The Scar/WAVE family of proteins mediates signals to actin assembly by direct activation of the Arp2/3 complex. These proteins have been characterised as major regulators of lamellipodia formation downstream of Rac activation and as members of large protein complexes.We have investigated the interactions of the three human Scar/WAVE isoforms with several previously described binding partners for Scar/WAVE 1 or 2. We find that all three Scar/WAVE isoforms behave similarly and are likely to participate in the same kinds of protein complexes that regulate actin assembly.Differences between Scar/WAVE proteins are therefore likely to be at the level of tissue distribution or subtle differences in the affinity for specific binding partners. The mrotein]) ,8. Homolrotein]) -11. N-WAin (SHD) .in vitro, but have been postulated to be regulated by the small GTPase Rac in vivo [Regulation of WASP-family proteins involves many interactions and is still the subject of intensive research. WASP and N-WASP are found in an inhibitory complex with WIP/CR16 proteins and can be activated by the small GTPase Cdc42 and a co-activating protein TOCA-1, recently described by Ho et al. . Phospho in vivo ,18. In 2 in vivo . They prSome of the members of this so-called "Scar/WAVE complex" had been previously studied by other groups and implicated in signalling. SRA1  is a Rain vitro actin polymerisation assays [Since Eden et al., a similar complex has also been isolated in association with Scar/WAVE2 ,31. Thisn assays ,31,34,35Drosophila Scar/WAVE protein severely affects the ability of cultured cells to produce lamellipodia, ruffles and filopodia [Drosophila cells deficient in the NckAP1 homologue, Kette, reveal a lack of actin based protrusions in the absence of functional protein [Drosophila Abi1 and SRA1 also prevents the formation lamellipodia in tissue culture cells [Dictyostelium knock out of Scar protein exhibits severe defects in chemotaxis and motility, but cells can still extend pseudopods and migrate directionally [PirA gene encoding an homologue of SRA1 causes an excessive lamellipodial protrusion phenotype, thought to be due to unregulated Scar protein activity [Knockdown of Abi1, SRA1 and NckAP1 in mammalian tissue culture cells causes severe defects in formation of lamellipodia ,36, simiilopodia -41. Muta protein ,43. RNAire cells ,41,43. Dtionally ,45, wheractivity .While the Scar/WAVE complex is currently thought to be the most likely regulator of Scar/WAVE activity in cells, several other Scar/WAVE binding proteins have been identified and proposed to regulate its activity. IRSp53, for example, binds to Scar/WAVE2 and also to Rac and was proposed to be a Scar/WAVE2 regulator prior to Eden et al. -50). It . It 50])Scar/WAVE3 is the most tissue-specific of the three mammalian Scar/WAVE isoforms, being found in haematopoietic cells and brain tissue, but not yet being characterised in cells ,56. It hProtein sequence conservation between the three Scar/WAVE proteins in the N-terminal Scar Homology Domain (SHD) indicates a potential for association of binding partners with all three members of the family. Scar/WAVE1 and 2 have been shown to associate with the key adapter protein Abi1 through the SHD, so it is possible that Abi1 would also associate with Scar/WAVE3 ,30. To tOur data and previously published studies indicate that Abi1 exhibits the potential to bind to all three mammalian Scar/WAVE isoforms. To test whether the association of Scar/WAVE1, 2, and 3 with Abi1 is mediated by the Scar Homology Domain, Myc-Scar/WAVE SHD of each of the three family members were transiently expressed in Cos cells with HA-Abi1 or HA-HSPC300 and immunoprecipitated using a 9E10 anti-myc monoclonal antibody. HA-Abi1 precipitated with each of the three SHDs, but not in the absence of antibody or Myc-SHD Fig. . HA-HSPCConservation of Scar Homology Domain binding partners raises the question of whether other binding partners of Scar/WAVE are also capable of interacting with the entire Scar/WAVE family. IRSp53 has been implicated in regulation of actin dynamics through binding to Scar/WAVE proteins ,50,54. GAs an activator of the Arp2/3 complex, Scar/WAVE3 would be expected to localize to the same areas of the cell as polymerized actin, subunits of the Arp2/3 complex, and members of the Scar/WAVE complex. C2C12 cells were used for immunocytochemistry with anti-Scar3 polyclonal antibody Fig. , phalloiIf the SHD of Scar/WAVE protein mediates the interaction with a regulatory complex it can be expected that the SHD will co-localise with binding partners in cells. Ectopic expression of full length Scar/WAVE isoforms in cells reveals a peri-nuclear and cytoplasmic pool, causing an abundance of polymerised actin in the cytoplasm, but with enrichment of Scar/WAVE protein in lamellipodia ,58. A siWhen expressed with Myc-Scar 1 SHD Fig. , HA-HSPCHA-Abi1 Fig.  stronglyWe show that Abi1 and HSPC300 interact with three human Scar/WAVE isoforms, via the Scar Homology Domain. Conservation of Abi1 binding between all three Scar/WAVEs is indicative of the association of the "Scar/WAVE complex" with all three isoforms, as Abi1 seems to be the link between Scar/WAVE and NckAP1 and SRA-1/PIR121 in previous reports . AssociaLittle is known about the role of Scar/WAVE3 in cells. Expression of the mammalian Scar/WAVE3 gene is largely limited to the brain and lung with relatively little expression in other adult tissues ,32,56. Tbrk1 gene product, the Maize homologue of HSPC300, causes aberrations in cellular morphology and filamentous actin distribution of leaf epidermal cells [The role of interaction of HSPC300 with the Scar/WAVE3 complex is unknown. Loss of function of the al cells ,25. Thisal cells .Association of all three Scar/WAVE isoforms with an Abi-NckAP1-SRA1 complex may show that there is some functional redundancy between Scar/WAVE family members but data from mice deficient in a Scar/WAVE isoform indicates that Scar/WAVE1 and 2 do not have completely overlapping functions ,38,57,61We conclude that Scar/WAVE3 is likely to participate in similar signalling complexes to Scar/WAVE1 and 2 and that the differences between these Scar/WAVE proteins is likely to be at the level of tissue expression, differences in affinity for certain binding partners and possibly interaction with yet undiscovered binding partners.All chemicals were purchased from Sigma-Aldrich, UK unless otherwise specified. Antibodies were from the following sources: 9E10 anti-Myc monoclonal (Cancer Research UK), 12CA5 anti-HA monoclonal (Cancer Reaearch UK), anti-Myc polyclonal 1:500 anti-HA polyclonal (Santa Cruz), 1:500 anti-Scar/WAVE3 (Upstate), 1:100 anti-Abi1, or 1:100 anti-Scar/WAVE1 , anti-AbHSPC300 and Abi1 expression vectors were created using the Gateway Cloning system (Invitrogen). Open reading frames for HSPC300, and Abi1, derived from I.M.A.G.E. clones 4519512 (HSPC300) and 4158413 (Abi1) (UK-Human Genome Resource Centre, Babraham), were amplified by PCR and ligated into the pENTR-Topo entry vector (Invitrogen). N-terminally HA- and Myc- tagged HSPC300, and Abi1, were created in pRK5 DEST-Myc or pRK5 DEST-HA Gateway destination vectors by recombination from entry vectors. pRK5 DEST-Myc or pRK5 DEST-HA were created by ligation of the Gateway Vector Conversion System (Invitrogen) reading frame A cassette into pRK5-myc or pRK5-HA cut with SmaI. Scar/WAVE1, Scar/WAVE2, and Scar/WAVE3 full length and deletion constructs were created by PCR and cloned into pRK5-myc or pGEX4T-2. Scar/WAVE-1 constructs were derived from KIAA00429; Scar/WACos 7 and C2C12 cells were grown in DMEM plus 10% Foetal Calf Serum and antibiotics. Transfections were performed using Genejuice transfection reagent (Novagen) following the manufacturers instructions. Cell lysis was performed from confluent cells in a 1% Triton X-100 lysis buffer (1% Triton X-100, 50 mM Tris-HCl pH 7.5, 150 mM NaCl,, 1 mM EDTA, 10% Glycerol, 1 mM PMSF, and 1 μg/ml each of chymostatin, aprotinin, leupeptin, and pepstatin). Lysates were cleared by centrifugation and equalised to 1 mg/ml total protein concentration.Mouse brains were homogenised on ice in 2 μl of 1% Triton X-100 lysis buffer per mg of tissue using a fine gauge needle. Homogenates were centrifuged once for 10 minutes at 13,000 rpm in a microcentrifuge, then for 30 minutes at 100,000 g in a Beckmann TLA-100.2 rotor. Supernatants were equalised to 1 mg/ml total protein concentration with 1% Triton X-100 lysis buffer.Lysates or brain extracts  were incubated with 30 μl bed volume of pre-washed protein G beads (Cancer Research UK) for 1 hour, designated beads minus antibody controls. Beads were precipitated and supernatants removed. 5 μg of antibody monoclonal 9E10 anti-myc, 12CA5 anti-HA, Rabbit anti-Scar/WAVE3, or Mouse anti-Abi1 or an irrelevant control antibody (rabbit or mouse anti-myc) was added to the supernatant and incubated for 1 hour, before the addition of 30 μl bed volume of pre-washed protein G beads, designated beads plus antibody, for 1 hour. Beads were precipitated, and the supernatant removed. Beads were washed 3 times in 30-fold bed volume of lysis buffer. Beads were resuspended in an equal volume of 2× SDS-PAGE loading buffer . 10 μl oE.coli induced for 3 hours with 0.2 mM IPTG, or 15 hours at 25°C for GST-IRSp53. Cells were spun down and sonicated in 1 ml of 1% Triton PBS (1% Triton X-100, PBS pH 7.5, 1 mM PMSF, and 1 μg/ml each of chymostatin, leupeptin, aprotinin, and pepstatin) per 100 ml bacterial culture. GST fusion proteins were batch purified on 100 μl Glutathione-s-agarose (Sigma) per 100 ml culture and washed 5 times with 10-fold bed volume of sonication buffer. Beads were resuspended in an equal volume of 1% Triton lysis buffer. SDS-PAGE analysis and coomasie staining were used to qualify GST fusion proteins. 500 μg total protein of transfected Cos7 cell lysate was incubated with GST-fusion protein or GST alone for 1 hour. Beads were precipitated, lysates removed and the beads were washed 3 times in 1% Triton lysis buffer. Beads were resuspended in and equal volume of 2× SDS-PAGE loading buffer. Samples were analysed by SDS-PAGE and western blotting.GST-fusion proteins were expressed in BL21 Proteins were separated by SDS-PAGE and transferred onto nitrocellulose membrane by western blotting. Blots were saturated with 5% dried milk in PBS 0.2% Tween-20, probed with 1:500 9E10 anti-Myc monoclonal, 1:500 12CA5 anti-HA monoclonal, 1:500 anti-Myc polyclonal 1:500 anti-HA polyclonal, 1:500 anti-Scar/WAVE3, 1:100 anti-Abi1, or 1:100 anti-Scar/WAVE1 primary antibody in 2.5% BSA 0.2% Tween-20 PBS followed by 1:10'000 dilution of HRP-conjugated secondary antibody diluted in 0.2% Tween-20 PBS. Bound secondary antibody was visualised using SuperSignal (Pierce) according to the manufacturers instructions.Cells cultured on glass coverslips were fixed in 4% paraformaldehyde and permeabilized with 0.1% Triton PBS. Cells were stained with 1:200 Rb anti-HA polyclonal, 1:200 12CA5 anti-HA monoclonal, 1:200 9E10 anti-myc monoclonal, 1:100 anti-Scar\WAVE3, 1:200 monoclonal anti-Arp3, or 1:25 anti-Abi1 primary antibodies diluted in 1% BSA PBS. Secondary antibodies used were Goat anti-mouse or rabbit, FITC, TRITC, Alexa-488 or Alexa-546 Conjugates at 1:200 dilution in 1% BSA PBS. Filamentous actin was visualised using Alexa-546 conjugated phalloidin (Molecular Probes) diluted 1:500 in 1% BSA PBS. Slides were viewed using a BioRad MRC100 confocal laser scanning microscope. For endogenous Scar/WAVE3 labelling, it was important to test which cell lines expressed Scar/WAVE3. Among the following cell lines: NIH 3T3, N1E 115 neuroblastoma, N19 neuroblastoma, PC6 neuronal precursor, J774.A1 bone marrow macrophage, RAW 264.7 alveolar macrophage and C2C12 myoblast, we found that only C2C12 expressed endogenous Scar/WAVE3 by western blotting of whole cell lysates (M. Vartiainen, unpublished results). We thus used C2C12 for localisation of endogenous Scar/WAVE3 protein.GST glutathione-S-transferase, WH2 WASP-homology 2, A acidic sequence motif, SHD Scar homology domain, NckAP1 Nck-associated protein 1, HSPC300 haematopoeitc stem/progenitor cell protein, Abi 2 abelson tyrosine kinase interactor 2C.F.S. performed most of the experiments and contributed intellectually to the design and interpretations. C.F.S. also drafted the manuscript. T.H.M. performed the experiments shown in Figure A) Schematic representation of the domain architecture of Scar/WAVE family proteins and the deletion constructs used in these experiments.  