Tissue samples were isolated from female Wistar rat and suspended in ice-cold lysisbuffer containing 1% Tween and protease inhibitors . Each sample was homogenized and absolute protein concentration wasmeasured, using the BCA Protein Assay Kit ,and adjusted to equal levels for each sample. Next, 6× SDS was added, and thesamples were heated to 90°C for 5 min. Finally, the samples were vortexed andloaded on a gel. 20 µg total protein was loaded for each sample in Neurons were harvested at DIV3, DIV10, and DIV17 in ice-cold lysis buffer containing1% Tween and protease inhibitors . Absolute protein concentrationwas immediately measured using the BCA Protein Assay kit (Thermo 23225) and adjustedto equal levels for each time point. Relative protein levels were probed usingspecific antibodies directed against ArhGAP44 , thepostsynaptic marker PSD95 , the presynapticprotein Bassoon , and the loading control beta-tubulin .For detection, we used secondary antibodies from Invitrogen and SuperSignal WestFemto Maximum Sensitivity Substrate .4, washedbriefly with water and dehydrated in an ascending ethanol series  before critical point drying with liquid CO2in a Tousimis 815B . Samples were mounted on colloidalGraphite on 15-mm aluminum stubs  and sputter-coatedwith 70A of Au/Pd using a Denton Desk 11 Sputter Coater. Visualization of samples wasperformed with a Zeiss Sigma FESEM  operated at2–3 kV, working distance 4–6 mm and an in-lens SE detector under highvacuum conditions. Images were captured in TIFF format.Neurons were cultured on Poly-L-Lysine-coated glass coverslips and fixed using 2%Glutaraldehyde (8% stock-EM grade) and 4% p-Formaldehyde in NaCacodylate buffer pH7.4 for 10 min. Neurons were rinsed with 0.1 M NaCacodylate buffer (pH 7.4) afterprimary fixation and post-fixed for 1 hr with aqueous 1% OsONeurons were cultured on glass slides for various periods of time , fixed and prepared for SEM as described above. Using low resolution(1000× magnification), individual neurons were identified , a 514-nm argon laser , and a 594-nm solid-state laser ,respectively. Images were captured using an EMCCD camera , driven by Micromanager mounted on the side port of aninverted microscope .Primary cultured rat hippocampal neurons were transfected 7 days after plating withsiRNA directed against ArhGAP44 together with a fluorescent marker and fixed atDIV12. Fluorescently tagged ArhGAP44(R291M) was transfected at DIV11 and fixed atDIV12. For each condition, the sample was fixed and individual neurons were imaged.Filopodia density was measured manually, analyzing only the proximal 100 µm ofeach dendrite. For each condition tested, >20 cells were used.HeLa cells were grown in DMEM (high glucose) supplemented with 10% FCS and Pen/Strepuntil they reached 80% confluency and then transfected either with CFP-ArhGAP44(wt),CFP-ArhGAP44(R291M), or empty CFP plasmid (control) using LF2000 according to themanufacturer's protocol for 4 hr in DMEM in the absence of FCS and Pen/Strep. Cellswere then serum starved for 18 hr. For all conditions, live cell fluorescence 18 hrpost transfection showed transfection efficiency of >80%. Cells were stimulatedwith 50 ng/ml EGF for 5 min. Next, cells were scratched and protein levels weremeasured and adjusted for all samples to equal levels. Of each sample 900 µlwere used for pulldown and 100 µl for loading control. GTP levels were probedusing Rac1 Activation Assay Kit  according tothe manufacturer's protocol. In brief, GTP-bound Rac was eluted from cell lysatesusing PAK PBD agarose beads and detected by western blot using α-Rac1  antibody. Relative intensities were compared to loadingcontrols.The protocol used to synthesize siRNA has been previously reported . Specifihttp://fiji.sc/wiki/index.php/Temporal-Color_Code).Neurons were imaged for 10 min every 60 s using a 63× objective. Changes infilopodia dynamics were assessed manually using ImageJ. In detail, dynamicprotrusions  were counted and normalized tofilopodia that remained over the course of the acquisition .Dynamics was visualized using the Temporal Color-Code designed by Kota Miura(The software used for ratio-metric imaging has been previously described . In briePatch −POIBG)/(CytosolPatch −CytosolBG)]/[(POIDendrite −POIBG)/(CytosolDendrite − CytosolBG)].To measure the relative protein concentration at patches, average fluorescentintensities of the protein of interest (POI) and the cytosolic reference weremeasured in the patch and in the adjacent dendritic stretch. The background (BG) wasdetermined separately for both channels using the average of four sectors outside thecell adjacent to the region of interest. The relative intensity of POI’s wasdetermined as  Several key experimental questions need to beaddressed, however, to further strengthen this study.Reviewer #1: 1) How frequently do filopodia emerge from Nadrin2-positive patches? Arefilopodia more likely to emerge from these patches than from Nadrin-2 negativedendritic shaft areas? This information will allow to assess how important a playerNadrin2 is in the control of filopodia formation. If most emerging filopodia do notcontain Nadrin2, this protein is only relevant to control a subset of theseprotrusions.In the revised version we now investigate the origins of dendritic protrusions in moredetail. The following three experiments were added:We show that 83% ± 7% of all protrusions emerge from dendritic nodes .We show that 89% ± 6% of all protrusions emerge from actin-rich patches . Thisfu100% of dendritic actin patches show enrichment for ArhGAP44. The average enrichment ofArhGAP44 in dendritic actin-patches is 80% ± 8% over a cytosolic reference .Together, these added experiments argue that the majority of dendritic protrusions areemerging from dendritic nodes that have convoluted membrane invaginations and that showa relative increase for both, actin and ArhGAP44 concentration.2) Does overexpression or loss of Nadrin2 impact the number of dendritic spines?This is important to assess the biological role of Nadrin2-mediated filopodiaformation.To further explore the function of ArhGAP44 in aged neurons, we added the following twoexperiments to the revised manuscript:Full-length and the isolated N-BAR domain of ArhGAP44 both enrich in dendritic spines inneurons .Knockdown of ArhGAP44 in aged neurons (DIV17) increased the fraction of dynamicfilopodia-shaped vs. spine-shaped dendritic protrusions .We did not include this data in the initial submission, as we felt that it does notstrengthen the core findings of this manuscript that is focused on the initiation ofexploratory filopodia from dendrites.3) Can the authors show more directly that Nadrin2 is recruited to membranes ina curvature-dependent manner?Yes, we can. We have previously established an assay that relies on cone-shapednano-scale structures (nanocones) to deform the plasma membrane (PM) in live cells: When cuWe show that the isolated N-BAR domain of ArhGAP44 forms puncta selectively abovenanocone-induced membrane-deformation in the basal PM of primary hippocampal neurons.We show that enrichment of ArhGAP44 over nanocone-induced PM-deformations does notcorrelate with enrichment of the PM  or of acTogether, these experiments argue that nanocones locally deform the PM and that theresulting local high PM-curvature is sufficient for ArhGAP44 enrichment in neurons.4)only shows that Rac1 overexpression promotes the formation of transientfilopodia independent of Nadrin2. The statement that “Nadrin2 limitsRac-dependent de novo filopodia formation” is not supported by thesedata.We thank the referee for pointing out that the title of the figure legend is misleading.We have rephrased the figure title to: ´Knockdown of ArhGAP44 and overexpression ofRac1 both increase de novo filopodia formation´.5) Knock-down neurons should be stained for Nadrin2 to confirm antibodyspecificity in immunostaining.The following two experiments were added to confirm the antibody specificity:Overexpression control: Neurons transfected with ArhGAP44(R291M) for 24h, fixed andstained with an antibody directed against ArhGAP44 show a >8-fold increase influorescence intensity compared to non-transfected cells .Knockdown control: When stained with an antibody directed against ArhGAP44, neuronstransfected with siRNA directed against ArhGAP44 show reduced fluorescence intensitycompared to cells transfected with a control siRNA .6) The tissue distribution blot in Figure1 needs to be repeated for bettersignals.The blot shown in My major concerns are as follows:Reviewer #2: 1) The authors overuse the terms “transient” and“dynamic” to describe the Nadrin2 enrichment at patches. For example,“A major finding of our study is that transient recruitment of Nadrin2 toactin patches...”