Cryptic structural abnormalities within the subtelomeric regions of chromosomes have been the focus of much recent research because of their discovery in a percentage of people with mental retardation (UK terminology: learning disability). These studies focused on subjects (largely children) with various severities of intellectual impairment with or without additional physical clinical features such as dysmorphisms. However it is well established that prevalence of schizophrenia is around three times greater in those with mild mental retardation. The rates of bipolar disorder and major depressive disorder have also been reported as increased in people with mental retardation. We describe here a screen for telomeric abnormalities in a cohort of 69 patients in which mental retardation co-exists with severe psychiatric illness.We have applied two techniques, subtelomeric fluorescence in situ hybridisation (FISH) and multiplex amplifiable probe hybridisation (MAPH) to detect abnormalities in the patient group.A subtelomeric deletion was discovered involving loss of 4q in a patient with co-morbid schizoaffective disorder and mental retardation.The precise region of loss has been defined allowing us to identify genes that may contribute to the clinical phenotype through hemizygosity. Interestingly, the region of 4q loss exactly matches that linked to bipolar affective disorder in a large multiply affected Australian kindred. The isolation of unique DNA probes from the sub-telomeric regions of all chromosomes has opened up a field of cytogenetics research that was previously inaccessible to conventional karyotyping protocols . Since tFISH, using a commercially available set of subtelomeric probes is the most commonly used screening technique ,20. VariDISC1/DISC2 [DIBD1 [GRIA3 [Psychiatric disorders such as schizophrenia (SCZ) and bipolar affective disorder (BPAD) are relatively common in the general population and there is much evidence for a genetic component to susceptibility (for a review see ). HoweveC1/DISC2 , DIBD1 [2 [DIBD1  and GRIA1 [GRIA3 . The chrThe risk of schizophrenia and affective disorders in patients with idiopathic mild mental retardation is significantly raised and it is well established that schizophrenia is three times more common in this group than the general population and that there is a strong familial element . Both biLocal research ethics permission was obtained for this study. The patients were initially ascertained through computerised psychiatric clinical case-registers that allowed us to identify adults with dual diagnosis of psychosis and mental retardation. A specific psychiatry service exists in Scotland to meet the needs of patients with mental retardation who also suffer from psychiatric disorders and initial clinical diagnoses were confirmed by consultation between the relevant specialist clinician involved and the research team member who is also a specialist in the psychiatry of mental retardation (WM). Confirmation that IQ fell within the mild range of mental retardation was obtained from case records.th Edition  and a referral diagnosis of co-existing schizophrenia or major affective disorder were studied. Parental samples were not available in many cases due to the age of the probands. This cohort is a subset of 74 originally ascertained subjects: 5 were removed because of aneuploidy (2 cases of 47(XXX)) or after more thorough psychiatric evaluation. One discounted subject with only mental retardation possessed a 6q subtelomeric deletion as determined by several MAPH probes (data not shown). The lifetime version of the Schedule for Affective Disorders and Schizophrenia (SADS-L ) along w (DSM-IV ). Diagno . In addition, 5 subjects were diagnosed as having a unspecified functional psychosis (DSM-IV 298.9, Psychotic disorder NOS).None had co-existing Down Syndrome or Fragile X disorder. A breakdown of the patients into their clinical categories and methodology of screening is presented in table DNA was extracted from venous blood samples (10 mls) of all patients by standard methods using Nucleon BACC2 kits (Nucleon Biosciences). 1 mg/ml dilutions were prepared for MAPH.All 57 MAPH samples were tested in triplicate using the subtelomeric screening set described previously . All samBlood samples from a subset of the patients were cultured in Peripheral Blood Medium (Sigma) for 72 hours. After colcemid treatment for one hour, lymphocytes were lysed and fixed in methanol:acetic acid (3:1). Fixed metaphase material was dropped onto microscope slides. Each slide was hybridised by three fluorescently labelled probe mixes (ToTelVysion, Vysis Inc.) under separate coverslips. All 15 mixes covering every subtelomeric region for a patient could thus be analysed on 5 slides. Images were captured on a Zeiss Axioskop2 microscope coupled to a Macintosh G4 computer running SmartCapture2.1 software . Five metaphases were scored for each probe mix.-3, corrected for multiple observations). It is unlikely that this copy number change (4q-) defined by MAPH represents an irrelevant polymorphism; no similar change was found on analysis of 83 unrelated control individuals, giving an upper (95% confidence) limit of 1.6% for the frequency of this variant.Complete accord was seen in the 12 instances where both screening methodologies were used. The FISH approach did not detect any subtelomeric abnormalities . However, MAPH identified a subject with a loss of one copy of the 4q subtelomeric region . P values for boundary probes , p < 5 × 10-5 for deletion and p > 0.05 for normal dosage. This 4q deletion encompasses a region of annotated genomic DNA of approximately 3 Mb. The transcript map of this region is not yet completely defined  offers a model of how a cryptic deletion associated with schizophrenia has highlighted several candidate genes for future study -35.DISC1 in psychiatric patients with (non-subtelomeric) chromosomal rearrangements. Therefore, the selection of the screening technique should be determined by the number of cases to be studied and the nature of the abnormalities expected.We used two methods to screen for subtelomeric changes in our cohort of patients. The MAPH technique has proved a fast and accurate method for determining copy number changes in the human genome and represents a cost-effective route to the screening of large numbers of patients. In addition, the disorders that can be studied by this approach are limited only by the design of suitable primers so that screening for both single and contiguous gene disorders is feasible. MAPH has also been proved to be a simple method to map deletion breakpoints with greater resolution than FISH. The subtelomeric FISH approach is a more demanding approach because of the patient sample preparation, the requirement for specialised microscopy equipment, the cost of the commercial probe sets and the labour involved. Nevertheless, FISH has some advantages: the FISH approach is the only way to detect balanced chromosome rearrangements such as inversions and translocations. Other studies of subtelomeric regions in mental retardation subjects have identified such chromosomal aberrations. In addition, FISH has been a vital technique for identifying disrupted genes such as de novo origin. However, psychiatric illness has not been diagnosed in other members of the family. We cannot, therefore, formally link the presence of the deletion with mental retardation and/or psychiatric illness in the patient. The annotation of transcripts at 4q35.2 is currently an active area of research . Consent was not forthcoming to determine whether the deletion was of parental or FRG1, for facioscapulohumeral muscular dystrophy. The 4q patient does not show any of the typical features of this disorder but this can be explained by the fact that copy number does not appear to be critical for the onset of the disorder [The subtelomeric region of 4q is also interesting because it contains a candidate gene, disorder . Rather,disorder .Of the 69 patients with clear co-morbidity, one (1.4%) possessed a single copy subtelomeric deletion. This frequency is in line with those from studies of individuals with mental retardation alone . As moreThe identification of the precisely delimited 4q deletion may contribute to the mapping of the susceptibility gene for psychiatric illness at this locus. The finding of bipolar affective disorder linkage to this region suggests that, in this case at least, the schizoaffective and mental retardation components to the co-morbid phenotype may be discrete and genetically separable in the manner of other contiguous gene disorders. If it is assumed that either component of the clinical phenotype is caused by haploinsufficiency then examining the comparative expression levels of candidate genes in normal, bipolar-linked and 4q deleted lymphoblastoid cell lines might provide a quick route to the identification of causative genes. Alternatively, gene association studies may be required to identify the candidate psychiatric illness gene at 4q35. The high rate of recombination in subtelomeric DNA means that a higher density of genetic markers will be required to establish linkage or association reliably; conversely, once detected, the high rate of recombination will allow high resolution fine mapping of significant loci.None declared.BSP participated in the design of the study and the FISH analysis and drafted the manuscript. EJH and JALA carried out all MAPH assays and associated statistical analyses. MPM carried out blood culture and FISH analysis. WJM conceived the study and was responsible for the generation of all clinical data, and DHRB and DJP participated in the study design and coordination. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