Numbers indicate start or end point of Scar/WAVE1 deletion mutants in amino acid residue position.  Start/End points for Scar/WAVE2 deletion mutants are FL = 498, delta A = 456, SPW = 455, SP = 435, SB = 245, SHD = 170, BPWA = 171, PWA = 246.  Start/End points for Scar/WAVE3 deletion mutants are FL = 499, delta A = 488, SPW = 457, SP = 439, SB = 238, SHD = 171, BPWA = 172, PWA = 239.  FL; Full length.  SHD; Scar Homology Domain.  B; Basic Rich Region.  PRR; Proline rich region.  W; Wiskott Homology 2 domain.  C; Central or connecting region.  A; Acidic Rich Region.  (B) Co-immunoprecipitation of HA-Abi1 with Myc-Scar/WAVE deletion constructs.  (C) Co-immunoprecipitation of HA-Abi1 or HA-HSPC300 respectively with Myc-Scar/WAVE deletion constructs.  Protein G beads were used to immunoprecipitate protein from transiently transfected Cos 7 fibroblasts before (-Ab) and after addition (+Ab) of an anti-Myc (9E10) monoclonal antibody.  Bead bound fractions and supernatants (Sup) were analysed by SDS-PAGE followed by immunoblotting.  Blots were probed with an anti-HA polyclonal antibody for the presence of HA-Abi1 or HA-HSPC300 in immunoprecipitated complexes.Characterization of HSPC300 and Abi1 binding domain of Scar/WAVE1.  (Click here for file


The analysis of gene expression using DNA microarrays provides genome wide profiles of the genes controlled by the presence or absence of a specific transcription factor. However, the question arises of whether a change in the level of transcription of a specific gene is caused by the transcription factor acting directly at the promoter of the gene or through regulation of other transcription factors working at the promoter.a1-α2 complex, which represses haploid-specific genes in the yeast Saccharomyces cerevisiae. Our analysis identified many known or suspected haploid-specific genes that are direct targets of the a1-α2 complex, as well as a number of previously uncharacterized targets. We were also able to identify a number of haploid-specific genes which do not appear to be direct targets of the a1-α2 complex, as well as a1-α2 target sites that do not repress transcription of nearby genes. Our method has a much lower false positive rate when compared to some of the conventional bioinformatic approaches.To address this problem we have devised a computational method that combines microarray expression and site preference data. We have tested this approach by identifying functional targets of the These findings show advantages of combining these two forms of data to investigate the mechanism of co-regulation of specific sets of genes. A bioinformatic approach to identifying cis-regulatory elements controlling transcription has become feasible with the availability of complete genome sequences and large scale expression data using high-throughput methods such as microarrays ,2 and SASaccharomyces cerevisiae [a and α cells, and the a/α diploid, that differ in their ability to mate and in the proteins they express. Cell mating-type is determined in part by α2 and a1, which are cell-type-specific proteins that are members of the homeodomain (HD) DNA-binding family. In an a/α diploid cell, α2 binds with a1 to form a heterodimer complex that represses transcription of haploid-specific genes [α2 HD binding DNA alone and in complex with a1 have been solved, providing models for how these complexes bind DNA [a1-α2 complex. Previous work, using a relatively simple binding site search program identified targets for the α2-Mcm1 complex, which represses a-cell-type specific genes in α and a/α cells [a1-α2 complex that may be involved in cell-type specific processes. Interestingly, we identified several genes that are repressed in diploid cells but do not appear to be direct targets of the a1-α2 complex, suggesting that these genes are controlled by another transcriptional regulatory factor that is directly or indirectly regulated by the a1-α2 complex. We have also identified a number of a1-α2 target sites that do not repress adjacent genes. The combination of site preference and expression data is therefore a valuable tool to identify direct functional targets of a transcription factor or complex.We have applied this methodology to study the transcriptional regulatory system that specifies cell mating-type in the yeast revisiae . Yeast hic genes . The crybind DNA ,7. Biochbind DNA -10. Genobind DNA . The com/α cells . The mora and α haploid and a/α diploid cells, as well as various polyploids [a1-α2 complex should be absent in any of the homozygous a or α type polyploids (a, aa, ..., α,αα,..., etc.) we expect the expression of haploid-specific genes in these cells to be much higher than in cells that are heterozygous for the MAT locus (aα, aaα, aαα, aaαα, etc.). Thus, one term in the scoring function rewards lower expression in heterozygous cell types compared to the homozygous cell types (see the Methods section for details). We expect that most of the haploid-specific genes will be expressed equally in both of a and α cell types. Consequently, we have introduced a second term in the scoring function that penalizes such differences in expression in the two haploid cell types. This scoring function would identify haploid-specific genes that are repressed in diploid cells, but would not indicate if these genes are direct targets of the a1-α2 repressor complex.To generate an algorithm that combines microarray expression data and mutational analysis of binding sites, we first defined a scoring method that ranks gene expression data. We utilized the microarray expression data from Galitski and coworkers for gene expression in the lyploids . Since ta1-α2 complex we used the available mutational data on the a1-α2-binding site [a1-α2 consensus binding site were measured by assaying their ability to repress transcription of a heterologous promoter and by electrophoretic mobility shift DNA-binding assays (EMSA). Under the assumption that the level of expression is proportional to how often that site is unoccupied, we used the effects of the single base mutations to estimate the parameters for the binding energy of sites with different bases at each position. We then used this information to search for potentially strong binding sites in the promoter regions (in practice, 800 bp upstream of the translation start site) of every gene in the genome. This search provides us with a list of genes with putative a1-α2 binding sites in the promoter, irrespective of functionality.To identify genes from this ranking that are directly repressed by the ing site . In thesa1-α2 complex. In addition, our accuracy is limited by the noise in the microarray expression data, as well as by simplifying assumptions made to utilize single-base mutational data to score arbitrary sequences. We therefore decided to rank the genes using a scoring system that takes into account expression patterns across different mating types, as well as the likelihood of finding a good a1-α2-binding site in the promoter region of the gene. To test the significance of these composite scores, we generated permuted data, which combined random promoters with the expression data. This analysis suggested that the top 10–15 predictions were significant. The results of experiments on the 24 genes with the highest combined scores from our analysis are shown in Table Even under ideal conditions, either of the above lists of genes would not directly indicate a haploid-specific gene directly repressed by the a1-α2 repressor complex, we assayed binding by the complex to the identified promoters using chromatin-immunoprecipitation (ChIP) assays with polyclonal antibody directed against the α2 protein. In α haploid and a/α-diploid cells the α2 protein combines with the MADS-box transcription factor Mcm1 to bind to elements in the promoters of a-specific genes to repress their transcription [a-specific gene STE6 to serve as a positive control for the ability to ChIP α2 in both haploid α and diploid a/α cells. This primer set also allowed us to rule out the possibility that the predicted a1-α2 target genes were immunoprecipitating because of binding by the α2-Mcm1 complex. The primer set for the YDL223C promoter, a gene not bound or repressed by the a1-α2 or α2-Mcm1 complexes, was included in the reaction as a negative control for non-specific immunoprecipitation of the DNA. A gel displaying the results for a few of the promoters that were assayed is shown in Figure a1-α2 complex is listed in Table a1-α2 complex in vivo. Genes that had a combined p-value higher than the threshold of 1.5 × 10-4 . However, the search identified one site upstream of the AMN1/CST13 gene that was a potential target site. The ChIP analysis verified this as a functional target site for the a1-α2 complex in vivo  of the gene. However, there are a few genes, like a1-α2 site by our analysis  . Promoters with a number of weak affinity sites may therefore be directly regulated by the a1-α2 complex. However, it is also possible that these genes are indirectly repressed by a1-α2, through its ability to repress expression of another transcription factor that is required for expression of the genes identified in the microarray. To distinguish between these possibilities we assayed for a1-α2 binding to these promoters by ChIP . However, much of our success has to do with cutting down of false positive rates by using microarray data judiciously.a1-α2-binding sites in their promoter region and repression in diploid cells. This raises the question of whether all a1-α2-binding sites function as repressor sites. To address this question we searched for all potential binding sites in the yeast genome. As expected, many of the best sites are in the promoters of known or previously identified haploid-specific genes (Table a1-α2-binding sites within ORFs (Table a1-α2 complex is able to bind to these sites we performed electrophoretic mobility shift assays (EMSAs) with purified α2 and a1 proteins and radiolabeled oligonucleotides containing these sites . However, these sites did have slightly better binding affinity than to the HO(8) site, which we have shown is unable to repress transcription on its own (Mathias and Vershon, unpublished).Among the genes identified in the computational analysis, there is a good correlation between the presence of strong Fs Table . To testites