…“deletion of Nadrin2 curvature-sensitivityas well as limiting action-myosin dependent contractile forces  both preventeddynamic Nadrin2 recruitment.”… “both treatments led to asignificant reduction of Nadrin2 recruitment”In fact, the time course of recruitment is never analyzed in the paper. There isonly onethat shows any transience. No treatment was analyzed in a manner that would haveassessed the dynamic nature of the recruitment. Perhaps the decreased but remainingcolocalization is just as transient but weak, or more short-lived but just aspronounced. Regarding myosin, the ML7 results  seem to suggest only thatretention, not recruitment, of Nadrin2 are mediated by myosin II. It seems thatexamining the time course of accumulating Nadrin2 with respect to the time offilopodial extension would be required to evaluate the model. A more specific testwould be to compare the time course of active Rac1 in the patch with Nadrin2 levels.Similarly, the curvature aspect could be addressed in detail specifically bytimelapsing the N-BAR construct during extension.(A) Overuse of the terms ‘transient’ and ‘dynamic’. We havesubstantially reduced the use of both terms in the text, using them now only 2 times inthe whole manuscript to describe ArhGAP44.(B) Curvature-aspect of protein recruitment. In the revised version of the manuscript,we added a series of experiments to investigate the curvature-dependence of ArhGAP44enrichment :We show that the isolated N-BAR domain of ArhGAP44 forms puncta selectively abovenanocone-induced membrane-deformation in the basal PM of primary hippocampal neurons.We show that enrichment of ArhGAP44 over nanocone-induced PM-deformations does notcorrelate with enrichment of the plasma membrane  or of acThese experiments provide evidence that inward plasma membrane deformation is sufficientfor enrichment of ArhGAP44 to inward deformed plasma membranes in neurons.(C) Time course of ArhGAP44 enrichment. The following four experiments were added:ArhGAP44 is recruited to contracting structures: We added a synthetic approach that wedeveloped earlier, where we used a small molecule to rapidly recruit a Rac GEF to the PMand activate Rac in order to increase actin polymerization . In thisKinetic analysis of actin and ArhGAP44 during filopodia initiation. The followingexperiments were added: we find that 83% ± 7% of all protrusion emerge fromdendritic nodes , that 89Together, these added experiments further strengthen the statement that ArhGAP44 isrecruited to contracting actin patches within nodes that precede filopodiaelongation.(D) Retention vs. Recruitment. We propose that ArhGAP44 diffuses trough the cytosol byBrownian movement, and that enrichment of ArhGAP44 at nodes is caused by binding of theprotein to inward membrane deformations that transiently form at such sites due tomyosin-dependent contraction of membrane-associated actin cables. Binding of N-BARdomain proteins (such as ArhGAP44) has been shown to depend (i) on increasedelectrostatic interactions between negatively charged lipid head groups in curvedmembranes and positively charged amino acids of the banana-shaped protein dimer facingthe membrane, and (ii) the insertion of an amphipatic helix present in all N-BAR domainproteins into membrane-imperfections that predominantly occur in curved lipid bilayers. Enrichm2) The authors state “the transient localization of Nadrin2 to patchesbut not to extended filopodia argues that Nadrin2 limits initiation rather thanelongation of newly formed filopodia.”Why doesn't this argue equally well that recruitment of Nadrin2 initiatesoutgrowth? Loss of Nadrin in an elongating filopodium would seem to indicate that itmight in fact suppress elongation, not initiation. With the few examples availablefor viewing, initiation seems to occur before loss of Nadrin. Clearly, an additionalstep of mechanism would be required to explain the overexpression and knockdownphenotypes, but I think it is too facile to say the live imaging supports thismodel.We agree with the referee that it is a valid question whether ArhGAP44 functions more asan inhibitor of filopodia initiation vs. an inhibitor of filopodia elongation/outgrowth:We considered that an inhibitor of filopodia elongation/outgrowth would be expected tolocalize together with positive regulators of actin dynamics (e.g. MENA or IRSp53) atthe tip of extending filopodia where actin-polymerization in growing filopodia occurs.In contrast, an inhibitor of filopodia initiation would be expected to be present at the´birthplace´ of filopodia, and to dissociate once elongation/outgrowth begins.The localization of ArhGAP44 all across actin patches, which our data argues is causedby acto-myosin dependent inward curved plasma membrane deformation, suggests that itreduces Rac and Cdc42 activity globally across the patch which we think explains thelower frequency of filopodia extension. The dissociation from extending filopodia (withoutward membrane deformation) can best be explained by the change in curvature  in the extending membrane tubes. Based on the selectivity of N-BAR domainproteins to positively curved membranes, we therefore think that our data is consistentwith a role of ArhGAP44 in regulating filopodia initiation. In the revised version ofthe manuscript we discuss this now in the text.As an added note: the possibility that ArhGAP44 may have additional functions in nodes(e.g. inhibit filopodia initiation AND elongation/outgrowth) will be discussed in moredetail below in point 4.3) The authors state “Our experiments identified the small GTPase Rac asthe principal target of Nadrin2 within actin patches .” The indicated figure does not demonstrate thisat all.We agree. The statement has been replaced.4) The assumption is that Nadrin2 acts through Rac1 only, not Cdc42, but thealternative (interaction with Cdc42) is not tested. Can this really be dismissed outof hand?tests the relative levels of GTP-Rac1, but does not test whether Nadrin2 or itsmutant alter levels of other potential targets (e.g. Cdc42).A related but unstated presumption is that the dynamic assays ofpredict total protrusion number, and expressing Cdc42 would provide a test ofthat as effect of its overexpression is opposite to Rac1. Does Cdc42 overexpressionalter the dynamic measures in a way opposite to Rac1? Does it decrease the number ofnodes?This is an excellent question. Considering the dual specificity of ArhGAP44 to Rac1 andCdc42 (as we cite in the paper), ArhGAP44 also likely has an added function inregulating Cdc42 during filopodia initiation . In theArhGAP44 can hydrolyze GTP-Cdc42: Consistent with previous reports, we find hydrolysisof GFP-Cdc42 by ArhGAP44 .Overexpression of Rac1(wt) but not of Cdc42(wt) phenocopies protrusion dynamics uponknockdown of ArhGAP44: We have previously shown that knockdown of ArhGAP44 increasesdensity , yellow Overexpression of Rac1(wt) but not of Cdc42(wt) phenocopies protrusion morphology uponknockdown of ArhGAP44: In the revised version of the manuscript, we added raw materialshowing that overexpression of Rac1 but not of Cdc42 phenocopied the ArhGAP44 knockdowncausing the formation of dynamic dendritic nodes and filopodia , 6–10. ATogether, while these results argue for ArhGAP44-dependent regulation of Rac1 in actinpatches, we agree with the referee that a discussion about the function of Cdc42 hasbeen missing in the initial manuscript. Following discussion on the role of Cdc42 havebeen added to the text: Cdc42 acts as an activator of Irsp53 , promoti5) Presumably, dynamics were not measured following Nadrin2(wt) overexpressionbecause there are so few filopodia left, but shouldn't the Rac1+Nadrin2(wt)case be examined, since Nadrin2(wt) should suppress the Rac1 phenotype? That is theclaim in the model of, leftcolumn.Expression of ArhGAP44(wt) rapidly triggers varicosity formation and cell death , likely 6) Surprisingly, the movies do not make a great case for this paper, and in factin my mind raise more questions than they answer. Technically, the intensity levelsfluctuate substantially, making me wonder whether small or thin structures such asnascent filopodia or nodes could really be identified with confidence. One troublingexample is in the upper right panel, going from frame 1 to 2. The structure seeminglyof most interest is the node or protrusion in the middle of the process (under“n2”), which appears to pop into existence. But the entire imagebrightens in this panel, making it unclear whether to trust the relative intensity ofthe node. Related to that type of phenomenon, some of the fluctuation is apparentlydue to an inconsistent z span; e.g. in Movie 1, the overexpression of Nadrin2 panelclearly shows the middle one of the processes fade out and then return, as it wasinsufficiently spanned by the planes of the z sectioning. In the upper right panel,that particular node could just have been captured more completely in bounds of the zstack rather than appear or brighten in reality.In terms of observations, it doesn't seem as though the control example inmovie 1 demonstrates any protrusion formation at all. Am I missing something? Basedon the quantification in, thereshould be just as many forming and collapsing processes as there are processes thatpersist through the movie, but I don't see this at all. At the very least,I'd say that readers will need some help: formation, collapse, and unclassifiedchanges should probably be pointed out with markers of some sort.Raw data (movies) of the Rac1 and Rac1+Nadrin2(R291M) experiments would begood to see.More raw data (stills or movies) from the key experiments inis needed.(A) Added raw material: We agree with the reviewer that addition of more raw data wouldbe helpful to (i) explain what types of dendritic protrusions exist, and (ii) show howthese protrusions are affected by overexpression and knockdown of ArhGAP44 or the smallGTPases Rac1 and Cdc42. To better explain and illustrate the main findings of theanalysis shown in To illustrate different types of protrusions formed on dendrites, and better explain theanalysis used in Timelapse examples of static and dynamic protrusion types characterized in Raw data movie showing stable dendritic filopodia (Raw data showing formation of dynamic dendritic nodes .To illustrate the findings of Raw data movie showing increased dendritic node and reduced protrusion formation uponArhGAP44(R291M) overexpression .Raw data showing increased dendritic node and protrusion formation upon knockdown ofArhGAP44 .Raw data movie showing dendritic protrusion emerging from dendritic nodes upon knockdownof ArhGAP44 .To illustrate the findings of Raw data examples showing abnormal dendritic node and filopodia formation upon Rac1overexpression .Raw data examples showing abnormal dendritic filopodia formation upon Cdc42overexpression .Raw data movie showing synthetic rescue of ArhGAP44(R291M)-dependent reduction infilopodia formation by co-overexpression of Rac1(wt) .(B) Protrusions ‘pop into existence’? As the referee correctly state, weonly observe structures in the illuminated plane of the confocal microscope. This meansthat dynamic dendritic sections that move in z-direction during the 10 minute longacquisition period may cause filopodia to leave the confocal plane. To investigate thefrequency and origins of protrusions that ‘pop into existence, the followingexperiments were added:Timelapse analysis of filopodia formation using a shorter acquisition interval: As shownin Time series showing dendritic filopodia emerging from node . This exThese added experiments provide evidence that the majority of filopodia that ‘popinto existence’ are reflective of filopodia emerging from dendritic nodes thatwere to short-lived to be captured using a 60-second interval. We consider the remaining17% of filopodia that ‘pop into existence’ to be a mixure of (i) filopodiaemerging from nodes in less than 15 seconds (i.e. between frames), (ii) filopodiaemerging directly from the dendritic shaft, or, more likely, (iii) filopodia that appeardue to changes in the focal plane of the dendritic tree.7) In, theaccumulation of a set of SEM images into a numbered series of panels portrayed as“stages” is unnecessary and misleading-there is no evidence presentedhere or cited that the stages correspond to a growth sequence. There is a near-totallack of information about how these images were assessed or quantified, and whatconstituted a node or a protrusion or any other feature.The reviewer raised a valid point. As the sequence of electron micrographs that wasprevious shown in New chapter in the Material and methods section: Quantification of Protrusion Typesusing Scanning Electron Micrographs. Neurons were cultured on glass slides for variousperiods of time , fixed and prepared for SEM as described above.Using low resolution (1000x magnification), individual neurons were identified (


Runx genes, Runx1, Runx2 and Runx3, encode transcription factors that are essential for cell proliferation and differentiation in major developmental pathways such as haematopoiesis, skeletogenesis and neurogenesis and are frequently associated with diseases. We describe here the characterization of Runx gene family members from a cyclostome, the Japanese lamprey (Lethenteron japonicum). The Japanese lamprey contains three Runx genes, RunxA, RunxB, and RunxC. However, phylogenetic and synteny analyses suggest that they are not one-to-one orthologs of gnathostome Runx1, Runx2 and Runx3. The major protein domains and motifs found in gnathostome Runx proteins are highly conserved in the lamprey Runx proteins. Although all gnathostome Runx genes each contain two alternative promoters, P1  and P2 , only lamprey RunxB possesses the alternative promoters; lamprey RunxA and RunxC contain only P2 and P1 promoter, respectively. Furthermore, the three lamprey Runx genes give rise to fewer alternative isoforms than the three gnathostome Runx genes. The promoters of the lamprey Runx genes lack the tandem Runx-binding motifs that are highly conserved among the P1 promoters of gnathostome Runx1, Runx2 and Runx3 genes; instead these promoters contain dispersed single Runx-binding motifs. The 3′UTR of lamprey RunxB contains binding sites for miR-27 and miR-130b/301ab, which are conserved in mammalian