European scientists are pressing for the creation of an independent body to fund European research - driven by the pursuit of scientific excellence Science looks set for a fundamentalist revival within the European Union. Its leading proponents are taking advantage of unprecedented political upheaval—as ten new Member States accede to the Union—to press their case for funding of basic research that is driven solely and independently by investigators themselves in the pursuit of excellence.The broad thrust of their appeal calls for the setting up of a new agency, most commonly referred to as a ‘European Research Council’. The ERC could be an entirely new organisation or a new division within an established body, run by a small staff able to draw on the best expertise available. It would administer a new fund from EU coffers, tagged the European Fund for Research Excellence, that would be valued modestly, initially at least, at much less than half of the EU's existing budget for research. Most importantly, dispersal of that fund would reflect the wishes of eminent peer reviewers, assessing competitive bids in search of the best science, rather than the judgements of Eurocrats, looking for the most politically and economically expedient solutions and operating on a lead time of two years or more.juste retour, or fair reward, in which Member States traditionally seek to recover grants at least equal to their contributions to the EU pot . ‘Research activities at national and [European] Union level must be better integrated and co-ordinated to make them as efficient and innovative as possible, and to ensure that Europe offers attractive prospects to its best brains,’ concluded the EU leaders, eager to reverse the flow of trained talent abroad, notably to North America. All appropriate means, they added, ‘must be fully exploited to achieve this objective in a flexible, decentralised and non-bureaucratic manner’.Two years later, at the European Council in Barcelona, the EU leaders went one step further by defining the target more precisely. ‘In order to close the gap between the EU and its major competitors,’ they said, ‘overall spending on R & D and innovation in the Union should be increased with the aim of approaching 3% of GDP by 2010. Two-thirds of this new investment should come from the private sector.’The scale of the challenge is illustrated by the latest figures for R & D expenditure, published in February by the Statistical Office of the European Communities (Eurostat). The EU's estimated R & D spending in 2002 was 1.99% of GDP, still far short of the US (2.80%) and Japan (2.98% in 2000), and a long way from the target of 3%.Emphasising the UK's uneasiness about the EU's escalating enthusiasm for a regional science base, the Royal Society  poured scorn on the ‘ambitious’ GDP target by noting how the UK alone would have needed an extra £11 billion in 2000, or more than 60% of total spending on R & D, to lift its ratio of 1.85% to the 3% target. The Royal Society also noted how public funding of R & D in the EU matches that in the US and Japan, with the disparity among GDP ratios reflecting the differentials in private investment in R & D, over which the EU has little control.Nevertheless, the challenge could not be ignored. According to Bob May, professor of mathematical biology at the University of Oxford, president of the Royal Society, and former UK Chief Scientist, such initiatives might be ‘driven more by political expediency than common sense, but the moment you see that train beginning to roll, there's a chance to do something useful with it’.Among the leading proponents of an ERC is Bernard Larrouturou, director general of the National Centre of Scientific Research (CNRS) in Paris, France. For Larrouturou, a biomathematician currently engaged in streamlining the organisation, the changes at the European level are a breath of fresh air. However, he is not convinced that funded investigators should expect to exclude Commission strategists entirely from their lives. The scientific community should lead an ERC, says Larrouturou, ‘but I do not like the idea that this should be completely under the guidance and wisdom of the scientific community with no strategy guidance. You cannot ask for 1 or 2 billion Euros every year and say there will not be any strategy and [that it will be done solely] on this basis of excellence.’ And Larrouturou distances himself from the idea that basic and applied research can be treated separately because this suggests, wrongly he says, a conflict between the two.On these issues, Larrouturou moves onto some common ground with John Taylor, former director general of Research Councils UK and now chairman of Roke Manor Research, a UK subsidiary of Siemens, the German electronics group. Research Councils UK oversees spending of Britain's national research councils (currently, just over £2 billion from its 2004–2005 Science Budget of nearly £2.7 billion). Interactions across disciplines and between scientists and technologists ‘are not helped by making artificial distinctions between this kind of research and that kind of research,’ says Taylor. ‘The distinctions I make are much more between top-down and bottom-up.’While Taylor is a joint architect of one proposal to create an ERC, he remains unconvinced that the research funding system is broken, especially from the UK's perspective, and needs to be fixed. Nor is he convinced that EU funds for an ERC will not affect national R & D budgets. ‘I'm middle of the road,’ he says. ‘Much greater collaboration is good. It has to be a slow process, with all the different cultures involved. Collaboration on various areas of science is an excellent way to go, provided you don't try to organise it from the top and legislate for it all to happen in a particular way and to a particular timescale. Excellence is key.’Taylor's cautions reflect his experience of the EU's Framework Programme and his reservations that any initiative from Brussels can be free of red tape. ‘If you want to do research, then you can't lay out beforehand all the answers you're going to get,’ he says. ‘And if you try to get people to stick rigorously to a plan, then you get a lot of silly things going on. If you try to form very complex bureaucratic organisations to do the research, you get a lot of delays and so on, so things are not very timely.’But the Framework Programme's failures need not spell disaster for the fledgling funding council, insists Lennart Philipson, former director general of the European Molecular Biology Laboratory (EMBL) in Heidelberg, Germany, and now an emeritus professor at the Karolinska Institute in Stockholm, Sweden. Drawing on his 11 years as head of EMBL, until 1993, Philipson recalls how ‘pan-European peer review was the best method for distributing the funds of EMBL and EMBO [European Molecular Biology Organization]’. The continuing high status of the two organisations, he says, is testimony that the system works. In fact, EMBO is mentioned as a possible incubator for an ERC, in spite of its specialisation.Other proponents of the proposed research changes in the EU include 45 Nobel Laureates from Europe or of European origin, who headed a petition organised by EMBO. The European Life Scientists Organization (ELSO) organised another. Its president, Kai Simons, also the Director of the Max Planck Institute for Cell Biology and Genetics in Dresden, Germany, says research funding in Europe is just not working. ‘It's not geared for basic research—it has other aims,’ he notes. EU funds are ‘not grants, they are contracts with in-built milestones that have nothing to do [with basic research]. Basic research doesn't work like that.’The evaluation and peer review system is falling apart, continues Simons. He says that the best people are not interested in peer reviewing a system that doesn't work: ‘You're not attracting the peer reviewers that you need to maintain quality.’ But at last, acknowledges Simons, someone in Brussels is listening. ‘In the past two years there has been enormous progress.’Within a month of the Barcelona Council in 2002, the European Science Foundation (ESF), which brings together the funding agencies of 29 countries and acts as a bridge to Brussels, had formed a High Level Working Group to review the case for an ERC and how it might operate. The group, chaired by Sir Richard Sykes, Rector of Imperial College, London, United Kingdom, reported a year later, in April 2003. It endorsed the creation of an ERC as ‘the cornerstone for the ERA and the key approach to developing a locus for…long-term fundamental curiosity-driven research judged on the basis of excellence and merit’. The Sykes group also proposed, controversially, an enhanced ESF as the most effective medium for establishing an ERC swiftly.‘Some people say that the ESF has no experience in funding large amounts… for research,’ acknowledges Enric Banda, director general of the Catalan Research Foundation in Barcelona, Spain, who finished a five-year term as the ESF's chief executive at the end of 2003 and is credited with ‘waking up’ the foundation. ‘But certainly if you create a new [organisation], that's the same thing. So the ESF is in a good position because its member organisations are the funding agencies.’Bertil Andersson, who was a member of the Sykes Group before taking over from Banda at the ESF in January, also stakes the ESF's claim to nurture a fledgling ERC. But he accepts that any one of the respected national funding agencies, such as the German Research Foundation (DFG), or even a specialist body, such as EMBO, could do the job. ‘We don't need a new skyscraper in Brussels, but a lot of… peer review and running of the ERC could be done by existing bodies.