Fig . The a1-a1-α2 complex was able to bind to these sites with weak to moderate affinity in vitro, it is possible these sites may partially repress transcription on their own. To test this model, we cloned these sites into the context of the CYC1 promoter driving expression of a lacZ gene and measured the ability of the sites to repress transcription of the reporter in diploid cells [CDC25 and URB1 ORFs did not repress transcription of the reporter promoter in diploid cells  repressed the reporter promoter. This result indicates that this site can function as a repressor site in vivo if placed in the proper context. We next tested whether a1-α2 bound to these sites in the normal genomic context in vivo by ChIP assays. None of the sites in the ORF regions were bound by the a1-α2 complex , is a direct target for the a1-α2 complex [RDH54, a gene involved in double-stranded DNA break repair, is a direct target for the a1-α2 complex [RDH54 is required for meiosis and null mutants show significantly reduced spore viability. It is likely that the a1-α2 complex only partially reduces the level of expression of the gene and that diploid cells require a lower level of activity of the protein.The second subset of genes identified in the analysis is associated with mating type switching and recombination. The affinity . Repress complex ,28. It h complex . This reRME1, which encodes a transcriptional repressor of IME1, the master regulator of meiosis [a1-α2-mediated repression of RME1 is required to allow cells to enter the meiotic pathway in diploid cells. Interestingly, we also found that PDE1 and MET31 are weakly, but reproducibly, direct targets for repression by the a1-α2 complex. The Pde1 protein is a low affinity cAMP phosphodiesterase that appears to have a role in response to stress and cell aging [PDE1 in diploids may partially account for the difference of starvation response between haploids and diploids. Met31 is a zinc finger DNA-binding protein that activates genes involved in sulfur metabolism [a1-α2 complex.We also identified several genes that fell outside of these two subsets. One is  meiosis -32. a1-αll aging . Represstabolism . It is ua1-α2 target site upstream of a gene that has lower expression in diploid cells was fortuitous and that these sites were not functional targets. However, if this was the case then there would be little pressure to conserve these binding sites through evolution. Several closely related species of yeast have been sequenced and comparison of the corresponding promoter regions has led to the discovery of conserved regulatory motifs [Saccharomyces species using the PhyloGibbs program [a1-α2 binding site among a promoter set including many known haploid-specific genes (HO, NEJ1, GPA1, STE4, and STE18). This analysis also showed that the a1-α2 binding sites in the RDH54, PDE1and MET31 promoters are strongly conserved among multiple species, suggesting that these sites play an important functional role.It is possible that the presence of an y motifs ,36. Alth program . The proa1-α2 repressor complex We define a scoring algorithm that ranks gene expression patterns. For gene g) = sgn((Xa(g) + Xα(g))/2 - Xa/α(g)) [(Xa(g)) + Xα(g))/2 - Xa/α(g)]2 - A(Xa(g) - Xα(g))2.Score(g for the three cell types for the variables Xt(g) with t indicating the type. We have since found that using the complete expression data for the polyploids is a better strategy. In the final results, shown in the paper, Xa(g) is the average of the log expression for a, aa and aaa. Likewise, Xα (g) is the average from α, αα and ααα. Xa/α(g) comes from averaging log expression over aaα, aαα and aaαα. The polyploid averaged quantities tend to be less noisy , namely, fraction of genes with score higher than g.An explanation of the purpose served by different terms in the overall score is described below. The first term scores well when expression in diploids is lower than the average expression in haploids. The second term penalizes the gene if the expressions in different types of haploids are very different. a1-α2 repressor complex. In order to select for genes with an upstream region with a strong a1-α2 repressor, we used the binding site mutational data available [a1-α2 repressor, was measured. Under the assumption that the degree of repression is inversely proportional to how often that site is occupied, we derived the expression: 1/Repression ∝ [1 - 1/(1 + eβE(S)/z)] ≈ eβE(S)/z assuming near saturation of binding. The symbol z represents the fugacity and β is inverse of kBT, kB being the boltzman constant. The binding (free) energy is given by E(S) = ΣibεibSib, within the single base model [i runs over the positions in the motif and b runs over the bases A, C, G, T.Sib is 1 or 0 depending upon whether the i-th base is b or not. The parameters εib represent effects of single base changes on the binding (free) energy. They are related to the weight matrix parameters [βE(S)/z would more commonly be represented as (K/ [Protein])•exp(ΔG(S)/RT) in the biochemistry literature [FAR1 and MATα1. Thus, from the effect of the single base mutations, we estimated the parameters εib. Armed with these parameters, we found the probability P(E|ε, L) that a random sequence of a certain length, L, would have a subsequence of binding energy greater than E. For gene g, the strongest site in the upstream region of length L would have binding energy Eg. Low values of Pbinding(g) = P(Eg|ε,L), indicated the presence of a good binding site.This scoring function would rank haploid-specific genes high but may not select out genes that are directly regulated by the vailable . In thesse model ,16. The rameters ,18 widelterature . The indPexprn(g) Pbinding(g), and then ranked as candidates for haploid-specific genes that are directly repressed by the a1-α2 complex. One of the issues in such studies is how to decide on how many of the top candidates are significant. This problem occurs even in solely, sequence based analysis as well [The genes were ordered according to the lowest value of a combined p-value,  as well . In our wia, was set to log((fia + δ)/(Pa + δ)), where fia is the frequency with which base a appears in the ith position in the known sites, Pa is the frequency with which base a appears in the promoters of genes and δ is a small number added to ensure that the weight matrix score is finite even when fia = 0. Each subsequence, Sia, of length 20 in each promoter was assigned a weight matrix score Σia Sia wia. After a threshold score is chosen, sites scoring above that threshold are declared to be binding sites.A weight matrix -18 searcσ, was implemented. To each pair of numbers, du, and dd, representing primer distances upstream and downstream of the candidate binding site respectively, and primer lengths lu, and ld, a score is assigned viaAn automated procedure for generating primers flanking a specified site in the genome sequence, S(du, dd, lu, ld,σ) = - Σaka(Pa(du, dd, lu, ld,σ) - )2Pa are functions including the melting temperatures, distances of upstream and downstream primers from the candidate site, total length of the bound region, and the difference between the primer melting temperatures. The  s are the preferred values of those functions. The ka are adjusted to reflect the relative importance of the parameters; for example it is more important that the difference in melting temperatures be close to zero than it is that the distance to the upstream primer match the distance to the downstream primer.where the du, dd, lu and ld are restricted to those whose corresponding primers have GG, GC, CG, or CC at the end nearest the candidate site. Primers are identified by selecting the values of du, dd, lu and ld which maximize S.Values of ][A web-based interface to this algorithm is available at MATα/MATaade2-1/ADE2 HIS3/his3-11,15 leu2-3,112/leu2-3,112 trp1-1/trp1-1 ura3-1/ura3-1 ash1Δ::LEU2/ash1Δ::LEU2) and JRY118 (MATα/mataΔ::TRP1 ade2-1/ADE2 HIS3/his3-11,15 leu2-3,112/leu2-3,112 trp1-1/trp1-1 ura3-1/ura3-1 ash1Δ::LEU2/ash1Δ::LEU2) cultures were grown to an A600 of 0.5 and treated with 1% formaldehyde for 20 min at RT on a rotating shaker at low speed. Cells were collected, washed 2X with cold 1XTBS. Equal volumes of cells were aliquoted into ten 1.5 ml microfuge tubes, washed once with 1.5 ml of cold 1X TBS. The pellets in each tube were resuspended with 400 μl of lysis buffer (50 mM HEPES, pH 7.4, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, 0.1% Na-Deoxycholate) plus 1 mM PMSF, 1 mM benzamidine, and 1X Protease inhibitor cocktail from Roche (Cat No. 1873580) and also manufacturer recommended concentration of protease inhibitor cocktail from SIGMA (Cat No., P 8215). To this 200 μl of glass beads were added to each tube and lysed using a multitube vortexer at full speed for 30 min at 4°C. The lysate was transferred in a new tube and 400 μl of lysis buffer was added and vortexed briefly. The lysates were centrifuged at 12,000 g for 10 min at 4°C and the supernatants were sonicated at 30% output for four 10 sec cycles with intermittent cooling on ice.Chromatin immunoprecipitation (ChIP) was carried out as described previously  with theth volume aliquot was removed and frozen to be used as total chromatin control. The remaining sample was precleared by the addition of 25 μl recombinant protein G-agarose beads, incubated while nutating for 30 min and the supernatant was collected after centrifugation at 12,000 for 5 min. 1 μl of rabbit anti-α2 antiserum (a gift from A. Johnson, UCSF) was added to each supernatant of the samples and incubated 12 h on a nutator at 4°C. To immunopreciptiate α2 50 μl of recombinant protein G agarose beads (Roche) was added to the samples and nutated for 90 minutes at 4°C. The protein G beads were pelleted, washed once in low salt buffer (0.1%SDS, 1% Triton X-100, 20 mM Tris pH8.0, 2 mM EDTA and 150 mM NaCl), once in high salt , once in LiCl buffer (0.25 M LiCl, 1% IGEPAL, 1XTE and 1% Na-Deoxycholate) and twice with 1XTE (pH8.0). The immunoprecipitated DNA was eluted twice with 250 μl of elution buffer (1%SDS and 0.1 M NaHCO3) and the eluates were pooled . To this 20 μl of 5 M NaCl was added and incubated 12 h at 65°C. To remove the crosslinks, 10 μl of 0.5 M EDTA, 20 μl of 1 M Tris-HCl, pH 7.5 and 2 μl of proteinase K (10 mg/ml) was added and incubated for 45 minutes at 45°C. The DNA samples were extracted once with Phenol:chloroform:Isoamylalcohol and the DNA was ethanol precipitated, washed once with 70% ethanol and resuspended in 50 μl (IP) or 500 μl (TC) TE.The lysates were cleared by centrifugation at 12,000 g for 10 min and 1 mM PMSF was added to the samples. A 1/10STE6 promoter as a positive control for the immunoprecipitation of α2 and the YDL223C ORF as a negative control for nonspecific immunoprecipitation, along with the specific primers for candidate α2-a1 target sites. PCRs were carried out in 50 μl containing 10 pmols of each primer, 0.2 mM dNTPs, 2 mM MgCl2, 1X Eppendorf Taq buffer, 0.5X Taq Master buffer and 2.5 U of Eppendorf Taq polymerase. The amplifications were carried out at 94°C for 1 min and 30 secs, followed by 25 cycles of 94°C for 30 secs, 52°C for 1 min, and 72°C for 30 secs and a final extension step of 7 min at 72°C. The PCR products were separated on 2.5% agarose gels.Purified DNA from the immunoprecipitated samples was subjected to multiplex PCR amplification with primers specific for the a1-α2 binding sites from within