Runx1 and Runx3, respectively. Overall, the Runx genes in lamprey seem to have experienced a different evolutionary trajectory from that of gnathostome Runx genes which are highly conserved all the way from cartilaginous fishes to mammals.The cyclostomes (jawless vertebrates), comprising lampreys and hagfishes, are the sister group of jawed vertebrates (gnathostomes) and are hence an important group for the study of vertebrate evolution. In mammals, three  Runt gene family members, RUNX1, RUNX2 and RUNX3 that contain an evolutionarily conserved 128 amino acid Runt domain responsible for DNA binding and heterodimerization with the β-subunit. The β-subunit comprises a single protein, RUNXβ  that does not directly interact with DNA but serves to allosterically enhance the DNA-binding activity of the α-subunit and regulate its turnover by protecting it from ubiquitin-proteasome-mediated degradation Branchiostoma floridae) and tunicates (Ciona and Oikopleura) possess a single Runx gene. By contrast, vertebrates such as cartilaginous fishes  and tetrapods contain three Runx genes  Runx2 gene (Runx2b) that resulted from an additional teleost-specific genome duplication event Tetraodon) possess three Runx genes like tetrapods and an additional fourth Runx gene called frRuntRunx gene that was lost in tetrapods and zebrafish Runx genes, Runxb is present as a single copy in vertebrates as well as in invertebrates analysed, with the exception of the fruit fly (Drosophila melanogaster) that possesses two orthologs of Runxβ genes, Brother and Big brotherThe polyomavirus enhancer-binding protein 2 (PEBP2) or core-binding factor (CBF) is an ancient Runt domain heterodimeric transcription factor of α and β subunits. In human, the α-subunit is encoded by three Runx genes play pivotal roles in several developmental processes including haematopoiesis, neurogenesis, and skeletogenesis. Runx1 is required for the emergence and maintenance of hematopoietic stem cells Runx2 is indispensable for osteogenesis as evidenced by the complete lack of ossified skeleton in Runx2-deficient mice RUNX2 results in the autosomal dominant bone disease, cleidocranial dysplasia Runx3 is involved in diverse biological pathways including the regulation of epithelial homeostasis in the gastrointestinal tract Runx3 is also essential for the differentiation of proprioceptive neurons In mammals, the three Runx genes.The living vertebrates are divided into two broad groups: the jawless vertebrates (cyclostomes) and jawed vertebrates (gnathostomes). While the gnathostomes are represented by cartilaginous fishes, ray-finned fishes, lobe-finned fishes and tetrapods, the cyclostomes are represented by only lampreys and hagfish which constitute a monophyletic group. Cyclostomes diverged from gnathostomes ∼500 Mya and are morphologically and physiologically quite distinct from gnathostomes. They possess a single median dorsal “nostril” as opposed to ventrally located nostrils in gnathostomes. In addition, they lack mineralized tissues, hinged jaws, paired appendages, pancreas and spleen Runx genes, designated MgRunxA and MgRunxB have been previously cloned in a cyclostome, the Atlantic hagfish (Myxine glutinosa). These Runx genes were found to be expressed in cartilaginous tissues, suggestive of their involvement in early vertebrate skeletogenesis Petromyzon marinus) led to the identification of partial sequences for only two Runx genes, PmRunxA and PmRunxB and the conclusion that this is likely to be the full complement of Runx genes in lamprey Lethenteron japonicum) was generated using DNA from the testis http://jlampreygenome.imcb.a-star.edu.sg/). The Japanese lamprey and sea lamprey are northern hemisphere lampreys that diverged from each other 30–10 Mya ago Runx genes, we mined the Japanese lamprey genome assembly for Runx genes and completed the coding sequences for Runx genes by RT-PCR and RACE. Our analyses show that the Japanese lamprey genome codes for three Runx genes like gnathostomes but they are not the exact one-to-one orthologs of gnathostome Runx1, Runx2 and Runx3.Full-length coding sequences of two The Japanese lamprey specimens were collected from commercial fishermen who routinely catch them like other commercial fishes in Ishikari River near Ebetsu in Hokkaido, Japan. The procedure for extraction of DNA and RNA from lamprey tissues was approved by the Institutional Animal Care and Use Committee of the Biological Resource Centre, Agency for Science, Technology and Research (A*STAR), Singapore.http://jlampreygenome.imcb.a-star.edu.sg/) was searched with human RUNX1, RUNX2, RUNX3 and RUNXβ protein sequences using the ‘TBLASTN’ algorithm. Scaffolds #47, #165, #769 and #850 containing sequences homologous to human and elephant shark Runx1, Runx2, Runx3 and Runxβ protein sequences were searched against the non-redundant protein database at NCBI using BlastX to identify the coding exons of lamprey Runx genes. Missing exons in the scaffolds (presumably due to gaps in the scaffolds) were identified by sequencing RACE (Rapid Amplification of cDNA Ends) products as described below.The genome assembly of the Japanese lamprey (Runx transcripts were obtained by 5′ RACE and/or 3′ RACE (primer sequences available upon request). 5′ and 3′ RACE were each performed in a nested PCR. The first round of PCR was performed with the Universal Primer and a gene-specific primer. The resulting PCR product was diluted 20× and 1 µl used for the nested PCR with the Nested Universal Primer and a second gene-specific primer. All RACE products were cloned into the pGEM-T Easy Vector , and sequenced completely using the BigDye Terminator Cycle Sequencing Kit  on an ABI 3730×l capillary sequencer . The genome structures of the Japanese lamprey Runx genes, including the exon-intron structures, UTRs and transcription start sites were deciphered by mapping the cloned full length cDNA sequences to the Japanese lamprey genomic sequence. Sequences for various isoforms of the Japanese lamprey Runx genes have been deposited in GenBank (KJ787775–KJ787788).Total RNA was extracted from various tissues of adult Japanese lamprey using Trizol reagent  according to manufacturer's protocol. One µg of total RNA was reverse transcribed into 5′RACE-ready or 3′RACE-ready single-strand cDNA by using the SMART RACE cDNA Amplification kit . Primers were designed for representative exons identified in the Japanese lamprey scaffolds and full-length Runx genes, including the two Runx sequences (RunxA and RunxB) from the Atlantic hagfish were retrieved from the National Center for Biotechnology Information (NCBI) database. RunxA and RunxB from the sea lamprey were not used as they are partial sequences. Multiple sequence alignments were generated using MUSCLE (www.ebi.ac.uk/Tools/msa/muscle/). For phylogenetic analysis, the alignments were trimmed using the Gblocks Server (ver. 0.91b) with less stringent selection parameters http://www.megasoftware.net/) was used to determine the most appropriate amino acid substitution model for each dataset. Maximum Likelihood (ML) and Bayesian (BI) methods were used for phylogenetic analyses employing the JTT+G+I or JTT+G model. MEGA 6.06 was used for ML analyses and 100 bootstrap replicates were used for node support. BI analyses were carried out using MrBayes 3.2 (http://mrbayes.csit.fsu.edu/). Two independent runs starting from different random trees were run for 1 million generations with sampling done every 100 generations. A consensus tree was built from all sampled trees excluding the first 2,500 trees (burn-in).The full-length protein sequences for human and other chordate Runx loci of human, chicken, zebrafish, sea