‘Compared to soccer, we have only the national leagues—we don't have the Champions League [the league of Europe's best teams],’ says Andersson. There is no competition for basic research grants across national boundaries in Europe, he insists. ‘The Swedish league is exciting, but the Champions League is more exciting.’In the meantime, while the Sykes group was still deliberating, the Council of the EU appointed another group of experts to evaluate the case for an ERC. Chaired by Federico Mayor, former director general of the United Nations Educational, Scientific, and Cultural Organization (UNESCO), the ERC Expert Group also delivered its verdict—a resounding endorsement—within 12 months.‘The first and main task for the ERC should be to support investigator-driven research of the highest quality selected through European competition,’ concluded the Mayor report, published in December 2003. ‘In doing so, the ERC should create and support nodes of excellence in European universities and research institutions, strengthening the knowledge-base that underpins economic, industrial, cultural and societal development, and thereby stimulating European competitiveness and innovative capacity at all levels.’While few disagreed with the Mayor report's sentiments, the absence of a detailed analysis exposed underlying tensions over the rationale for an ERC. In the UK, in particular, some scientists seemed concerned that their mature and respected system for funding research risked dilution.‘The British have always had doubts about what goes on in Europe,’ notes Kai Simons. ‘They always think that they can do it better. But the big problem for the British is that they are also too small to fund a new innovative area,’ he says. ‘Of course, we can do it without Britain, but they are an important part of Europe and it would be sad if they're not part of it.’The agnostic John Taylor, who was a member of the Mayor group, recalls his early reservations when the group convened. ‘I'm way beyond the euphoria; I'm into practical pragmatics,’ he notes. ‘My major input into the whole thing has been to get them to “get real” instead of just philosophising. They've been using the sort of, dare I say it, Gallic approach… of thinking about the reasons why, and the philosophy, and not thinking about what you would actually do.’Taylor dismisses the notion that wariness of the ERC is representative of a general antipathy in Britain towards European integration. ‘What we're saying is that science in the UK is not yet well-funded enough to say we would rather do this [the ERC] instead of the things that we're already trying to get done in the UK scene.’Anticipating the Mayor report's publication, the Royal Society quickly pulled together a detailed background paper late last year that identified ‘a number of problems that need resolution, although not necessarily through the establishment of any major new institutions within Europe’. An addendum followed in March, in direct response to the Mayor report. That addendum highlighted what it saw as the paucity of solid evidence in the Mayor report and, in some cases, the confusing data in the report's case for an ERC.On balance it looked as though the Royal Society, and as such the British science establishment as a whole, had weighed the disadvantages of an ERC as greater than its advantages, but Bob May is quick to refute this charge. ‘My vision and the Royal Society's vision of the ERC is that it will fund the very best science,’ he insists. ‘The Mayor committee itself was really good people who'd produced basically a good report…. I'm basically in favour of this European Research Council… provided it can be set up properly, which is by no means certain.’For May, and other scientists on the continent, the ERC offers a real chance to redress the balance of fortune in favour of young scientists. ‘The way to encourage science is to get the best people and set them free to express their creativity while they are young, which means bring them into the best laboratories—don't let them get entrained in hierarchies of deference to second-rate people,’ says May.juste retour, whether they're serving some industrial purpose, it will just try to fund the best science. But I hope increasingly the best projects will involve collaborations, as they do in Britain, collaborations among institutions within Europe.’‘The most important single thing to create one Europe in science is a flexible postdoctoral programme that gets the best young people wherever they are and lets them go to the best places,’ enthuses May. An ERC will then foster those collaborations, he forecasts. ‘It won't ask whether they're Council of the European Union – Ruling organisation , and not to be confused with the European Council (see below). It comprises ministers from governments of the Member States, which have varying voting powers led by France, Germany, Italy, and the UK.Euro (€) – Common European currency launched on 1 January 2002 in 12 participating Member States (the UK, Sweden, and Denmark chose to postpone adoption of the currency indefinitely).European Commission – Executive organisation, mainly based in Brussels, run by 20 Commissioners and around 24,000 civil servants.European Council – Body that brings together leaders of Member States to define broad policy objectives for the EU's six main institutions (Parliament, Council, Commission, Court of Justice, Court of Auditors, and Ombudsman). Meets twice a year in the Member State holding the Council's presidency, which changes every six months.European Parliament – Elected organisation, based in Strasbourg, France, that rules the EU (jointly with the Council of the EU, see top) and will have 732 Members after the accession of the ten new Member States in May 2004.European Research Area – Commissioner Philippe Busquin's vision for the future of research in Europe, and the main focus of the 6th Framework Programme. It aims to achieve ‘scientific excellence, improved competitiveness and innovation through the promotion of increased co-operation, greater complementarity and improved co-ordination between relevant actors, at all levels’.European Union – Evolving political, social, and economic union of an increasing number of European countries, or Member States. First proposed in 1950 during rehabilitation after the Second World War and formally created by the Maastricht Treaty in 1992. Grew from six nations in 1951  to nine in 1973 (addition of Denmark, Ireland, and the UK), to ten in 1981 (addition of Greece), to 12 in 1986 , to 15 in 1995 (addition of Austria, Finland, and Sweden), with a total population of 380 million people . Ten more countries  join in May 2004, which will lift the EU's population to 450 million people. Bulgaria and Romania are due to join in 2007, which will add another 50 million people.Framework Programme – The EU's principal mechanism for funding research in Member States, proposed by the Commissioner for Research (Philippe Busquin) and adopted by the Council and Parliament. Framework Programmes have four-year budgets but cover five-year periods, so consecutive programmes overlap, and are prescribed two years before they begin. The 6th programme (FP6) is worth €17.5 billion  and runs from the beginning of 2003 to the end of 2006.Juste retour (fair reward) – Claim made by Member States for rewards at least equal to their share of the cost of any programme or initiative; critics say it promotes bureaucracy and uncompetitiveness.


Defensins are important components of innate immunity to combat bacterial and viral infections, and can even elicit antitumor responses. Clusters of defensin (DEF) genes are located in a 2 Mb range of the human chromosome 8p23.1. This DEF locus, however, represents one of the regions in the euchromatic part of the final human genome sequence which contains segmental duplications, and recalcitrant gaps indicating high structural dynamics.We find that inter- and intraindividual genetic variations within this locus prevent a correct automatic assembly of the human reference genome (NCBI Build 34) which currently even contains misassemblies. Manual clone-by-clone alignment and gene annotation as well as repeat and SNP/haplotype analyses result in an alternative alignment significantly improving the DEF locus representation. Our assembly better reflects the experimentally verified variability of DEF gene and DEF cluster copy numbers. It contains an additional DEF cluster which we propose to reside between two already known clusters. Furthermore, manual annotation revealed a novel DEF gene and several pseudogenes expanding the hitherto known DEF repertoire. Analyses of BAC and working draft sequences of the chimpanzee indicates that its DEF region is also complex as in humans and DEF genes and a cluster are multiplied. Comparative analysis of human and chimpanzee DEF genes identified differences affecting the protein structure. Whether this might contribute to differences in disease susceptibility between man and ape remains to be solved. For the determination of individual DEF gene repertoires we provide a molecular approach based on DEF haplotypes.Complexity and variability seem to be essential genomic features of the human DEF locus at 8p23.1 and provides