the ORFs of URB1, PRM8, PRM9, YKL162C and CDC25 and the promoters of COX13, REX2, LSM1, and FMP14 were synthesized, one strand was end-labeled with [γ-32P]-ATP, and then annealed with excess cold complementary oligonucleotide. The HO(10) and HO(8) a1-α2 sites within Upstream Regulatory Sequence 1 (URS1) of the HO promoter were used as strong and weak binding sites respectively. The EMSA was performed as described previously [μM a1 and five-fold titrations of α2 starting at 82 nM in protein dilution buffer .Oligonucleotides containing the predicted eviously , using aa1-α2 binding sites were synthesized with 5' overhangs to allow cloning into the XhoI site of pTBA23 (2μ URA3 Ampr), a reporter plasmid containing a CYC1-lacZ fusion [β-galactosidase activity was measured on three independent transformants, as described previously [Oligonucleotides containing Z fusion . Reporteeviously .SAGE – Serial Analysis of Gene ExpressionHD – HomeodomainORF – Open Reading FrameChIP – Chromatin immunoprecipitationNHEJ – Non-Homologous End JoiningTC – Total ChromatinIP – Immunoprecipitated sampleEMSA – Electrophoretic Mobility Shift AssayPCR – Polymerase Chain ReactionPMSF – Phenyl Methyl Sulfonyl FluorideEDTA – Ethylenediaminetetraacetic AcidVHN carried out the ChIP experiments, and wrote parts of the first draft of the manuscript. RAO did the bioinformatics analysis, and wrote programs facilitating primer design. ARB did EMSA and beta-gal assays. JRM constructed the strains and contributed to the design of the ChIP experiment. AKV and AMS supervised and coordinated the computational and the experimental research as well as prepared the manuscript. All authors contributed to the manuscript and approved the final version.


In order to confirm a previous finding of linkage to alcoholism on chromosome 1 we have carried out a genetic linkage study.DNA from eighteen families, densely affected by alcoholism, was used to genotype a set of polymorphic microsatellite markers at loci approximately 10 centimorgans apart spanning the short arm and part of the long arm of chromosome 1. Linkage analyses were performed using the classical lod score and a model-free method. Three different definitions of affection status were defined, these were 1. Heavy Drinking (HD) where affected subjects drank more than the Royal College of Psychiatrists recommended weekly amount. 2. The Research Diagnostic Criteria for alcoholism (RDCA) 3. Alcohol Dependence Syndrome (ADS) as defined by Edwards and Gross (1976) and now incorporated into ICD10 and DSMIV.Linkage analyses with the markers D1S1588, D1S2134, D1S1675 covering the cytogenetic region 1p22.1-11.2 all gave positive two point and multipoint lods with a maximum lod of 1.8 at D1S1588 (1p22.1) for the RDCA definition of alcoholism. Another lod of 1.8 was found with D1S1653 in the region 1q21.3-24.2 using the HD affection model.These results both support the presence of linkage in the 1p22.1-11.2 region which was previously implicated by the USA Collaborative Study of the Genetics of Alcoholism (COGA) study and also suggest the presence of another susceptibility locus at 1q21.3-24.2. Epidemiological studies report that alcoholism as defined by the Research Diagnostic Criteria (RDC) affects almost 10–15% of the general population in the USA and 1–5% in Europe . The oveSelection criteria for the families eliminated all but 9% of the families contacted from all sources. Press contacts were found to be the most productive source of suitable families willing to participate in the study, accounting for over 50% of subjects recruited. There appeared to be no bias in the affection status of subjects recruited from the different sources . The affAssuming dominant transmission, two-point heterogeneity LODS (HLOD) for linkage to the HD affections status gave a score of 1.8 near the marker D1S1653. The three-point multipoint analysis produced an HLOD of 1.8 with the markers D1S1653 and D1S1677. Analyses with the same markers assuming recessive transmission produced an HLOD of 1.1 with a two-point analysis and an HLOD of 1.5 with a three-point analysis. The model-free (MFLINK) analysis produced an MLOD of 1.5 for both the two-point and three-point analyses. Three-point analyses of D1S1677 and D1S1679 produced an HLOD of 1.7 assuming recessive transmission and a MALOD of 1.3 for the model-free three point analysis. The marker D1S1675, on the short arm of chromosome 1, produced an HLOD of 1.2 with both a two-point analysis and a three-point analysis in combination with D1S3723 assuming dominant transmission, Model-free analyses of D1S1588 produced a MALOD of 1.5 and 1.2 using two-point and three-point analyses, respectively. Figure Assuming recessive transmission the marker D1S1679 was linked to RDC alcoholism with a two-point HLOD of 1.0 and a three-point HLOD of 1.6 with the markers D1S1677 and D1S1679. The MLOD two point MFLINK score with D1S1677 was 0.9 and this increased to 1.4 with a three-point model-free analysis.For the RDCA level of affection status the highest MALOD score of 1.8 was found near the telomere of chromosome 1p near the marker D1S1588 under the model free analysis. Figure The marker D1S1591 gave the highest maximum admixture lod (MALOD) of 1.00 with the model-free method of analysis for the ADS category. The results of MFLINK multipoint analysis for this and the other affection status models are shown in table The COGA study first reported evidence of a locus linked to alcoholism on chromosome 1p with the markers D1S532 and D1S1588 linked to Alcohol Dependence . Curtis Dick et al  who analIn the UK linkage study the two regions on chromosome 1 are implicated with two different affection status categories. We could speculate that the two different loci on chromosome 1 could be mediating an effect on alcoholism through two loci that have different types of effect on susceptibility. The most interesting and probably important finding from the COGA linkage study is that the stratification of families that have members with both alcoholism and depression maximizes the lod to 5.12 on chromosome 1p near the markers D1S1648 and D1S1588 . WithoutPrior to commencing the study, ethical permission for this research project was obtained from the University College London Medical School Clinical Investigations Panel which has been updated in 2003 with multicentre research ethics committee (MREC) approval. Information stored on computer was registered under the 1984 Data Protection Act.Caucasian families multiply affected by alcoholism, and suitable for linkage studies, were ascertained with the following selection criteria.(a) Presence of two or preferably more cases of alcoholism as defined by the RDC.(b) Large families, preferably with two or more generations willing and able to participate.(c) Evidence of unilineal inheritance of alcoholism in the parental generation, which meant that families were excluded where both parents were affected.(d) Willingness of as many family members as possible, especially affected individuals, to participate in the research project. The families were identified from hospital records, clinicians, advertisements in the media and other contacts . The famFifty nanograms of total genomic DNA was extracted from venous blood samples and amplified by Polymerase Chain Reaction (PCR) with oligonucleotide primers using standard methodology . Short T. Positions of the markers are shown in table Ten markers on 1p21-35 were genotyped: D1S1591, D1S2134, D1S1665, D1S1728, D1S532, D1S1588, D1S551, D1S1631, D1S3723 and D1S1675. Four markers on 1q were genotyped: D1S1595, D1S1653, D1S1677 and D1S1679. Marker order and intermarker distances were as compiled in the Ensembl database Genotypes were read blind to diagnostic information. Tests for Mendelian inheritance of marker data were performed and inconsistent genotypes were repeated or omitted.The linkage analysis was performed using the classical lod score method and using likelihood-based model-free analysis carried out with the MFLINK program . For lodIG carried out the genotyping and produced the manuscriptCCHC collected the multiplex family sample and participated in the design of the studyWK participated in genotypingAD participated in genotypingAM participated in genotypingDC performed the linkage analysisHMDG conceived the study and participated in its design and coordinationAll authors read and approved the final manuscript


Sequences flanking microsatellites of similar length show remarkable levels of convergent evolution, indicating shared mutational biases. These biases extend 25–50 bases either side of the microsatellite and may therefore affect more than 30% of the entire genome. To explore the extent and absolute strength of these effects, we quantified the observed convergence. We also compared homologous human and chimpanzee loci to look for evidence of changes in mutation rate around microsatellites. Most models of DNA sequence evolution assume that mutations are independent and occur randomly. Allowances may be made for sites mutating at different rates and for general mutation biases such as the faster rate of transitions over transversions. Our analysis suggests that these models may be inadequate, in that proximity to even very short microsatellites may alter the rate and distribution of mutations that occur. The elevated local mutation rate combined with sequence convergence, both of which we find evidence for, also provide a possible resolution for the apparently contradictory inferences of mutation rates in microsatellite flanking sequences.Microsatellites are a major component of the human genome, and their evolution has been much studied. However, the evolution of microsatellite flanking sequences has received less attention, with reports of both high and low mutation rates and of a tendency for microsatellites to cluster. From the human genome we generated a database of many thousands of (AC) An analysis of sequences flanking microsatellites in the human and chimpanzee genomes suggests that mutations do not occur independently and randomly, as is commonly assumed in models of DNA sequence evolution DNA base substitutions do not occur randomly . InsteadMicrosatellites are sequences of repeated 1–6-bp motifs that mutate primarily through the gain and loss of repeat units, in a process thought to depend on DNA replication slippage . PreviouTo our knowledge, no one has yet conducted a systematic study of mutational biases operating around microsatellites. The direct study of naturally occurring mutations in flanking sequences is virtually prohibited by their slow rate of accumulation, and inferences based on comparisons between homologous microsatellite loci rely on small numbers of sequences. However, an indirect approach is possible, based on comparisons among very large numbers of microsatellite flanking sequences from the finished human genome. If microsatellites have little or variable influence on their flanking regions, among-locus similarities will be minimal or absent. Conversely, if microsatellites generate similar local mutation biases, nonhomologous loci should betray evidence of convergent evolution. With the publication of large blocks of sequence from the chimpanzee genome, one can extend this approach to ask questions about rate of divergence between homologous flanking sequences.Here we use a combination of these indirect approaches to show that microsatellites appear to create regions around them in which both the rate and spectrum of mutations are modified.n, and for simplicity considered only ‘isolated’ repeats, defined as those at least 100 bp from the nearest AC repeat as small as two units in length. 