anemone (Nematostella vectensis) and sponge (Amphimedon queenslandica) were obtained from Ensembl (www.ensembl.org); amphioxus (Branchiostoma floridae) from the UCSC Genome Browser (http://genome.ucsc.edu/); elephant shark from the elephant shark genome assembly http://esharkgenome.imcb.astar.edu.sg/) and Japanese lamprey from the Japanese lamprey genome assembly (http://jlampreygenome.imcb.a-star.edu.sg/).The order of genes in the Runx genes were normalized using beta-actin gene as the reference. Quantification of gene expression levels was performed using the comparative CT method Runx gene in different tissues were estimated in relation to a reference tissue . Note, however, that these relative expression levels are not comparable between different Runx genes.Purified total RNA was reverse-transcribed into cDNA with Superscript II . The single strand cDNA was used as a template in qRT-PCR reactions with KAPA SYBR FAST qPCR Kit reagents . Sequences of primers used in qRT-PCR are given in Runx genes in the Japanese lamprey, we BLAST-searched the Japanese lamprey genome assembly Runx genes. The missing exons and full-length coding sequences of these genes were identified by RT-PCR and/or RACE using cDNA from gill, intestine or skin  that occurred early during the evolution of vertebrates Runx1, Runx2 and Runx3 gene loci. For example, synteny of paralogs of Runx, Clic and Rcan are conserved in the Runx1, Runx2 and Runx3 loci of all gnathostomes analysed so far  are conserved in the Runx2 locus of chicken, zebrafish and elephant shark indicating that the Runx gene in this locus of these gnathostomes is indeed an ortholog of human RUNX2  and demosponge (Amphimedon queenslandica), the Supt3h gene is located next to the single Runt gene  distinct from those containing Runx genes. Either this gene previously located next to a Runx gene was translocated to a new location in the lamprey genome or the Runx gene linked to it has been lost. Of the three Japanese lamprey Runx genes, the most extensive synteny information is available for RunxB. The syntenic genes of the lamprey RunxB include three genes  whose orthologs are conserved in the Runx loci of gnathostomes. However, while the ortholog of Son and Donson are found in the gnathostome Runx1 locus, the ortholog of Cdc5l is found linked to gnathostome Runx2. The synteny of genes in the lamprey RunxB locus therefore suggests that RunxB is not an exact ortholog of either Runx1 or Runx2 of gnathostomes. This provides further support to the inference of the phylogenetic analysis that the three lamprey Runx genes are not one-to-one orthologs of the three gnathostome Runx genes.In order to verify the orthology relationships of the three lamprey Runx genes is largely similar except for Runx3 which characteristically lacks an exon (exon 5.1) at the C-terminal end, that is present in both Runx1 and Runx2  that is not equivalent to any of the gnathostome exons including exon 4.1 in elephant shark Runx1  and P2  that are separated by a characteristically large intron  promoter. Our efforts to identify a distal promoter by 5′RACE using cDNA from several tissues failed to yield transcripts originating from a distal promoter and led us to conclude that the P1 promoter is absent in RunxA  as well as by alternative splicing of exons  at similar locations in the 5′UTRs Runx genes Runx genes suggests that they were present in their single ancestral Runx gene. In contrast to gnathostome Runx genes, the P1 promoters of the lamprey RunxB and RunxC genes lack these characteristic tandem Runx binding sites. Instead, the P1 promoter of RunxB contains dispersed single Runx binding sites at positions −506, −282 and +131. In addition, its P2 promoter contains one Runx binding site at position −328 (data not shown). However, no such single Runx binding site is found in the P1 promoter of RunxC or in the P2 promoter of RunxA. It remains to be seen if these dispersed Runx motifs in the lamprey RunxB promoters are involved in its auto-regulation and/or in its cross-regulation by RunxA and RunxC.The P1 and P2 promoter regions of gnathostome Runx1–3 genes is post-transcriptionally regulated by miRNAs that target highly conserved miRNA binding sites in the 3′UTR of their transcripts Runx genes are also regulated by miRNAs, we searched the 3′UTRs of the lamprey Runx genes for seed miRNA binding sites that are well characterized in the mammalian Runx genes. While no such binding sites were found in RunxA and RunxC, RunxB was found to contain binding sites for miR-27 and miR-130b/301ab  and inhibitory domain (ID) located at their carboxy termini. In addition, gnathostome Runx proteins contain a nuclear localization signal (NLS) located contiguous to the Runt domain that mediates their nuclear import as well as a nuclear matrix targeting signal (NMTS) in the TAD that directs Runx transcription factors to specific nuclear-matrix associated sites involved in the regulation of gene expression. Other distinctive features of gnathostome Runx proteins include the PY and VWPRY motifs that mediate the transcriptional activity of Runx proteins by recruiting different interacting partners. The proline-rich PY motif in the TAD mediates the binding of Runx proteins to WW domain-containing proteins, such as Yes-associated protein (YAP), transcriptional co-activator with PDZ-binding motif (TAZ), and Smurf Ciona. The biological significance of these extra residues is unknown. Additionally, lamprey RunxB contains a unique stretch of 47 amino acids within the NLS domain of RunxB (RunxB (Runx1 (MVFLW), Runx2 (MRPIV) and Runx3 (MHIPV) genes of teleost fishes such as fugu and zebrafish All of these domains and motifs are conserved in the Japanese lamprey RunxA, B and C proteins . All thrB  Runx in the developing gut.We investigated the expression patterns of  lamprey . In the RunxB and RunxC in the skin of the lamprey  and Ciona intestinalis (urochordate) contain a single Runx gene, known as Runt genes. The functions of these genes are not well characterized, although reports documenting Runx expression in the developing gut, pharyngeal endoderm and regenerating oral cirral skeletal cells of the amphioxus Invertebrate chordates such as the amphioxus, Runx genes in the lamprey and their comparative analysis with Runx genes known in gnathostomes, we propose that the stem vertebrate ancestor contained a single Runx gene. As part of the two rounds of whole genome duplications, this gene locus underwent duplications , we infer that the fourth paralog was lost in the common ancestor of gnathostomes. The remaining three Runx genes in the gnathostome ancestor acquired specialized and distinct functions such that Runx1 is involved mainly in hematopoiesis Runx2 in skeletogenesis Runx3 in neurodevelopment and immunity Based on the identification of the three ications  in the lHox clusters in the Japanese lamprey and the relative age of paralogous genes in the lamprey genome as measured by the transversion rate of four-fold degenerate sites Runx genes in the lamprey lineage. Since only three Runx genes are present in the Japanese lamprey, we infer that five of the eight Runx genes have been lost Click here for additional data file.Figure S1Exon-intron organization of elephant shark and lamprey Runx genes. Schematic representation of the gene structures of elephant shark Runx1, Runx2 and Runx3 and lamprey RunxA, RunxB and RunxC. Exons are indicated by boxes. Exons constituting the Runt domain are indicated in grey. The two alternative promoters are denoted as P1 and P2. Crosshatched boxes indicate 5′- and 3′-UTRs. The asterisk (*) indicates an exon in LjRunxB that is absent in mammals and different from exon 4.1 in elephant shark. Not drawn to scale.