an ongoing challenge for the best possible representation in the human reference sequence. Dissection of paralogous sequence variations, duplicon SNPs ans multisite variations as well as haplotypes by sequencing based methods is the way for future studies of interindividual DEF locus variability and its disease association. One obvious reason for these gaps is that the appropriate regions are enriched in sequences that are not tolerated by the cloning systems. The second possibility is that even if clones are available and amenable for sequencing, their sequences cannot be unambiguously aligned due to gap flanking segmental duplications. Generally, those duplicons are defined by >90% sequence identity and lengths of >1 kb and about 87% of all human ones are longer than 50 kb [Despite the tremendous efforts and successful completion of the Human Genome Project by April 14an 50 kb  and hamsde facto gaps [de facto gaps may be formed. In the case of unresolved segmental duplications, usually a large number of clones has been sequenced with high accuracy [It is a fact that structural variations between chromosomal haplotypes complicate the sequence assembly and lead to the formation of cto gaps ,8. The maccuracy  and the DEF cluster a contains the genes DEFB1, DEFA6, DEFA4, DEFA1, DEFT1, DEFA3 and DEFA5; DEF cluster b comprises the genes DEFB109p, DEFB108, DEFB4, DEFB103, SPAG11, DEFB104, DEFB106, DEFB105, and DEFB107  [DEFB4, DEFB103, DEFB104  [Interestingly, the DEF cluster region was identified as the distal breakpoint (REPD) of a 4.7-Mb segment inversion, identified as a common polymorphism with frequencies of 39% and 26% in the Japanese population and in Europeans, respectively -18. Althes each)  and by aes each) . It is gPan troglodytes), both revealed the differences in the defensin protein panel of both species and showed that DEF clusters are also multiplied in the ape.The polymorphic nature of this locus suggested to us that the pool of clones presented in the hg16 assembly should be aligned in a different way. Our alternative assembly creates more DEF cluster copies and better reflects the individual variability of the locus. In addition, comparative sequence analysis of DEF genes in our closest relative, the chimpanzee (Moreover, extraction of single nucleotide polymorphisms (SNPs) from overlapping regions of clones harboring DEF genes provided haplotypes which were analyzed for their ratio in individuals and used for the determination of individual gene copy numbers.DEF clusters b1 and b2, in our sequence assembly a consistent 360-kb-contig comprising five clones was built that also contains a DEF cluster b. Interestingly, this additional contig could neither be joined unambiguously to cluster b1 nor to cluster b2. This is in contradiction to the hg16 assembly where clones 13 and 14 of the novel contig are positioned in DEF cluster b1. This disagreement prompted us to evaluate carefully the alignment of the entire region. For this, we used all 32 finished clones which map in the region. To circumvent the problems of an automatic assembly in repeat rich, duplicated regions, the clones were manually joined according to the following criteria: only single base exchanges and insertions/deletions in repeat stretches were tolerated and joins were not performed if the total ratio of single base differences in the overlapping clone portions was >0.8%.In the framework of the International Human Genome Sequencing Consortium we sequenced 19 out of 32 BAC clones mapping to the 8p23.1 DEF region  Clones 23–27 not present in the hg16 assembly were located distally of the gap, whereas clones 11, 12 and 17 were excluded since they cannot be aligned according to our criteria. Clones 13 and 14 were moved to the new 360-kb-contig (see below).DEF cluster a. We found that clone 4 contains four copies of a DEFA1 / DEFTP tandem and one copy of DEFA3, whereas clone 5 harbors only two DEFA1 / DEFTP tandems and the DEFA3 gene  In the framework of the human and vertebrate analysis and annotation initiative (HAVANA) , where g In the f(3) Our assembly of the 32 finished clones mapping to the locus created an additional 360-kb-contig which consists of five clones representing an additional, third DEF cluster copy named b3. Since the repeat in clone 30 is located on the (-)-strand, the contig cannot be joined to any of the repeats at either side of the gap but instead has to be located within the gap in unknown orientation.In silico identification of these paralogs was in good accordance with fluorescence in situ hybridization (FISH) experiments using clones from the 8p23.1 DEF locus as probes  Fig. . The reps probes  and 4. FDEFA1 and DEFTP. The incomplete fifth copy harbors DEFA3. The content of interspersed repeats is low (20%); the nucleotide identity between the subunits is 99%. The repeat subunits of cluster I are unique to this locus and do not show significant homologies to other regions of the human genome.The 89.6 kb cluster consists of 4.7 subunits of a 19.2 kb repeat. Each complete subunit contains DEF clusters b, do not show substructures and do not contain genes. In contrast to repeat units of type I they differ in length and orientation, contain inversions and show a high degree of interspersed repeats (55%). The considerable length differences are caused by an initial partial duplication and/or subsequent deletions. The nucleotide identity of the repeats is 96–99% except for II.1, located adjacent to DEF cluster a, which exhibits an identity of ~92% to repeats II.2-6.Repeats of type II flank Blat search of the longest repeat (II.2, 82 kb) revealed that repeats of type II are also present at about 12 Mb on chromosome 8p23.1 as well as on other human chromosomes, e.g. 3, 4, 7, 11, 12 and 16. The paralogs vary in length (25–133 kb) and show nucleotide identities of 93–95% compared to II.2 on 8p23.1. On chromosomes 3, 4 and 11, they are arranged as inverted repeats. The distances in between "repeat pairs" vary remarkably: Whereas in 11q13.2-11q13.4 and 4p16.2-16.1 the parts are separated by 4 and 5 Mb, respectively, the repeats on chromosome 3 are located in 3p12.3 and 3q13.31, framing 51 Mb on both arms of the chromosome.DEF clusters b, they differ in length and orientation and consist of various numbers of 7.7 kb-subunits showing a low content of interspersed repeats (17%). The nucleotide identity of two subunits of a single type III cluster is >99%, whereas the identity of two subunits of different clusters is in the range of 96–98%. Each of the 7.7 kb-subunits harbors one copy of a gene for the hypothetical protein FLJ10408 [GenBank:NM_018088]. The coding sequences of the gene copies differ and in some cases the reading frame contains premature termination codons. Blat search of the type III repeat subunit revealed three paralogs in the human genome. Two of these are located on chromosome 8p23.1 at about 12 Mb (flanked by repeats of type II), the third at 12p13.31. The paralogs are slightly rearranged in comparison to the subunits of III.1-5.Located adjacent to DEF clusters b .We manually inspected seven clones covering Pan troglodytes, ptr) whole genome shotgun (WGS) working draft , but trace data inspection indicates the presence of several different haplotypes. Additionally, ptrDEFB108 and ptrDEFB109p are not covered by any chimpanzee WD sequences. As an alternative to the WD approach, we sequenced for ptrDEF cluster b three BAC clones. Examination of SNPs in overlapping regions (104 kb) of the three clones [GenBank:AC150655], [GenBank:AC150656], [GenBank:AC150657] revealed three different haplotypes originating from one chimpanzee. The detected aa changes in human and chimpanzee defensins are illustrated in ptrDEFB108 is a pseudogene, since also the start codon ATG is changed into GTG.In order to compare the human chromosome 8p23.1 DEF region to the orthologous locus in our closest relative, we both employed the chimpanzee  to 1:7 (proband 1). Interestingly, this haplotype is also found in the trace archives of chimpanzee and baboon. Furthermore, ratios of the individual haplotypes of DEFB104 as well as of DEFB4 indicate different DEF cluster b numbers in the four individuals. While proband 3 bears five copies, proband 4 most probably harbors eight copies or multiples thereof.In order to determine individual DEF copy numbers we PCR-amplified a 500 bp fragment of ls Table . Three ode facto gap, it better reflects the region in the sense of a „human genome reference“, since the clones harboring copies of DEF clusters b derive from three libraries (RP11, RP13, SCb) and may therefore represent up to five alleles (library RP13 is represented by only one clone). Our assembly also reflects better the diversity of all available sequence data of this chromosomal region: 27 out of 32 finished clones are incorporated into the tiling path. The remaining five clones cannot be included in the assembly according to our quality criteria and therefore must be regarded as parts of additional copies or alleles. Furthermore we point out that the identification of a third copy of the DEF cluster b in the 360-kb-contig does not represent an allele of clusters b1 or b2 derived from an alternative library / donor, since besides four SCb clones one RP11 clone is incorporated. With respect to the RP11 library of which most of the DEF cluster b covering clones derive we conclude that at present sequence information of at least five variants of the cluster is available from a single individual: cluster b1 (clones 9, 10, 15); b2 (clones 16 – 20); b3 (clone 30); b4 (clone 11) and b5 (clone 12). All these results are in agreement with the reported interindividual variability of DEF cluster b genes [The manual clone-by-clone alignment and gene annotation as well as detailed repeat and SNP/haplotype analyses significantly improved the assembly of the human DEF 8p23.1 locus. Eventhough the revised alignment Fig.  does notDEF cluster a; Fig. DEFA1 / DEFTP tandems present in clone 4. Since both clones derive from the same library (SCb), we conclude that either copy 3 of the tandem was lost during the cloning process of clone 5 or the clones represent two different alleles of the same chromosomal locus. This perfectly agrees with the variation in the copy number of DEFA1 reported by Mars [Alignment and analysis of the intergenic distances of clones 4 and 5  and the two DEF clusters b (196 kb each) resulting in a SNP density of 1 SNP per 220 bp. Close examination indicates that the SNPs are arbitrarily allocated to the two DEF cluster b loci present in the hg16 assembly. Therefore, in such regions, only manual clone-by-clone inspection as performed during our assembly process provides a reliable set of SNPs for the determination of haplotypes. Human SNPs such as rs1800968 in DEFB1, affecting cysteins (C67S) might be of functional relevance, since the cystein connectivity is assumed to determine the correct fold of the defensins which is essential to elicit chemotactic responses as shown for DEFB103 [SNP detection and correct assignment to regions with segmental duplications is not trivial and hampered by paralogous sequence variations , duplico DEFB103 .ptrDEFA1 and ptrDEF cluster b were found as single copies. Familiar with the drawbacks of the WGS automatic assembly [ptrDEF cluster b according to a high quality standard and conclude that it is at least duplicated. This suggests that the DEF locus of the chimpanzee is probably as complex as in humans.In order to answer the question whether the extraordinary complexity of the DEF locus is human specific, we closely inspected the orthologous region of the chimpanzee WD. In contrast to the human organization, assembly ,32, we sThe exceptional genomic complexity and heterogeneity of the human 8p23.1 DEF locus and the prominent position of defensins in the innate immunity framework raise the question whether individual patterns of haplotypes together with their variable copy number affect the functionality of the defensin system. A similar situation is found for a chemokine gene cluster where an individually variable gene copy number of CCL3-L1 regulates the gene's expression and is supposed to affect the susceptibility to and progression of inflammatory diseases . Systemade facto gaps no essential elements of the DEF locus are missing in the human reference sequence. In comparison to its actual representation, our revised clone alignment clearly better represents the 8p23.1 complexity and improves the human reference sequence as an invaluable resource for the investigation of individual genetic variations. Finally, the presented work provides a set of SNPs and haplotypes as well as a robust sequencing based method suitable for future studies of interindividual DEF locus variability and its disease association.Complexity and variability seem to be essential genomic features of the major human DEF locus and of – yet unknown – functional significance for the innate immunity framework. This is supported by our human-chimpanzee genomic comparison. In conclusion of the presented repeat analyses we propose a model of the repeat and DEF cluster organization  that is Clone alignments were performed using the GAP4 assembly program, version 6 , using tLCRs were identified by application of the Miropeats program  to the rThe clones were identified by Blast of the revised alignment consensus to the chimpanzee BAC end sequence database . SubclonThe gene annotation was performed by using the automated sequence annotation system RUMMAGE  and ANA_DEFB4 GGCGATACTGACACAGGGTT (sense) and ATGGGGAAGGTCAAGGAATC (antisense) and for DEFB104 TTCTGTAGCCCCAACACCTC (sense) and GGTGCCAAGGACATCTAGGA (antisense), respectively. PCR products were cloned into PCRTopo2.1 (Invitrogen) and individual clones were sequenced as described above.Genomic DNA was extracted from the blood of four male volunteers using the QIAamp DNA Blood Kit (Qiagen). PCR was performed in a total volume of 25 μl using ReadyToGo PCR beads (Amersham) with 5 pmoles of each primer and 100 ng of DNA. Cycling conditions were 94°C for 30 sec followed by 35 cycles with 94°C for 20 sec, 58°C for 30 sec and 72°C for 60 sec, plus a final 72°C extension for 10 min. Oligos used were for BAC Bacterial artificial chromosomeDEF Defensinin situ hybridizationFISH Fluorescence GAP Genome assembly programHAVANA Human and vertebrate analysis and annotation initiativeLCR Low copy repeatNCBI National Center for Biotechnology InformationPCR Polymerase chain reactionptr Pan troglodytes (chimpanzee)SNP Single nucleotide polymorphismUCSC University of California Santa CruzWD Working draftWGS Whole genome shotgunST performed the assembly revision, the gene annotations, the LCR, SNP and chimpanzee sequence analyses and drafted the manuscript. PG performed mapping and repeat analyses in the 8p23.1 region, completed by IFL's FISH experiments. KR contributed to the sequencing process of clones in the region. AS, SA, NS, KS and MS accounted for the assembly revision and the LCR analyses. AF and RS supported the gene annotations and the DEF structure and function discussions. KH and KR were in charge for the individual haplotype and copy number determinations. MP conceived, designed and coordinated the project.Accession numbers, libraries and clone names of all clones shown in main text, Fig. Click here for fileLCR of clones SCb-561b17 [GenBank:AF238378]; green/yellow signals) and CTB-415D8 [GenBank:AF228730]; red signals) visualized by FISH on metaphase chromosomes according to standard protocols ,53. MetaClick here for fileResolving LCR type II and III "pairs" on chromosomes at approx. 900 band stage. Probe CTB-415D8 [GenBank:AF228730]; LCR type II and III) generates two clearly separated FISH signals at 4p16 and 8p23, respectively : In contrast, probe SCb-561b17 [GenBank:AF238378]; LCR type I) yield a single signal at 8p23, solely , that is co-localized with the telomeric signal of probe CTB-415D8 [GenBank:AF228730]. Signals with lower intensity are indeterminable in this picture.Click here for fileptrDEF cluster a) are deduced from the chimpanzee WD and might therefore include sequencing errors. Those for ptrDEFB1, ptrDEFB107, ptrDEFB105, ptrDEFB103, ptrDEFB4 and ptrDEFB108 (DEF cluster b) are derived from high quality BAC sequences and the appropriate traces were visually inspected. The gray shadow indicates the motif of six cystein residues (except for DEFB107 with only five cysteins).DEF aa sequences with highlighted residues (bold) different between human and chimpanzee. Boxes: human aa – human position – chimpanzee aa. All aa positions refer to the following human protein accessions: DEFA6 = [GenBank:NP_001917]; DEFA4 = [GenBank:NP_001916]; DEFA1 = [GenBank:NP_004075]; DEFA5 = [GenBank:NP_066290]; DEFB1 = [GenBank:NP_005209]; DEFB107 = [GenBank:AAM93909]; DEFB105 = [GenBank:NP_689463]; DEFB103 = [GenBank:NP_061131]; DEFB4 = [GenBank:NP_004933]; DEFB108 = [GenBank:AAN33116]. Aa for the chimpanzee orthologs ptrDEFA6, ptrDEFA4, ptrDEFA1, ptr novel defensin similar to DEFA4, ptrDEFA5 and ptrDEFB1 , individual loci may have incorporated variable numbers (F, R) of additional b clusters in either orientation. The proposed duplicon consists of two inverted LCRs flanking a DEF cluster b (top/bottom). The orientation of any DEF cluster b can change either by inverted duplication/crossover (i) or homologous recombination within inverted LCRs (x, right). Moreover, the proposed genomic structure indicates that even in a 'minimal' DEF locus one or both DEF clusters may be deleted due to homologous recombination between direct LCR copies (Δ). Sequence features of the most distal LCR  as well as LCRs type II (rectangles) are shown. Click here for file


The main goal in analyzing microarray data is to determine the genes that are differentially expressed across two types of tissue samples or samples obtained under two experimental conditions. Mixture model method (MMM hereafter) is a nonparametric statistical method often used for microarray processing applications, but is known to over-fit the data if the number of replicates is small. In addition, the results of the MMM may not be repeatable when dealing with a small number of replicates. In this paper, we propose a new version of MMM to ensure the repeatability of the results in different runs, and reduce the sensitivity of the results on the parameters.Hyp mice. In each study, the proposed algorithm successfully selects genes closely related to the disease state that are verified by biological information.The proposed technique is applied to the two different data sets: Leukaemia data set and a data set that examines the effects of low phosphate diet on regular and The results indicate 100% repeatability in all runs, and exhibit very little sensitivity on the choice of parameters. In addition, the evaluation of the applied method on the Leukaemia data set shows 12% improvement compared to the MMM in detecting the biologically-identified 50 expressed genes by Thomas et al. The results witness to the successful performance of the proposed algorithm in quantitative pathogenesis of diseases and comparative evaluation of treatment methods. Recently, microarray