47% of AC repeats on human Chromosome 1 match these criteria. From the human genomic sequence, maximum sample size was set at 5,000 randomly selected loci for length classes (AC)2 to (AC)5. For longer microsatellites, of which fewer than 5,000 could be found, all sequences encountered were included. 2+ repeats, was generated from Chromosome 1.We studied the most abundant class of human dinucleotide repeats, (AC)n and (CA)n repeats, (AC)n microsatellites were divided into subclasses, termed ‘cassettes’, according to their immediate 5′ and 3′ flanking bases such that 5′-X(AC)nY-3′ is referred to as cassette X/Y. Hence, a (CA)3 repeat would be classed as cassette C/A around (AC)2. Such a distinction may appear pedantic, but since both DNA replication fidelity and repair efficiency are known to be influenced by base order and local sequence context (n and (CA)n are equivalent. This nomenclature also helps to resolve the problem of defining microsatellite length because (CA)3 equals (AC)2. nY is viewed as comprising two dinucleotides, XA and CY. The probability of observing XA and CY jointly is then calculated from the individual frequencies of XA and CY estimated using 1 Mb of randomly sampled sequence from Chromosome 1 containing no (AC)2+ repeats.To distinguish between (AC) context , it seem2 microsatellites, the cassette frequencies are broadly similar to those expected from the frequencies of the component dinucleotides in unique sequence DNA, though eight cassettes show significant differences (Around (AC)ferences A. Howeve5, since this exhibits the strong patterns while at the same time retaining sufficient sample sizes for analyses to be conducted on the rarer cassette types. Several features are apparent. First, levels of patterning can be remarkably strong, with the probability of observing a given base at a given site varying from less than half of that in unique sequence to more than double, often at adjacent sites. Second, many of the patterns show strong dinucleotide periodicities, presumably reflecting the dinucleotide structure of the microsatellite. Third, there are several examples of clear 5′ to 3′ asymmetry, indicating that mutational patterns on one side of a microsatellite may not be the same as those on the other.We consider flanking sequences to extend 50 bases either side of a microsatellite. To compare flanking sequences, we first divided microsatellites according to (AC) repeat number and cassette type, and then calculated the frequency of each of the four nucleotides at each of the 100 possible positions. Any mutation biases present should be revealed by locally changed base composition, and this appears to be the case. For many microsatellite length–cassette combinations, the flanking sequences exhibit strong deviations from random. The observed patterns can be placed in six broad classes according to the strength of a two-base periodicity and the degree of 5′ to 3′ asymmetry. These patterns are illustrated in 9 but then declines in longer microsatellites. As well as showing variation between cassettes, flanking sequence patterning also changes with microsatellite length. For those cassettes where sample size is sufficient to show a trend, that is, T/A and to some extent C/A and A/A, both amplitude of deviation from random and the breadth of patterning tend to increase with increasing AC repeat number. We do not see dramatic changes such as a reversal in 5′ to 3′ asymmetry or the emergence of new patterns. For cassette T/A, pattern strength increases up to (AC)In view of the strong two-base periodicities seen for some base–cassette combinations, we next examined the distribution of frequencies of all 16 possible dinucleotide motifs. As expected from the single nucleotide patterning, dinucleotides also tend to show periodic patterning  and S2, From 3, then increases towards a peak in strength at (AC)9 before diminishing as AC repeat number increases further. Cassettes A/A and C/A show similar trends and together suggest that, where patterning occurs, it is apparent by (AC)6. When data were plotted without first categorizing them by cassette type, although a number of the dinucleotide patterns in As with the mononucleotide patterning, the periodicity in dinucleotide frequencies changes in amplitude and width 5′ to 3′) as repeat number increases. For most cassettes, the paucity of long microsatellites precludes study of how the patterning changes with repeat number. However, cassette T/A is sufficiently abundant for the progression to be described, and cassettes A/A and C/A, although less common, nonetheless yield meaningful results. ′ to 3′ a5. Classifying loci by cassette type, length, and whether they occurred in LINE/L1, SINE/Alu, or unique sequence DNA yielded in most classes sample sizes too low to be of use. However, where sample sizes were adequate, that is, cassette T/A, dinucleotide AT, patterning in unique sequence loci was indistinguishable from that in LINE and SINE microsatellites 5 to provide strong patterning and large sample sizes. The results of deleting all flanking sequences containing AT microsatellites with n or more repeats, where n = 2, 3, 4, and 5, are given in n allowed, suggesting that the dominant AT patterning results from (AT)n microsatellites developing immediately adjacent to the AC tract.The strongest two-base periodicities we find tend to involve motif AT. Such periodicity might arise in two main ways: either because AT motifs in phase with the microsatellite tend to expand through slippage to form (AT)n repeats. This is an important test given that our strict definition of a microsatellite restricts our analysis to pure AC repeats and allows the possibility that compound repeats, for example, (AT)n(AC)n(AT)n are included. To do this, we plotted the distribution of single AT motifs around (AC)3+ microsatellites in the subset of flanking sequences with no (AT)n microsatellites, where n > 1. Summed over all AC microsatellite lengths, single AT dinucleotides are overrepresented in 5′ sequences at odd numbered positions and at even numbered positions in 3′ sequences (We next examined whether the phase of AT dinucleotides was determined solely by their tendency to form clusters next to (AC)equences . Excesse2 or longer were assigned back to their own class 57% of the time, showing that these sequences consistently differ from those near to AC repeat tracts. Remarkably, this figure falls to almost half (32%) for sequences flanking (AC)2, indicating that, even with just two repeats, similarities to other microsatellite flanking sequences already exist. The same pattern extends to other length microsatellites, with flanking sequences tending to be preferentially assigned back to their own or to an adjacent length class. When a flanking sequence is not assigned back to its own class, it is usually assigned to one of three other classes: the ‘1’ class of random sequences, the ‘9’ class where patterning is strongest, or to the longest class, class 21 (unpublished data).To assess the level of any convergent evolution, we used a simple assignment test to determine how often individual sequences resemble others flanking unrelated microsatellites of similar length see . Figure These analyses reveal a tendency for microsatellite flanking sequences to be similar to each other, but fail to quantify the level of sequence change involved. To do this, we sought to estimate similarity among three classes of sequence: (1) blocks of 50 bp lying immediately adjacent to a microsatellite; (2) blocks of 50 bp chosen randomly to lie between 500 and 600 bases downstream from a microsatellite (the random selection aims to remove possible complications of exact position and phase with the microsatellite); and (3) blocks of 50 bp randomly selected from around the genome. Comparisons within class 3 define the average level chance similarity in the genome, here estimated at 12.77 ± 3.28 (sd) bases out of 50. Comparisons within class 1 estimate how much convergent evolution is apparent at any given repeat number, and reveal a profile that rises to a maximum of 14.31 at a length of seven repeats, followed by a gentle decline with increasing length thereafter A. SimilaThus, in all cases, sequences immediately flanking a microsatellite show greater similarity to each other, to sequences nearby, and to sequences elsewhere in the genome than randomly selected sequences do to each other, a trend that is maximal for microsatellites around 7–10 repeats in length. We believe these similarities are generated primarily by the enhanced simplicity of microsatellite flanking regions and their tendency to gain AT motifs. Such characteristics allow unusually high matches when compared with random blocks of 50 bases that have high simplicity or contain polyA tracts. Over and above this background level of elevated similarity, proximity to any microsatellite appears to increase similarity, implying that microsatellites tend to lie more generally in regions of similar base composition. This might reflect either a tendency for microsatellites to arise preferentially in certain broad sequence contexts, or modification of the local base composition by the microsatellite itself. Moreover, similarity is further enhanced when a flanking sequence is compared with a neighbouring block. This suggests a local context effect such as might arise through the isochore structure of the genome, with neighbouring blocks being located in the same isochore and hence ‘coloured’ by the same nucleotide biases.2 flanking sequences to their own class is significant up to ten bases away from the microsatellite , and only when the window reaches 24 bases from the microsatellite does the assignment level fall to the value of 4.8% expected of random sequences.To define more precisely the regions where convergent evolution is occurring, we repeated the assignment test but instead of using the full 50 bases either side we now used a symmetric pair of moving 25-base windows placed either side of the AC microsatellite . Close tftp://ftp.ncbi.nih.gov/blast/executables) to compare microsatellites identified in completed sections of the chimpanzee genome against homologous loci identified in humans. If homologous loci are identified through comparisons among their immediate flanking sequences, an element of circularity will be introduced, since loci with lower rates of evolution will be identified preferentially. To circumvent this problem, we used a region 300 bases in length and 220 bases downstream of the microsatellite to conduct each Megablast search.So far we have considered only the nature of the mutations that affect flanking sequences, and not their rate. To examine whether mutation rates are affected by the presence of a microsatellite, we used Megablast ; A total of 8218 chimpanzee loci were identified, and they yielded 5537 unique human homologues. Since microsatellite flanking sequences may contain insertions or deletions, we adopted the following approach so as to minimise problems of alignment. The chimpanzee flanking sequence was divided into 20 contiguous blocks of 20 bases, ten on each side of the microsatellite. Each block was then