(PDF)Click here for additional data file.Figure S2Phylogenetic analysis of chordate Runx sequences (Bayesian Inference). Protein sequences of Japanese lamprey Runx genes were aligned with homologous sequences from selected chordates. A Bayesian inference (BI) tree was generated for the alignment. Statistical support values for the nodes are shown as Bayesian posterior probability values. Hagfish and Japanese lamprey Runx proteins are highlighted in red. Lancelet (Branchiostoma floridae) Runt (BfRunt) was used as the outgroup. Hs, Homo sapiens; Gg, Gallus gallus; Dr, Danio rerio; Cm, Callorhinchus milii; Sc, Scyliorhinus canicula; Mg, Myxine glutinosa; Lj, Lethenteron japonicum.(PDF)Click here for additional data file.Figure S3Runx protein sequence alignment used for phylogenetic tree in  and  Alignment obtained after trimming the gaps using the Gblocks Server (ver. 0.91b). Hs, Homo sapiens; Gg, Gallus gallus; Dr, Danio rerio; Cm, Callorhinchus milii; Sc, Scyliorhinus canicula; Mg, Myxine glutinosa; Lj, Lethenteron japonicum; Bf, Branchiostoma floridae.(PDF)Click here for additional data file.Figure S4Runx protein sequence alignment used for phylogenetic tree in  Alignment obtained after trimming the gaps using the Gblocks Server (ver. 0.91b). Hs, Homo sapiens; Gg, Gallus gallus; Dr, Danio rerio; Cm, Callorhinchus milii; Sc, Scyliorhinus canicula; Mg, Myxine glutinosa; Lj, Lethenteron japonicum; Ci, Ciona intestinalis; Bf, Branchiostoma floridae; Nv, Nematostella vectensis.(PDF)Click here for additional data file.Figure S5Phylogenetic analysis of chordate Runx sequences (including CiRunt and NvRunx). Protein sequences of Japanese lamprey Runx genes were aligned with homologous sequences from selected chordates. A Maximum Likelihood (ML) tree employing the JTT+G model was generated for the alignment. Sea anemone (Nematostella vectensis) Runx (NvRunx) was used as the outgroup. Hs, Homo sapiens; Gg, Gallus gallus; Dr, Danio rerio; Cm, Callorhinchus milii; Sc, Scyliorhinus canicula; Mg, Myxine glutinosa; Lj, Lethenteron japonicum; Bf, Branchiostoma floridae; Ci, Ciona intestinalis.(PDF)Click here for additional data file.


COMT) gene  and analyzed drug specific and general side effects before and after immunosuppressive medication and subsequent placebo intake in 62 healthy male subjects. Significantly more drug-specific as well as general side effects were reported from homozygous carriers of the Val158 variant during medication as well as placebo treatment compared to the other genotype groups. Val158/Val158 carriers also had significantly higher scores in the somatosensory amplification scale (SSAS) and the BMQ (beliefs about medicine questionnaire). Together these data demonstrate potential genetic and psychological variables predicting nocebo responses after drug and placebo intake, which might be utilized to minimize nocebo effects in clinical trials and medical practice.A large number of unwanted adverse events and symptoms reported by patients in clinical trials are not caused by the drug provided, since most of adverse events also occur in corresponding placebo groups. These nocebo effects also play a major role in drug discontinuation in clinical practice, negatively affecting treatment efficacy as well as patient adherence and compliance. Experimental and clinical data document a large interindividual variability in nocebo responses, however, data on psychological, biological or genetic predictors of nocebo responses are lacking. Thus, with an established paradigm of behaviorally conditioned immunosuppressive effects we analyzed possible genetic predictors for nocebo responses. We focused on the genetic polymorphisms in the catechol-O-methyltransferase ( The term nocebo was created in analogy to the term placebo, and refers to the development of negative effects that are attributed to medication, albeit the drug itself does not explain the provocation of these symptoms Nocebo responses, similar to placebo responses, are mediated through specific and interrelated mechanisms across different medical conditions and physiological systems COMT) was associated with increased placebo responses in patients with irritable bowel syndrome MAOA) and the COMT  predicted reduced placebo responses in depression Genetic variation had been identified that might predict placebo responses. The Met allele of a genetic polymorphism  in the catechol-o-methyltransferase gene  since it has been investigated most extensively COMT Val158Met polymorphism has never been investigated before.In a model of learned immunosuppressive placebo effects in healthy humans, we were able to identify biological and psychological predictor variables for the learned inhibition in cytokine release COMT genotypes were analyzed with respect to their experienced side effects.The presented results are part of a study program on behavioral conditioning of immune functions The study was approved by the local ethics committee for human investigations of the University Hospital Essen and follows the rules stated in the Declaration of Helsinki. All participants gave written informed consent and were reimbursed for their participation.As part of a larger study program on behavioral conditioning of immune functions In the established taste-immune conditioning paradigm in humans, the immunosuppressive drug cyclosporine A (CsA) (unconditioned stimulus/US) is paired with a gustatory stimulus (conditioned stimulus/CS) during acquisition. Mere re-exposition to the CS during evocation is mimicking the immunopharmacological properties of CsA, reflected by impaired Th1 cytokine production and decreased T cell proliferation On experimental days 1 (6 pm), 2 (8 am and 6 pm) and 3 (8 am) during the first week of medication intake, all subjects in all three groups received four oral doses of 2.5 mg/kg body weight of the immunosuppressive drug CsA  in capsule form. In addition, subjects in groups 1 (n = 24) and 2 (n = 26) received a green-colored novel-tasting drink (150 ml strawberry milk aromatized with lavender oil) (CS) with each capsule (CsA) intake whereas subjects in group 3 (n = 12) were not exposed to the CS (drink) . A pauseThe subtherapeutic dose of CsA was previously determined, in which 0.25 mg/kg CsA (10% of the dose used as an US) did not significantly affect functional immunological parameters such as interleukin 2 (IL-2) secretion and mRNA expression or anti-CD3 stimulated PBMCs cytokine production Pre I). A general medical history, traits such as beliefs about medicines (Beliefs about Medicines Questionnaire_extended version/BMQ) Medication and “Placebo” intake to rate CsA-specific side effects on a five-point Lickert scale   .Pre I). The GASE asks for the most frequent side effects in clinical trials according to FDA statistics and it also allows to assess the attribution of symptoms to a specific drug. Subsequently, only those symptoms that were attributed to the alleged drug were measured before the first intake of placebo capsules (Pre II), as well as after fourteen intakes of capsules containing no pharmacological agents (placebo) or a subtherapeutic dose of CsA (0.25 mg/kg) respectively, to analyze possible unwanted side effects, which were ascribed to the “Placebo” treatment (Post II).Before the start of the study, subjects completed the Generic Assessment of Side Effects questionnaire (GASE) capsules ; Pre II,reatment ; Post IIPre I) and after (Post I) Medication intake (acquisition phase) and before (Pre I) and after (Post I) “Placebo” intake (evocation phase), respectively.Cardiovascular parameters heart