technology has provided the means for simultaneous screening and analysis of thousands of genes. Although an enormous volume of data is being produced by microarray technologies, the full potential of such technologies cannot be accessed without the ability to sift through the noisy signals to obtain useful information. The large data sets produced by microarray technology have resulted in the need for reliable, accurate, and robust methods for microarray data analysis. A major challenge is to detect genes with differentially expression profile across two experimental conditions. In many studies, the two sample sets are drawn from two types of tissues, tumours or cell lines, or at two time points during the course of a biological processes.The computationally simple methods used for such analysis, including the methods of identifying genes with fold changes (such as the popular Log-ratio graphs) , are knoThe major disadvantages of the above-mentioned methods, especially the MMM, include the lack of repeatability of the results under different runs of the algorithm, and the sensitivity of the algorithm on parameter initialization. A reliable microarray analysis method must be reproducible and applicable to different data sets under different experimental conditions. More specifically, an accurate microarray processing method is expected to pinpoint, with a relatively high degree of accuracy and robustness, genes with elevated expression levels that are related to the experimental condition in all runs. The main objective of this paper is to design and test an extension of the MMM whose results are reproducible, more biologically meaningful, and significantly less sensitive to the models' parameters.The paper is organized as follows. In Algorithms section, a review of the MMM and its recent extensions, Mod2MMM, together with the detailed description of the proposed method are given. In Results and Discussion section, the K5M algorithm is first applied to the well-studied Leukaemia data set  that is Yij as the expression level of gene in array i , where the first j1 and last j2 arrays are obtained under two conditions. A general statistical model for the resulting data is:We start this section with a brief review of the existing MMM based techniques. Consider Yij = ai + bixj + εij     (1)xj = 1 for 1 ≤ j ≤ j1 and xj = 0 for j1 + 1 ≤ j ≤ j1 + j2. In addition, εij is a random error with mean 0. From the above formulation, it can be seen that ai + bi is the mean expression level of the first condition, and ai is the mean expression level of gene i in the second condition. The method requires that both j1 and j2, the number of data sets for each experiment condition, be even.Where ai is a random permutation of a column vector that contains j1/2 1's and j1/2 -1's and bi contains j2/2 1's and j2/2 -1's.The t-test statistic type scores (2) and (3) are calculated on the pre-processed data. Here, f0 and f are estimated as in (4) and (5), respectively. The null distributions, f0 and f, are estimated directly in a nonparametric model for gene expression data.Since the data are not assumed to be normally distributed, the distribution functions φ symbolizes the normal density function with mean μi, variance Vi, and the mixing proportions πi define the linear combination of the normal basis function. We use Φg0 to represent all unknown parameters {(πi, μi, Vi): i = 1, ..., g0 } in a g0-component mixture model. The number of normal basis functions, i.e. g0 can be estimated by the EM algorithm, which maximizes the log-likelihood function of (6) to obtain the maximum likelihood estimation of .Where K iterations, the EM algorithm is expected to find the local maxima for all unknown parameters. It is recommended to run the EM algorithm several times with various random starting parameters and choose the final estimate as the one resulting the largest log-likelihood [Within kelihood . As ment for different g0 's, the algorithm selects the sub-optimal g0 corresponding to the first local minimum of BIC or AIC [After finding the optimized C or AIC .vg0 is the number of independent parameters in Φg0. Then, the algorithm uses the resulting g0 as the number of normal functions to fit f0. The same procedure is performed to estimate the sub-optimal number of normal functions to estimate f. As mentioned above, with the fixed number of normal functions, the parameters of functions f and f0 are iteratively updated for a number of iterations. When the iterations are terminated, the likelihood ratio is estimated based on the final estimations of f0 and f:where LR(Z) = f0(Z) / f(Z)     (9)s, if LR(Z) <s, then the gene is identified to have significantly altered expression in two experiments. It is possible to determine the rejection region numerically, i.e. for any false positive rate α, the threshold value s = s(α) can be calculated from the following integral:A bisection method  with a Bα = 0.01 is often used as the genome wide significant level, so the gene-specific significance level is: α* = α/(2n) Recently a new modification of the MMM algorithm, Mod2MMM hereafter, was introduced [z may be different from the null distribution of Z, which can lead to invalid inference. The modified algorithm starts with the assumption that j1 ≥ 2 j2 [z and Z as you can follow in appendix1.In literature of microarray processing, troduced . This me1 ≥ 2 j2 , and conj1 ≥ j2 but j1 < 2 j2, j1 observations drawn under condition one are split into two equally-sized parts to calculate , vi(1a) and , vi(1b) respectively. To calculate  and vi(2) about j1/2 observations are drawn under condition two. While this modification can address the differences in the distributions of f and f0, the stability of the parameter estimation step still remains a major problem.For the cases where z  may be different from the null distribution of the statistics Z (as defined below). This assumption can potentially lead to invalid inference. A modified version of the MMM (Mod2MMM hereafter), introduced in [zi may not be independent. This method addresses the issue by constructing new zi and Zi variables as will be discussed later.The main difference between the conventional MMM and its recent extensions are that the conventional MMM disregards the fact that the true distribution of duced in , assumesA concern over all existing MMM based methods (including Mod2MMM) that greatly affects the results is associated with the way mixed distributions are estimated. In the MMM, Expectation Maximization (EM) algorithm  is oftenz by using mixture of normal distributions in a stable and reliable way. The following observations made in the experimental study of the MMM for gene expression analysis were the main motivations for the proposed changes to the MMM:In order to address the stability and reproducibility of the MMM, we propose a new modified approach for the MMM that estimates the distribution function of 1 The observed variations in the parameter estimation process in some versions of the MMM can be attributed to the algorithm's attempt to iteratively update the means and variances of the normal distributions using often noisy data. In experimental studies, often the direct observation of the data reveals specific points where centers (means) can be positioned and the scattering patterns that can give reliable estimates on the variance of each cluster. However, the iterative updating of model parameters with noisy data and based on some random starting points often misses the true optimal points and even creates variations and fluctuations in parameter estimation in many runs.f and f0. This in turns results to lists of differentially expressed genes in different runs. More specifically, a set of two typical runs of the algorithm on the same data set can result to two lists that are very different both in number of the genes as well as the exact genes picked up by the algorithm. In our study of the conventional MMM and Mod2MMM, two runs with the same algorithm (on the same data) resulted to lists whose size vary between 50 and 200.2 Even when variations do not occur, two runs of the algorithm can result to significantly different estimations of 3 The literature of other areas of research utilizing normal basis function for estimation including neural networks indicates that in order to have more robustness in different runs and have reproducible results, the means and variances of the basis functions must be estimated and fixed during the iteration on the coefficients . This isz is regarded partially as a clustering problem, i.e. the means and variances of the normal distributions are estimated as the prototypes of a clustering step. Specifically, if z is distributed in a one-dimensional space, wherever there is a mass of z, there is a cluster with mean μi and variance Vi, which are identified by the members of that cluster.4 Based on the observations mentioned above, in our proposed method, finding the distribution of f0 and f based on K normal distributions. While the algorithm can use K-means to find the optimal values of means and variances, the coefficients πi 's need to be optimized using an optimization process such as the EM.Hence applying a clustering method is capable of estimating the means and variances of each normal distribution. The key is to use a simple clustering technique such as K-mean to estimate the mixture distributions Based on the above discussion, the proposed algorithm can be described in the following two steps:Step 1: Using BIC, find the sub-optimal number of normal distributions for both f and