compared against 1,000 bases of human sequence, downstream from the region identified by Megablast, to find the best possible match. To filter sequences with major rearrangement we required both that all 20 blocks match with at least 15/20 bases, and that matched blocks all lie in the same order as their homologues. Differences between pairs of homologous flanking sequences were then quantified, at each of the 20-block positions, as the proportion of perfectly matching blocks and the average number of matches within each block .2–3 there is little apparent variation in either measure of similarity with block position apart from a small tendency for 5′ blocks to show an increasing average percentage match closer to the microsatellite. However, although the trends are by no means strong, around longer microsatellites similarity of blocks near to the microsatellite is reduced, particularly when measured using average percentage matches , and among these, approximately 77% of the blocks were identical. Around (AC) matches .Our analysis of dinucleotide frequencies around microsatellites shows patterns of similarity on a smaller scale than might be revealed with 20-base blocks of sequence. To investigate the number of changes in the immediate flanking bases, we also calculated the proportion of mismatches occurring at a given base for each 5′ and 3′ sequence in the immediate flanking blocks 9, but appears present even around sequences as short as (AC)2 and may extend as many as 50 bases either side. Some patterning is more or less symmetrical, but we also found several examples showing strong 5′ to 3′ asymmetry, implying that the two ends of a microsatellite are by no means equivalent. The net result is that sequences flanking microsatellites of a given length tend to be more similar to each other than to random sequences or to sequences flanking microsatellites of different lengths. Thus, there appears to be convergent evolution. Finally, we compared large numbers of homologous flanking sequences between humans and chimpanzees, and found evidence that mutation rates near microsatellites tend to be somewhat elevated.We have studied very large numbers of (AC)n tracts exhibit remarkable levels of patterning, with any given dinucleotide motif tending to be much more likely to occur at even numbered positions rather than odd, or vice versa. For several reasons, we believe that the patterning arises due to the structural properties of the microsatellite (see below), becoming more pronounced as repeat number increases. These reasons include the following: the consistently central placement of microsatellites within the patterning, the dependence of the strength of patterning on AC repeat number, the similarity between microsatellites in LINE and SINE elements and those elsewhere, the weakness of the patterning around (AC)2, and the strong influence of cassette type on the form of patterning. Unfortunately, it is surprisingly difficult to eliminate the alternative hypothesis, namely that the patterning arises due to some other force and that AC repeats then either form or expand more rapidly when placed centrally within the pattern. This ambiguity is particularly relevant to the question of cassette distribution, where it seems reasonable both that (AC)n tracts might cause biased interconversion between cassettes and that certain cassettes may allow slippage more than others. For example, while the structural properties of AC repeats are known to generate mutational biases in adjacent bases (Sequences surrounding (AC)nt bases  capable nt bases .2 already showing a small but significant bias in the distribution of cassette types and greater similarity to other sequences flanking AC microsatellites than to random sequences. In addition, the moving window assignment test indicates that significant convergence exists even when the ten bases closest to the microsatellite are excluded. Such a wide influence around such a common, short motif is remarkable and suggests that a high proportion of the genome may be affected by these and similar forces. To illustrate, (AC)2 is expected to occur every 250 bases, as is (GT)2. Taking the sphere of influence on each side as ten bases plus half the 25-bp window yields a value of 45. This predicts that over 30% (approximately 45 bases of every 125) of the genome will be affected by (AC)2 on one strand or the other, a figure that will only increase with inclusion of longer arrays and other microsatellite motifs.The relationship between an AC microsatellite and its flanking sequences begins surprisingly early, with (AC)5 and peaking in strength at (AC)9. Although patterning is seen in several different dinucleotide motifs, even in the human genome there are insufficient data to study any but the commonest cassette–motif combinations over a wide range of microsatellite lengths. Focusing on the motif AT, we found evidence that the strongest patterning was due to the development of AT microsatellites abutting AC tracts. However, this is not the only effect. After removal of all (AT)2 or longer microsatellites, there remains a significant tendency for single AT motifs to appear in phase with AC tracts, suggesting that mutation bias as well as slippage is involved.As AC repeat number increases, so does the strength of patterning, becoming pronounced by (AC)2 and (AC)9, it might seem logical that the pattern would become stronger and stronger as repeat number increases further. Instead, (AC)9 appears to be the peak strength, with longer microsatellites showing lower amplitude but a broader spread of patterning. It is interesting that this peak coincides with the length at which microsatellites begin to become polymorphic: a common rule of thumb for marker development in mammals is that primers are designed for loci carrying ten or more repeats (Given the increase in strength of patterning between (AC) repeats . This ma repeats . Again,  repeats .The rich patterning we find presumably arises through local mutation biases. Previous work on mutation biases has tended to reveal either generic effects such as isochors , where sn and (A)n induce local mutation biases  or A to Y (pyrimidine) transversion mutations, and also the increase in pattern strength around longer microsatellites, with interruptions in longer arrays more likely to be internal to the repeat tract and hence be excluded from the analysis. However, we suggest that this model is unlikely for two reasons. First, such a model fails to accommodate the strong asymmetry in patterning that is observed for some dinucleotides and specific cassette bases around the (AC)n repeat tract. Polarity has been noted for minisatellite mutations, with mutational processes differing between the two ends of the repeat tract at tract , but a mat tract .The patterning we describe appears to represent an important component of the forces that shape genome evolution, both in terms of its ubiquity and the absolute strength of its effect. It follows that there are many possible practical and theoretical implications. For example, even very short microsatellites appear able to cause some level of convergent sequence evolution, and hence to confound phylogenetic analyses. Similarly, microsatellites near genes may increase local mutation rates and influence the spectrum of new mutations that arise. To explore the size of these effects we designed experiments both to measure absolute convergence and to ask about evidence for changes in mutation rate.7–10 both for assignment to their own class and for similarity to each other relative to random blocks provide a clear indication that convergent sequence evolution is occurring. Interestingly, any given flanking sequence tends to be more similar to a block 500 bases away than to a similarly placed block near a different microsatellite, suggesting longer range patterning such as might arise through placement within the same isochore isochore . FurtherIn conclusion, previous studies of microsatellite flanking sequences have identified several features, including a tendency to harbour other microsatellites, a locally increased mutation rate, and, conversely, conservation over unexpectedly large tracts of evolutionary time. Our analyses support all these trends and provide a possible resolution for the apparent contradiction between faster evolution but at the same time greater sequence conservation. Although there is evidence that mutation rates near microsatellites are elevated, we also find evidence of convergent evolution. Consequently, the increased rate of change may be to some extent neutralised and perhaps even reversed by the tendency for similar changes to occur in related lineages. Furthermore, the greatest changes appear to occur in flanking sequences around microsatellites that are below the length used as markers, at least in humans. Overall, therefore, we have been able to formalise previous anecdotal evidence and hence to document a remarkably widespread source of directional change and nonrandom evolution that undoubtedly plays an important role in shaping the make-up of our genomes.n dinucleotide repeats was extracted from the human genome  using a custom macro written in Visual Basic. Only microsatellites separated by at least 100 bp from the nearest (AC)2 or longer were included in the dataset. Thus (AC)2AT(AC)10 would not be included in the dataset, whereas ACAT(AC)10 would be included as (AC)10. Flanking sequences are here defined as the 50 bases lying either side of a microsatellite. No attempt was made to translate TG repeats with complementary AC repeats on the opposite strand. Consequently, all our microsatellites are 5′-(AC)n-3′.Our dataset of (AC)n < 200 observations) or a normal approximation (n ≥ 200 observations). Bases used to define cassette type were excluded from all calculations, and expected frequencies were taken as the average frequency across all positions.For each frequency estimate, 95% confidence intervals were derived based on the binomial distribution (5 and declined to 175 for (AC)20 C)20 see .As an index of similarity, we calculated the log likelihood of observing a given sequence based on its position-specific dinucleotide motif composition:ijkf is the frequency of dinucleotide i at position j (j ≠ −1 or 0, with position 0 including the microsatellite and its cassette bases) in flanking sequences of class k. To avoid bias, when a sequence was compared with its own class, its contribution to the dinucleotide frequencies was first removed. For each sequence in turn, A was calculated for every class and the sequence was then assigned to the class that yielded the highest index value. Under convergent evolution, we expect sequences to tend to be assigned to their own or similar length classes.where Sequences were again divided into length classes 2 to 21, and each sequence contributed four 50-bp blocks of sequence, one from each side immediately adjacent to the microsatellite but excluding the cassette bases (class 1), and one from each side displaced by a randomly selected number 500–600 bases distal (class 2). In addition, we also generated a database of 5,000 non-microsatellite-associated sequences. When making comparisons within a class, nonindependence was avoided by randomising the sequence order and then comparing sequence 1 with sequence 2, 2 with 3, …, (n − 1) with n. Our index of similarity was simply a count of the number of matching bases. A few pairs of sequences (less than 0.1%) gave high similarity scores of over 30/50 matching bases, presumably because these loci have been duplicated or lie in repetitive elements. Such sequences were discarded. As with all other analyses, sequences containing base ambiguities (marked base N) were also discarded.n repeat microsatellites were extracted from the available chimpanzee finished-quality high-throughput genomic sequence (NCBI) as outlined above for humans. A 300-base region 220 bases upstream from each chimpanzee microsatellite was used by Megablast (Win32 version 2.2.6, NCBI) to identify homologous human loci in the finished genome sequence. Sequences with multiple high-scoring hits were discarded, as they presumably occur because a locus is found in repetitive elements or has been duplicated. Those nonoverlapping hits with at least 280/300 matching bases and an expectation  greater than five times that of any other hit to the same sequence were thus retained, giving a dataset of 5,537 sequences.