rate and systolic and diastolic blood pressure were analyzed before , according to the manufacturer's instructions.6 cells/ml in cell culture medium .In order to determine the effects of CsA, peripheral blood mononuclear cells (PBMCs) were isolated by density gradient centrifugation . Cells were washed with Hanks' Balanced Salt Solution , counted with an automated hematology analyser  and adjusted to 5×106 cells/ml) were transferred to 96-well flat bottom tissue culture plates and were stimulated with 20 ng/ml of soluble mouse anti-human CD3 monoclonal antibody  for 24 h . Concentration of IL-2 in culture supernatants was quantified using a commercial ELISA  according to the manufacturer's instructions.PBMC suspensions  according to the manufacturer's protocol. Genotyping was performed on a 7500 Fast Real-Time PCR System using the TaqMan SNP Genotyping assay for rs4680 (C_25746809_50) and the TaqMan genotyping master mix  following the manufacturer's instructions. Allelic discrimination analysis was performed with the SDS version 1.4 software .p<0.05.All data are expressed as mean ±SEM. Immunological, neuroendocrine, cardiovascular parameters, as well as CsA levels in whole blood were analyzed using repeated-measures analysis of variance (ANOVA). Psychological characteristics as well as behavioral parameters were compared with univariate analysis of variances (ANOVA) followed by Bonferroni post-hoc tests. Pearson correlations were measured, using PASW statistics version 18 . Allele and genotype distributions did not deviate from Hardy-Weinberg equilibrium. To analyze association of all variants, Fisher's exact test  was calculated with PLINK Medication intake (Post I). Moreover, during “Placebo” treatment, no significant group differences were observed, neither in reported general side effects ; nor CsA-specific side effects   or immunological parameters analyzed here  and 2. In intake , Post I.6, n.s.) .COMT Val158Met polymorphism. Subjects of the three treatment groups were evenly distributed across the three genotype groups. Subsequent analyses revealed significant differences between allele carriers of the COMT Val158Met polymorphism and their experienced side effects. When analyzing general psychological and medical indispositions with the GASE questionnaire before study entry , Val158/Val158 homozygous carriers reported significantly more general psychological and medical complaints compared to the Met158/Met158 and Val158/Met158 groups   compared to both other genotypes  , and homozygotes for the Met158 allele. Genotyping revealed genotype frequencies of 30.7% , 54.8%  and 14.5%  for the  p<0.01) . In paraenotypes . The ide p<0.02) .Medication intake, ANOVAs showed significant differences for CsA-specific side effects  with significant more side effects reported in Val158/Val158 compared to the Val158/Met158 (p<0.001) and Met158/Met158 groups (p<0.05)  with the most pronounced side effects reported in the Val/Val homozygous carriers compared to the Val158/Met158 (p<0.001) and the Met158 homozygous (p<0.01) groups  . Similar) groups .Univariate ANOVAs showed no significant differences between the three genotypes in sociodemographic variables or trait anxiety . In contmedication intake condition. CsA concentrations were significantly increased in all three genotype subgroups without significant differences between groups , CsA or IL-2 levels analyzed during the treatment conditions (Medication intake) and “Placebo” treatment is not due to distinct physiological responses to the treatments.CsA levels were determined two hours after the last of four CsA intakes  and after the n groups . After tnditions . Thus, tCOMT) Val158Met polymorphism and analyzed an association of reported drug-specific and general side effects during medication and placebo intake of the COMT genotypes. Our results demonstrate significantly more drug-specific as well as general side effects in homozygous Val158 carriers during medication and placebo treatment compared to the other genotypes. These differences in nocebo responses were not due to treatment specific changes in psychological (anxiety) or biological  parameters since allele carriers did not differ in these variables. In addition, homozygous Val158/Val158 carriers displayed significantly higher scores in the somatosensory amplification scale (SSAS) and the BMQ (beliefs about medicine questionnaire) compared to the other genotype groups.Clinical and experimental data on psychological, biological or genetic variables predicting placebo responses are rare; data on genetic variables predicting nocebo responses are lacking. With an established model of learned immunosuppression we focused on the catechol-O-methyltransferase . Additionally, there were no group differences in cardiovascular parameters which could have been indicators of an increased stress reaction to the experimental procedure or medication intake. Moreover, CsA levels in whole blood as well as IL-2 concentrations in culture supernatant, as an indicator of the immunosuppressive effect of CsA, were monitored with no difference between the three genotype groups which could have explained the increased CsA-specific and general side effects.The ability to analyze intra-individual nocebo responses after intake of an immunosuppressive medication during the acquisition phase of the conditioning procedure as well as after placebo intake during the evocation phase is certainly a unique advantage of the model employed here. However, there are also a number of limitations within this study. Firstly, our findings are limited due to the small number of volunteers included in the genetic analyses, which only included young and healthy males and have to be thus interpreted with caution. The sample size is not sufficient to detect small sized effects, which may bear the risk of type 2 errors, i.e., the risk to miss existing group differences due to low statistical power. Future studies in larger samples should consider multiple factor statistical models of potentially moderating or even mediating effects of sociodemographic or additional psychological or genetic variables. Secondly, the information material and systematic procedure of analyzing adverse side effects could have let to an increased number of reported side effects, compared to a “free recall” procedure, as the documentation itself is affecting the occurrence of side effects In summary, the identification of psychobiological and/or genetic predictor variables in order to minimize nocebo effects is of essential relevance for clinical practice and trials. Future studies have to confirm the reported personality and genetic predictor variables for nocebo responses for different drugs, different physiological systems and end organ functioning. If nocebo responders could be conveniently classified by genetic or psychological predictor variables, these individuals could receive a “personalized treatment”, such as the usage of a “contextualized informed consent” Table S1CsA serum levels and IL-2 protein concentrations during the Medication and “Placebo” condition of the COMT genotype groups. CsA treatment during Medication significantly increased CsA serum levels and significantly suppressed IL-2 protein concentrations after anti-CD3 stimulation in all COMT genotype groups. During the “Placebo” condition, treatment with subtherapeutical doses of CsA slightly increased CsA levels in Val158/Val158, Val158/Met158 as well as Met158/Met158 allel carriers, however did not significantly affect IL-2 concentrations in these groups.  (n.d. =  not detectable). Data are shown as mean ± SEM.(DOCX)Click here for additional data file.