f0 (as described above). These optimal numbers are then used as the number of clusters in K-means technique.Step 2: Using K-means clustering technique, for both f and f0 find the best mean μi and variance Vi for all clusters.Step 3: With the obtained values of μi, Vi and using the EM algorithm, iteratively update the values of the optimized πi for all clusters (both f and f0), i.e.f and f0. The MMM creates the likelihood ratio (LR) statistics plotted in Figure K = 4 forms the LR statistics plotted in Figure K = 2 results to the LR plot of Figure A reasonable number of clusters is expected to be obtained from the first step of the algorithm, and the estimation results of the two bellow data sets in Tables πi 's, in each run, it is expected that the number of identified differentially expressed genes fluctuate slightly. However, as indicated above, since the K- means clustering algorithm is known to a robust method, and considering the fact that in the EM estimation process, only a linear estimation is performed, it is expected that the robustness of the proposed algorithm be much more than the other version of the MMM based algorithms. This observation, as have been shown before, is supported by our experimental results. In addition, our experimental indicate that the most expressed genes are identified in all runs or the algorithm and in each run one or two new genes with less expression ratio are added to this set.It is worth mentioning that due to the random initialization in K-means and the random initialization of the coefficients In this section, first the two applications and their corresponding data sets are described and then the results produced by the proposed method (i.e. K5M) is compared with the other MMM based methods on two data sets. The detailed description of the methods is given in MMM & its recent extensions Section.α = 0.01 (according to Benferroni adjustment used in the MMM based methods). Each sample in the dataset is pre-processed as in [In this section, we apply the nonparametric MMM method with and without the proposed modifications to the Leukaemia data presented in . The objed as in , by subtThomas et al  used knoThe comparison of the result obtained by the K5M with those of the MMM and the Mod2MMM is summarized in Table K = 3 identifies the total of 185 differentially expressed genes, while with K = 4 the total of 58 genes are identified, however; the 58 genes found with K = 3 are the most expressed genes among 185 genes found by K = 4. This result shows the consistency of the K5M method.As the BIC suggested the optimum number of clusters K = 4 for the MMM, the K5M is applied with K = 4 also. Running K5M with different number of clusters leads to the different but reasonably similar results. As the number of the clusters increase, the number of expressed genes decreases. Table In order to further compare the performance of the MMM and K5M on the leukaemia data, The ROC curve is plotted based on False Positive rate and True Positive rate of the data set calculated as in [Hyp mouse. Hyp mice have a mutation of the Phex gene [Phex gene block renal adaptation to low phosphate diet. Hyp mice have a primary osteoblast defect and defects in vitamin D metabolism. Parabiosis experiments on normal and Hyp mice have revealed that there is an intrinsic osteoblast defect in Hyp mice rather than an intrinsic renal abnormality. Hyp kidneys transplanted into normal mice reabsorbed phosphorus at normal levels. Kidneys transplanted from normal mice into Hyp mice began phosphate wasting in the Hyp mice.The following study is the main application for which the proposed method was specialized and therefore is described in more details. Hypophosphatemia is a common X-linked metabolic bone disorder in human. Hypophosphatemia results from phosphate wasting in the renal tubules. Phosphate that is normally reabsorbed from the urine is excreted. It appears that elevated levels of FGF-23 activate the excretion of phosphorous by the kidneys. Previous studies have demonstrated an impairment of the high- affinity, low capacity Na+ dependent phosphate co-transport system . The maihex gene ,9. The dHyp mice should indicate the systems involved in the phosphorus homeostasis. In an attempt to identify these genes, nutritional experiments were performed on normal and Hyp mice [[Hyp mice were placed on low phosphate diets for 3 – 5 days. Tissue samples from the kidneys of test and control mice were collected. 16 samples were analyzed using Affymetrix GeneChip mouse U74A arrays- 4 samples for each experiment state. The mRNA of 12,488 genes was analyzed. Two GeneChip microarrays were done for each diet for normal mice and three microarrays for each diet for the Hyp mice for a total of 10 arrays.The mechanism that leads to the excessive excretion of phosphorous is unknown. On a low phosphate diet a normal mouse will activate systems to conserve phosphate by increasing re-absorption. The genes activated in the normal mouse on the low phosphate diet, and the genes with differential expression between normal and yp mice [,8-14 andyp mice []. NormalHyp were fed a control (1.0% P) or low phosphate (0.03% P) diet for five days. The four group experiments are shown in Table To investigate this, 5-week-old normal and In this study, we consider the gene expression signal less than 100 as noise caused by the microarray machine, and in the pre-processing step we ignored the genes whose expression signals in both conditions are less than 100. The following two specific goals are considered in this study:1. To identify the genes in whose mRNA expressions are altered by low phosphate diet in normal mice.Hyp mutation on this response, i.e. identifying the genes in Hyp condition that are differentially expressed across the normal and low phosphate diet experiments.2. To determine the effect of Hyp dataset includes five samples for each group. In order to make the number of data samples even, we used four samples of each group. For this data set, since j1 = j2, the Mod2MMM cannot be applied. In MMM method, five mixture models are used to estimate f0 and f  with number of normal basis functions ranging from 1 to 5, i.e. The MMM algorithm was run several times and the run with maximum log-likelihood was chosen as the final model. Bayesian Information Criterion (BIC) [α = 0.01, and therefore the gene-specific significance level used here is calculated as:The on (BIC)  was usedα* = 0.01/(95.44 * 2) = 5 * 10-7s is obtained as s = 3 × 10-6.. Both the MMM and K5M were run 100 times. Figure Using bisection method , as discHyp condition on the most definitely expressed gene in normal and low phosphate diet microarrays. The ten most significant genes that are differentially expressed across the two experimental conditions, i.e. Normal Low Phosphate and Hyp Low Phosphate, are listed in table 6. As shown in the table 6, again eight genes are related directly to the kidney's function. These further witnesses to the capability of the proposed technique to discover the genes that are truly involved in the biological study.The ten most significant genes expressed by the low phosphate diet in the normal mouse identified by the MMM, and the ten most significant genes provided by K5M are represented in Table Hyp mice [In this paper, we proposed a technique to improve the repeatability, and robustness of the mixture model method by using the K-mean clustering method in estimating the distributions. Our proposed method finds the distribution of the variables partially based on a clustering procedure and an EM optimization process. The method is applied to analyze two microarray data sets, Leukaemia data set and a data set reflecting the effect of the low phosphate diet on regular and Hyp mice  data. ThMaryam Zaheri, and Ali A. Rad were in charge of writing the codes and programming aspects of the paper.Siamak Najarian and Javad Dargahi's primary role was to perform a literature review on mixture model techniques, identify the aspects of the method that need to be improved, and provide suggestions to address these shortcomings.Kayvan Najarian's primary roles were to design improvments to the algoritm , prepare and pre-process the data (for both datasets), partcipate in preperation of the Hyp dataset, define the Hyp problem interpret the results and finally write and edit the manuscript.The Mod2MMM makes a new z and Z based on the following formula:Where:And:


The purpose of the present work was development of a theoretical framework to enable improved prediction of in vivo response from in vitro bioassay results.There is a substantial discrepancy between in vitro we demonstrated that the in vivo response has only one maximum. For biphasic patterns of biological response in vitro both the bimodal and biphasic in vivo responses might be observed.For dose-response curve reaches a plateau in vivo responses might be predicted from dose-effect curves measured in vitro.As the main result of this work we have demonstrated that  In vitro bioassay is very useful in biomedical experiments. It has the potential to yield very important data about molecular mechanism of action of any biologically active compounds. However, the major challenge for such experiments is extrapolation to in vivo responses. Unfortunately, there is a substantial discrepancy between in vitro and in vivo experiments, and there is a paucity of work directed to prediction of in vivo response from in vitro bioassay. So, the purpose of the present work was development of a theoretical framework to enable improved prediction of in vivo response from in vitro bioassay results.in vitro experiments fall into three classes. They are:A survey of literature revealed that most cases of dose-effect curves for • monophasic response;• biphasic pattern;• bimodal or polymodal dose-effect curve.MONOPHASIC RESPONSE is the form most commonly reported in articles on in vitro bioassay. In these cases, with increasing dose of biologically active substance (BAS), the cellular response increases to a maximum (dose-response curve reaches a plateau). The most general schemes exhibiting this class of response can be classified as 3 classes:(I) BAS regulation of enzyme activity,(II) Ligand interaction with one type of receptor, and(III) Ligand interaction with negatively cooperative receptors.We will consider these three classes:(I): BAS might regulate enzyme activity. It might be:• substrate:E+S ←→ ES → E+P → cell response,     (scheme 1)E is enzyme, S is substrate, ES is enzyme-substrate complex, P is product. Cellular response is suggested to be proportional to product concentration.where Scheme (2) approximates the classic Michaelis scheme .A)• enzyme activator (E+S ←→ ES → E+P → cell responseE+A ←→ EA     (scheme 2)EA+S ←→ EAS → EA+P → cell response increasing,Scheme (3) is characteristic of many BAS. The majority of these groups are vitamins and minerals, which are known to be enzyme cofactors and serve to increase enzyme activity.I)• enzyme inhibitor (E+S ←→ ES → E+P → cell responseE+I ←→ EI → no cell response,     (scheme 3)For example, there is the large class of drugs, whose action can be described with the help of scheme (4). This class is called "inhibitors of angiotensin-converting enzyme". These drugs are commonly used for hypertension treatment and prevention .(II) Ligand interaction with one type of receptors:L+R ←→ LR → cell response     (scheme 4)L is ligand (BAS), R is receptor, LR is ligand-receptor complex.where Scheme (4) is "classic" receptor theory as described by Clark 1937) [7 [3].For example, kinetic schemes of such type were proved in the case of estrogen regulation of gene expression , apolipo(III) Ligand interaction with negative cooperative receptorsL+R ←→ LRL+LR ←→ 2R L→ cell response     (5)2R Lis complex ligand-receptor complexes.where Scheme (5) is characteristic for insulin receptors .Kinetic equations for schemes 1)–(5) are well known . They in–(5) are y = B*x/(1+A*x)     (6)and for scheme (5):y = B*x2/(1+A*x2)     (7)x is incoming signal (x is BAS concentration). For scheme (1) x is substrate concentration, for scheme (2) it is activator concentration, for scheme (3) it is inhibitor concentration, for schemes (4) and (5) it is ligand concentration. y is cellular response for the in vitro system. A and B are scaling coefficients.where The BAS concentration in the whole organism changes as a function of time according to equation (14) (see Methods.) i.e.x(t) = C(t) = 0[exp(-kelγt)-exp(-k1t)] C    (8)in vivo responses for monomodal in vitro dose-effect curves for schemes (1)–(5). We used illustrative values from works [in vitro dose-effect curves, the in vivo response has only one maximum (fig. We used equation 8) as the incoming signal, substituted this into equations (6) and (7) and solved analytically using Math Cad 8 graphing software (MathSoft Inc., Cambridge, MA, USA) to predict  as the iom works  and demo schemes –(5). We We define β (degree of conjugation) as the proportion of BAS that is free of binding proteins and is available to interact with cognate receptors. The larger is β, the larger the proportion of "free" BAS (see Methods). For equation (6) the value of this maximum is increasing as β increases; for equation (7) this value is maximum for mid-range β values.in vitro experiments the low doses of BAS stimulate cellular response, and the high doses inhibit it. So, a maximum is observed on the dose-response curve. The most common kinetic schemes for such response are:In this case, in Negative back loop (substrate and product inhibition):• E+S ←→ ES → E+P → cellular responsea) ES+S ←→ 2 ES    (9)E+S ←→ ES → E+P → cellular responseb) ES + P ↔ ESPSuch schemes are characteristic of glucose metabolism .• Presence of two receptor types: one type stimulates cellular response, another type inhibits it.L+R ←→ LR → "positive" cellular responseL+R' ←→ LR' → "negative" cellular response     (10)R are receptors of the first type, R' are receptors of the second type, LR, LR' are ligand-receptor complexes with different receptor types.where This mechanism has been proven for estrogen regulation of nitric oxide synthase It has been suggested, that mechanism (11) is basic for drug tolerance . For exa• Change of effector's molecule conformation:"Active" conformation + ligand suplus ←→ "Passive" conformation     (12)++ regulation of 1,4,5-trisphosphate activity. The authors suggested that Ca++ surplus induces a change in Ca++-channel conformation from "open" or "active" to "closed" or "passive" [Scheme (12) was suggested by Bootman and Lipp (1999)  for Ca++passive" .For schemes (9)–(12), due to the first order Taylor series, this kinetic equation can be derived:y = A*x*exp(-B*x)     (13)in vivo biphasic dose-effects curves (fig. in vivo response is affected by the dose of BAS and its degree of conjugation (β). Both the bimodal and biphasic in vivo responses might be observed for biphasic dose-effect curves. Changes of dose of BAS concentration or its conjugation with blood proteins (or their concentration) might dramatically change the form of in vivo response. For the simulations shown in Figure kel and k1 and blood volume (4.9 liters) based on measurements by Baumann et al. (1987) [Using equation (13), we obtained a prediction of ves fig. . As is avolume 4. liters bet al. 197 [8] for. (1987) .B value. This might be observed in cascade system of signal transduction and amplification. If x regulate intermediate z formation in biphasic way with B1, and z has biphasic response on y formation with B2, then if 1B<2B, summary dose-effect curve (y concentration from x) is bimodal  will require measurements of acute biological response at multiple concentration and time points. For longer-term responses requiring protein synthesis , it could be argued that such multiple time point studies would not be as important. However, in so far as long term biological responses are the consequence of critical initial events which may require threshold concentrations of free hormone, or repeated patterns of hormone exposure over prolonged periods [Analogues of hormones are commonly used in medicine for hormone replacement therapy , for oral contraception, as anabolic drugs, for asthma therapy and so on . But eng periods ,19, thisin vitro such hormones seems to be normal , but in vivo they may have abnormal effects due to changes of their binding protein concentration, or ratio free/bound hormone.Another application of our work may be the study of hormone functions in glandular tumour disorders. With these disorders, there is usually serious metabolic or hormonal dysfunction. From our point of view, it may be not only due to gland biosynthesis of abnormal hormone. Tumour-produced hormones may not differ structurally from their normal analogues. The dysfunctional occurs due to abnormal concentrations of hormones, which are synthesised by tumours. As it follows from our results, changes in concentrations can dramatically change the form and value of biological response. On the other hand, in many tumour disorders the concentrations of binding proteins are changed. For example, in ovarian carcinoma the changes of sex binding protein and ratio free/bound sex hormones (β) are observed . As follin vivo responses might be predicted from dose-effect curves measured in vitro. For monophasic curves, in vivo response is proportional to BAS concentration. For the most complex in vitro curves, the value and the form of in vivo response depends in a predictable way on the dose of BAS and its degree of conjugation.So, as a result of this work we have demonstrated that To obtain the discussed results we used linear pharmacokinetics model:m1(t) mass of biologically active substance (BAS) in the place of infusion, m2(t) mass of BAS in compartment (blood), k1,kel constants of hormone diffusion from place of infusion to blood and excretion form blood (accordingly).where: Many of biologically active substances are conjugate into complexes with blood proteins (for example: GH, nerves growth factor, IGF-1):B+P ⇔K HP     (15)B is BAS, P is blood protein, BP is BAS-protein complex, K is dissociation constant.where For many BAS, concentration of free (not bound with blood proteins) BAS is equal to:0] [B] ≈β [B    (16)B] is concentration of free BAS, [B0] is initial concentration of BAS. If β = 1 then BAS dose not conjugate with protein. If β = 0 then all BAS is in conjugate form.where β is constant ("degree of conjugation"), [It may be that only conjugate BAS (for example, bilirubin), or only unconjugated BAS can be excreted form the blood (for example, sex hormones). This means that for scheme (14) the law of mass action will be written in the next way:1/dt dm= -1m1k, m1(0) = M2/dt dm= 1m1 k- γelm2k, 2(0) = 0 m    (17)where γ is a constant. γ = 1-β if only conjugate form of BAS can be excreted and γ = β if only unconjugated form is excreted.t: γ = const(t). This means that solution of system (17) is:But γ is a constant with respect to C(t) = 0[exp(-kelγt)-exp(-k1t)] C    (18)C(t) is BAS concentration in the blood compartment (C = m2/V, V = const (about 5 liters) is blood volume), C0 is seems initial BAS concentration (C0 = M/V).where