(AC)Figure S1From little structure of any kind (A) to complicated aperiodic clustering (C). Plots are as described in (1.7 MB TIF).Click here for additional data file.Figure S2Plots are as described in (3.0 MB TIF).Click here for additional data file.Figure S3Figure depicts equivalent patterns of asymmetry in AT dinucleotide frequencies for the commonest cassette type, (T/A), around microsatellites in unique sequence DNA (A), LINE/L1 elements (B), and SINE/Alu elements (C). Plotting conventions are the same as for (1.4 MB TIF).Click here for additional data file.Figure S4Block position is relative to the central microsatellite (not shown).2–3 microsatellites (n = 4,593).(A and B) Proportion of exact matches  and average number of matches, excluding exact matches (± standard error), with block position around (AC)4+ microsatellites (n = 356). Average proportion of exact matches and number of matches, calculated separately for 5′ and 3′ blocks around (AC)2–3 microsatellites, are shown by a black line in (A) and (C), and (B) and (D), respectively. Average percentage match rather than average match is plotted in (B) and (D) because overlapping blocks were truncated to exclude overlapping regions from the analysis, with the result that not all blocks contained 20 bases.(C and D) As (A and B) but for (AC)(2.9 MB TIF).Click here for additional data file.Figure S52–3 microsatellite loci (A) and over (AC)4+ microsatellite loci (B). Shown ± standard error. The microsatellite at base position 0 is not shown. Expectation, calculated separately for 5′ and 3′ sequences around (AC)2–3 microsatellites, is shown by a black line in both plots.The proportion of mismatches occurring at a given base in a flanking sequence are averaged over (AC)(3.4 MB TIF).Click here for additional data file.


PLoS Biology, volume 1, issue 1:In The Roles of APC and Axin Derived from Experimental and Theoretical Analysis of the Wnt PathwayEthan Lee, Adrian Salic, Roland Krüger, Reinhart Heinrich, Marc W. KirschnerDOI: 10.1371/journal.pbio.0000010fluxes and flux appeared without the fl.Table 1: In the legend, the words coefficients appeared without the fi. In the table, some numbers in the section “Binding, dissociation” were marked with a ± sign that should have been a ∓.Table 3: In the legend, the word coefficients appeared without the fi.Table 4: In the legend, the word Please see the corrected legends and table below.Table 1. Numeric Values of Input Quantities of the Model for the Reference Statev12 = 0.42 nM · min−1 (rate of β-catenin synthesis), v14 = 8.2 · 10−5 · nM · min−1 (rate of axin synthesis), k4 = 0.27 min−1, k5 = 0.13 min−1, k6 = 9.1 · 10−2 nM−1 · min−1, k−6 = 0.91 · nM−1 · min−1, k9 = 210 min−1, k10 = 210 min−1, k11 · 0.42 min−1, k13 = 2.6 · 10−4 min−1, k15 = 0.17 · min−1. See Table S2, found at http://dx.doi.org/10.1371/journal.pbio.0000010.t002, for more precise numbers used in the calculations.The data are grouped into concentrations of pathway components, dissociation constants of protein complexes, concentration ratios, fluxes and flux ratios, and characteristic times of selected processes. Experimental evidence for these data is discussed in the text. From these data, the following rates and rate constants are calculated: Bold: Measured values, Italics: Estimated values.DOI: 10.1371/journal.pbio.0000010.t001Table 4. Concentration Control Coefficients for the Total Concentrations of β-Catenin and Axin Relative to Changes in the Concentrations of Pathway ComponentsThe control coefficients were obtained by numerical determination of the response to a change of total concentrations by 1%. Coefficients are given for the reference state and for the standard stimulated state.DOI: 10.1371/journal.pbio.0000010.t004The full text XML and HTML versions of the article have been corrected online. This correction note may be found at DOI: 10.1371/journal.pbio.0020089.


This article reviews 19 studies (1987–2004) on quality of life for family caregivers helping those with chronic physical illness. Here we explore the concepts of and instruments used to measure caregivers' quality of life. We were particularly interested in understanding stress-related variables and documenting factors influencing quality of life based on family stress theory. Findings show that various positive and negative terms equated with quality of life were used to measure them. Results indicate that stress-related variables as possible predictors influencing caregivers' quality of life include: patient and caregiver characteristics, stressors, stress appraisal, stress coping methods, and social support. Our recommendations touch upon applying theory for intervention, developing measurement, making operable the concepts for measuring, and the need for longitudinal and comprehensive study. Recent reforms in U.S. health care systems mean that individuals with long-term, complex health problems are being cared for at home by family members . Specifiquality of life – may be partly influenced by other existing environmental stressors, stress appraisal, coping methods and social support.Existing studies document how caring for chronically ill family members or significant others at home influences multiple aspects of caregivers' lives. These effects are physical, psychological and social and may include worsened physical health, impaired social and family life, and increased stress, anxiety and depression -9). Plac. Plac9])The importance of family stress theory in studying normative family transitions and adaptation to major life changes and illness is based on the central role that family strengths and capabilities play in understanding and explaining psychological and behavioral outcomes . In famiQuality of life is a construct that encompasses health and functioning, socioeconomic status, psychological, emotional and spiritual aspects, and family . FerrellTo address the lack of review papers on this topic as well as inconsistent results among empirical studies, we undertook a thorough review of the literature. We were particularly interested in research that reported psychosocial or QOL outcomes and accounted for family stress variables as correlates or explanatory variables. The purpose of this paper is twofold: to review caregiver QOL impacts (loosely-defined) organized around family stress theory, and to discuss the implications of findings for future research (i.e., measurement, hypothesis testing, refinement of concepts and constructs).Only published peer reviewed research articles were included in this review. Several methods were applied in searching the literature. First, articles were limited to those published in English between January 1, 1987, and January 31, 2004. Second, participants were caregivers of patients over 21 years of age with chronic physical illnesses. Third, a study was excluded if patients were receiving hospice care. Fourth, a computer search was conducted in February 2004 to review the databases of MedLine and PubMed by using the following key words: caregivers, caregiving, chronic illness, quality of life, adjustment, life satisfaction, burden, distress, and family stress theory. The terms "health," "stress," and "coping" were also used for study retrieval. Databases also were searched for review articles published during the same time period. Additional sources for empirical reports included reference lists from published studies. Of the more than 220 articles identified, there were only 16 empirical studies based on family stress theory that focused on the caregivers of patients with chronic physical illness and measured the QOL of caregivers. Two additional articles were identified from searching review articles. Finally, one more article was found in the references of these 18 articles. Thus, a total of 19 articles are included in this review (Appendix 1 See ).Reviewed studies used diverse outcomes and models to indicate caregivers' QOL. Psychosocial outcomes falling with varied dimensions of QOL and measured included adaptation, mental health, life satisfaction, stress, emotional distress, health, caregiver burden, and depression. Given the variety of terms equated with QOL, both positive and negative terms were used to measure the QOL. Three studies ,13,21) u,21 u13,2per se: the Caregiver Quality of Life Index containing items related to physical, emotional, social, and financial wellbeing [Of 19 reviewed articles, only one used a single measure of QOL ellbeing . Eight pStudies of variables influencing caregivers' QOL are summarized in Table Nine articles examined the association between patient characteristics (including performance status, age, gender, depression, type of illness, pain, and symptoms) and caregiver QOL. Generally, there were significant correlations between caregiver QOL and the patient's physical and emotional characteristics as related to the illness. Seven studies  repo repo24])Eleven articles investigated the relationship between stressors and caregiver QOL, but they did not show consistent results. To varying degrees, caregiving demands, patient impairment, the duration and intensity of care, ADL (activities of daily living) dependency, stressor types, caregiver overload, how much assistance is given the caregiver, recurrence of illness and problem behavior in the patient were identified as primary stressors. Secondary stressors were caregiving demands, role change, responsibility, caregiver experience, and life-style interference. Seven studies  f,25 f15,2Haley et al.  found thOf the four coping strategies, Wallhagen  discoverSix articles examined the association between stress appraisal and caregiver QOL. Two ,14) demo demo14])Haley et al.  found thIn contrast, Wallhagen  did not Miaskowski, et al.  investigA majority of the articles addressed social support as a mediator and predictor variable. Six articles . ,28. 13,2For a number of reasons, we need longitudinal studies in research applying family stress theory to caregivers of the chronically ill. Otherwise we cannot understand longer-term or sustained effects. First, although a patient with chronic illness may have completed treatment, recurrence of the illness is possible. Second, coping methods and appraisal may change depending on the circumstances, because family stress theory is dynamic and influenced by both internal and external environments. A longitudinal study may see the attrition of participants over time. Due to the nature of chronic illness, many patients may have died or experienced recurrence at the follow-up evaluation. Therefore, caregivers of these patients need to be followed through the end to assess predictors and consequences of patient death and recurrence of illness.Family stress theory is very complex and comprehensive. As a result, it is difficult to conduct research using the full model. Examples of this complexity include interactions among various factors, interactions and transactions over time, individual dynamics within the family system, and the balance between family demands and the family. Considering these multidimensional aspects, it would take much hard work to test the full model of family stress theory. However, we need to try. Otherwise, we may never understand the complex relation between individual, family, and environment.Appendix 1: Review of empirical findings on quality of life of family caregivers. Author, topic, significant predictor variables, mediating variables, outcome variables, measurement, and intervention of 19 reviewed articles are shown in detail.Click here for file