FH2613 with BW and BC, marker FH2097withBL and BC, marker ZUBECA6 with BW and BC, whereas marker REN75M10 was associated with BL and TL. The strongest LD (r2 ranged from 0.15 to 0.33) was estimated between nine loci with significant effect on economically important traits .Polymorphism of 30 canine-derived microsatellites was studied in a group of 200 red foxes kept on 2 Polish farms. 22 out of 30 microsatellites were selected to study association between marker genotypes and body weight (BW), body length (BL), body circumference (BC), tail length (TL), ear height (EH), length of the right front limb (FRLL), length of the right rear limb (RRLL), length of the right front foot (FRFL) and length of the right rear foot (RRFL). A total of 112 alleles and 243 genotypes were found at 22 autosomal microsatellite loci. Three monomorphic loci deemed as uninformative were excluded from the study. The association between marker genotypes and the studied traits was analysed using general linear model (GLM) procedure and least squares means (LSM). Linkage disequilibrium (LD) was estimated to assess non-random association between microsatellite loci. Out of 19 microsatellites studied four markers showed no association with the studied traits, three markers had a significant effect on one trait, and another three markers had significant effect on two traits. Among ten microsatellites with significant effect on four economically important traits  four were associated with two characters: marker  Vulpes vulpes) had a common ancestor with the domestic dog (Canis lupus familiaris) about 12–15 million years ago , which combines traditional genetics and molecular biology would become a valuable tool in selecting foxes for traits of interest, such as body size, fur quality, fertility, tame behaviour or disease resistance. The information from the DNA testing, combined with the observed performance records for foxes could improve the accuracy of selection and increase the possibility of identifying individuals carrying desirable traits at an earlier stage of breeding.Canidae, including an assembled genome sequence  kept on two farms  located in a region of the Leszno city (West Poland) were studied. To ensure unrelatedness of foxes the pedigree of each animal was carefully checked. Only unrelated animals originating from different litters (one fox from a litter produced by unrelated parents) were sampled for the study. The analysed traits were: body weight (BW), body length , body circumference , tail length (TL), ear height (EH), length of the right front limb (FRLL), length of the right rear limb (RRLL), length of the right front foot (FRFL) and length of the right rear foot (RRFL). The traits measurements were taken at the end of the farm season (December) after technological slaughter of animals.post mortem using a razor blade. The DNA was isolated in accordance to ARK Genomic self-protocol.Tissue samples for DNA extraction were taken from tongues of the studied animals. Thin slices (approximately 1–2 mm) of tongues were taken Thirty microsatellite markers described and localized in the dog genome were chosen for the study Table . The mulijklyis the studied phenotypeμis the overall meaniFis the ith farm effectjSis the jth sex effectkGis the kth microsatellite marker genotype effectijkleijklth phenotype, N.is the random error associated with yThe association between microsatellite genotypes and the studied traits was analysed using general linear model (GLM) procedure and the following linear model:The least squares means (LSM) for the studied traits in relation to microsatellite genotype were computed using ‘lsmeans’ procedure implemented in the R package. LSM were estimated only for genotypes with the frequency higher than five. Multiple comparisons of LSM were performed with the Bonferroni-Dunn test. The studied microsatellite loci were tested separately (one at a time) and the significant differences between microsatellite genotypes for each locus were indicated.2) computed using MIDAS  was estimated. The squared coefficient of correlation  were observed in all studied silver foxes, while markers FH3970 and FH3771 were shared by the lowest number of foxes . A total of 112 alleles and 243 genotypes were found at 22 autosomal microsatellite loci. The number of alleles found in the studied loci ranged from one  to 12 (locus ZUBECA6) with an average of 5.09 per locus. The three monomorphic loci  deemed as uninformative were excluded from further study.Table FH3824 (0.758) followed by loci REN75M10, REN75M10 and REN258F18 . The lowest heterozygosity was found at loci REN64E19 and FH2613 . An average heterozygosity per locus was estimated at 0.485. Monomorphic loci as mentioned above were not considered for further study.The highest heterozygosity Table  was founet al. 2000), researchers started to search for the linkage of the canine-derived microsatellites in the red fox and arctic fox genomes  to 150 . Out of 19 microsatellites studied four markers  showed no association with the studied traits, three markers had a significant effect on one trait , and another three markers had significant effect on two traits . A significant effect on the highest number of traits  was estimated for marker FH2097, whereas markers REN75M10 and FH3970 were significantly associated with four traits .Least squares means and their standard errors of the silver fox morphometrics and body weight for different genotypes of ten microsatellites with significant effect on four economically important traits  are presented in Table To our knowledge association between the polymorphism of canine-derived microsatellites and traits under selection pressure in red fox farming has not yet been studied. In the fox breeding the most important traits are those of economic importance. These are usually litter size, pelt size and pelt quality characters. According to Peura et al.  who carrFH2613, FH3771, REN64E19, REN88H03 and ZUBECA6), while the remaining three traits are significantly affected by three markers: FH2097, REN135K06 and REN75M10 are associated with BL; FH2097, FH2613 and ZUBECA6 are associated with BC; FH3970, REN30J23 and REN75M10 are associated with TL. Out of the ten microsatellites with significant effect on the four economically important traits, four were associated with two characters: marker FH2613 was associated with BW and BC, marker FH2097 was associated with BL and BC, marker ZUBECA6 was associated with BW and BC, whereas marker REN75M10 was associated with BL and TL. Thus, in further studies on implementation of MAS in red fox breeding efforts should be focused on the set of ten microsatellite loci significantly associated with traits of breeders’ interest.The analysis of association between the studied microsatellites and four economically important traits Table  indicateP < 0.01) with body weight.In recent years most of the association studies carried out in red foxes have mainly focused on searching for genetic markers related to body weight. Skorczyk et al. , who conMC4R), in relation to body weight of the red fox was studied by Nowacka-Woszuk et al.  and the arctic fox (Vulpes lagopus)  and the fat mass and obesity associated gene (FTO), in four species belonging to the family Canidae  were studied by Grzes et al. (FTO gene (−28T>C in the 5′-flanking region) and one in the INSIG2 (10175C>T in intron 2), were used for the association studies in the red fox. The evidence was observed for their association with body weight  and weight of raw skin . These associations indicate that both genes are potential candidates for growth or adipose tissue accumulation in the red fox.The polymorphisms of two candidate genes, the insulin induced gene 2 . The strongest LD was found between ZUBECA6 and FH3771 (r2 = 0.33), followed by ZUBECA6 and FH3970 (r2 = 0.25), FH3771 and FH3790 (r2 = 0.25), FH3824 and REN75M10 (r2 = 0.20). The FH2097 microsatellite was strongly linked with three loci: REN210I14 (r2 = 0.16), REN258F18 (r2 = 0.16) and REN64E19 (r2 = 0.15). The pattern of LD found between the studied microsatellites may indicate that many decades of artificial selection towards genetic improvement of economically important traits in the red fox, favoured those marker loci which were tightly associated with preferred phenotypes. This strong footprint of selection in regions of the genome that are important for controlling important traits, opens up opportunities to successfully utilize LD-markers in MAS, which may markedly improve genetic progress in red fox farming.Figure A similar conclusion was drawn by Boyko et al.  who geneHTR1B), body weight (e.g. GHR), formation of hair structure and fur coat (e.g. KRT17).A closer look at the canine chromosomal regions where the studied microsatellite markers are located reveals a number of genes positioned in close distance to the markers  can be considered less important in selective breeding of foxes. These traits do not directly and significantly influence efficiency of fur production, thus the genetic improvement of these traits is not included in the breeding goal in fur farming (Wierzbicki et al. The present study confirms that canine-derived microsatellites can be successfully applied in red fox breeding. MAS using a set of selected LD-markers with significant effects on economically important traits may increase genetic progress, and thus profitability of fur farming. Further studies, including the candidate gene approach are needed to fully exploit possibilities of MAS in shaping the genetic structure of the farm red fox populations.