Retrovirology's choice for best basic science "retrovirus paper of the year" and a perspective on challenges and responsibilities facing HIV-1 and HTLV-I research are presented.In this editorial,  Retrovirology concluded its first calendar year of publishing. In actual fact, Retrovirology launched as an Open Access journal the final week of February 2004 and has been publishing continuously for a little more than 10 months. Over that period, with the wonderful efforts from my 6 very capable Associate Editors (Monsef Benkirane, Ben Berkhout, Masa Fujii, Mike Lairmore, Andrew Lever, and Mark Wainberg), the journal has thrived.The beginning of a year provides an occasion to look back upon progress made over the past 52 weeks. With the end of 2004, Retrovirology is to provide a visible forum for retrovirologists so that their works can be read by all in a free and openly accessible manner. What this means is that if you are a human immunodeficiency virus (HIV)-researcher and you had published a paper in Retrovirology, a graduate student in Sri Lanka updating his/her research protocol, an AIDS activist in South Africa looking for the latest information, and even your long-lost high school sweetheart wondering what you have been doing all these years, can all find your work (i.e. through a simple Google or PubMed search) and read your most recent findings. Perhaps more relevant to the enterprise of scientific communication is that numerous academic peers in Eastern Europe, Asia, South America, Africa and elsewhere do not have funds which would permit them to read Cell, Science or Nature. Hence, while some can read your research in Cell, Science, or Nature, all colleagues, rich or poor alike, can read your Retrovirology paper.The goal that we set for Retrovirology? You bet! Our monitored statistics tell us that in 2004, the most highly accessed review article [Retrovirology was read by over 3,500 individuals while a comparably popular original research article [Retrovirology articles with great immediacy. Thus, a recent paper by Rana and colleagues [Retrovirology on December 27th, 2004; and already by December 31st, 2004, a short 4 days later, that study had been read 389 times. Just as readers are quick to read our papers, I am equally pleased by our unmatched speed in publishing authors'works. In 2004, based on all papers we published in Retrovirology, the time from submission to publication averaged 40 days. From my personal experience of publishing in other virological journals, this duration is 3 to 4 times faster than our best competitors.Are they reading  article  publisheRetrovirology will initiate the annual recognition of the best basic science "retrovirus paper of the year". The Associate Editors and I decided that in 2004, the best basic science retrovirus paper was the work from Joseph Sodroski and colleagues describing the HIV-1 restrictive property of the tripartite motif 5 (TRIM5) protein [Retrovirology Editors will find it fitting to recognize a Retrovirology paper as the "best basic science retrovirus paper" of the preceding year.Different journals/magazines recognize "Molecule of the Year", "Breakthrough of the Year", or even "Person of the Year". With this editorial, I explain to my postdoctoral fellows that challenges are those issues which you think others should solve, while responsibilities are items that you think you should tackle. As a retrovirologist depending on how you regard yourself, pressing problems are either others' challenges or your responsibilities. I study two retroviruses, HIV-1 and human T-cell leukaemia virus type 1 (HTLV-I). The start of a new year offers me a chance to review briefly my personal bias on the important research question that confronts HIV-1 and HTLV-I, respectively.For HIV-1, the "holy grail" remains the development of an effective vaccine against the virus. As we enter 2005, mortality from AIDS is staggering. It is estimated that in 2004, 3.5 million individuals perished worldwide from AIDS; or nearly 10,000 AIDS deaths each day. We can view this number in another way. The recent tsunami in South Asia is estimated to have caused 150,000 fatalities. AIDS in 2004 is then the equivalent of 23 tsunamis. Imagine, unrelentingly tsunami-like casualties every fortnight from people dying from HIV-1! While it is laudable that the World Health Organization has a goal to treat three million HIV-1 positive individuals globally using anti-retroviral (ARV) medicine over the next five years, that approach will unlikely address the full magnitude of the AIDS problem, especially in developing nations. On the other hand, 100 million infants (even those in remote regions of the world) receive basic vaccinations each year. This fact suggests that when an AIDS vaccine does become available, that vaccine could be logistically and practically effective.in vivo.Separately, statistics from the World Cancer Report for the year 2000 show that 5.3 million men and 4.7 million women developed malignancy, and altogether 6.2 million persons died from cancer worldwide. The American Cancer Society estimates that approximately 553,000 individuals succumb to cancer in the United States each year. I see HTLV-I, the etiological agent for adult T-cell leukaemia (ATL), as perhaps the best retroviral system for retrovirologists to study human cancer. Substantive progress has indeed been made in our understanding as to how HTLV-I transforms cells in tissue culture [Retrovirology, I am also optimistic that I started this project at an age that provides ample time to develop this journal into a premier research forum.I first started studying viruses in the fall of 1977 at age 19 as a MD-PhD student at the Johns Hopkins school of medicine. For me personally, 2004 marked over a quarter-century of virus-research. At age 46, the unbridled youthful optimism of 19 is now tempered by realizations of physical and career limitations . Nonetheless, I am optimistic and hopeful that, despite enormous odds, the opportunity to see a successful AIDS vaccine will come before I leave retrovirology research in 20 or some years. Regarding Retrovirology's progress in our first year.Let me conclude this writing by thanking all authors, reviewers, Editorial board members, and our wonderful staff at Biomed Central who have contributed to


Haematuria is a classical symptom of urological disease often signifying a primary bladder cancer. Rarely, however, the presence of blood in the urine can be due to secondary spread of tumours into the bladder from distant sites. Notably this has been reported to occur in breast cancer, malignant melanoma and gastric cancers. Haematuria due to spread from a primary oesophageal cancer to the bladder has never been reported. We present a case of haematuria confirmed histologically to be due to metastases from a primary oesophageal tumour. Oesophageal cancer is capable of spread to all three neighbouring compartments (abdomen, chest and neck) and therefore has the potential to spread to unusual sites. Clinicians should always carefully regard haematuria in a patient previously treated for cancer and retain a high index of suspicion for distant metastases as being the cause. Haematuria is a commonly encountered symptom. It often represents the presence of serious disease such as a malignancy within the bladder. The majority of bladder tumours tend to be primary, and histologically these are usually transitional cell carcinomas. We present a case of haematuria which occurred due to metastases from a primary oesophageal carcinoma diagnosed 2 years prior and treated curatively.A 45-year old male presented to our unit with acute onset macroscopic haematuria. His past medical history was significant in that he had been diagnosed with adenocarcinoma of the distal oesophagus 2 years prior and had undergone curative resection after neo-adjuvant chemotherapy. At that time the tumour was found to be poorly differentiated with evidence of local nodal spread. He had been reviewed regularly by the oncologists and remained asymptomatic until the onset of frank haematuria.He subsequently underwent cystoscopy, which revealed a solid bladder tumour on the right lateral wall, which was treated with trans-urethral resection. Pathological examination confirmed a poorly differentiated mucus-secreting adenocarcinoma, identical histologically to the original oesophageal tumour Figures  and 2.A diagnosis of metastatic oesophageal adenocarcinoma to the bladder was made. A CT scan did not demonstrate any pelvic tumour outside the bladder and therefore metastasis by the trans-coelomic route was essentially excluded, indicating haematogenous spread of the primary oesophageal carcinoma. The patient was referred for further oncological therapy but unfortunately died 4 months later from disseminated carcinoma.Metastatic tumour spread to the bladder constitutes approximately 2% of all bladder neoplasms . Gross hThe bladder can be the recipient of metastatic tumour spread from a potentially large variety of primary sites. Most commonly direct invasion can occur from adjacent tumours of the lower gastrointestinal tract (33% of secondary neoplasms), prostate 19%) and female genital tract (11%) 9% and fe-5. The mDespite curative intent, surgical resection of oesophageal adenocarcinoma is associated with an overall tumour recurrence rate of 66% at 5 years .In autopsy studies, isolated lymph node metastases were found in approximately one half of patients with end-stage oesophageal carcinoma, with a similar proportion having combined lymph node and visceral metastases. Isolated visceral spread however is rare, accounting for only a handful of cases of primary oesophageal tumour spread .Notable sites of haematogenous dissemination of primary oesophageal carcinoma to distant organs include bone, liver, skin, lungs, adrenals, brain and peritoneum in descending order of frequency . A few aInterestingly, rare synchronous primary tumours of the bladder and oesophagus have been described ,16 but hHaematuria may be the only clinically apparent symptom of metastatic tumour spread to the bladder from a potentially large number of primary sites and therefore should be considered by all clinicians irrespective of specialty.The author(s) declare that they have no competing interests.