TEL-AML1], t[MLL-AF4], t[BCR-ABL], and t[E2A-PBX]. ALL has been studied at the molecular level through expression profiling resulting in un-validated expression correlates of these prognostic indices. To date, the great wealth of expression data, which has been generated in disparate institutions, representing an extremely large cohort of samples has not been combined to validate any of these analyses. The majority of this data has been generated on the Affymetrix platform, potentially making data integration and validation on independent sample sets a possibility. Unfortunately, because the array platform has been evolving over the past several years the arrays themselves have different probe sets, making direct comparisons difficult.Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy and has been the poster-child for improved therapeutics in cancer, with life time disease-free survival (LTDFS) rates improving from <10% in 1970 to >80% today. There are numerous known genetic prognostic variables in ALL, which include T cell ALL, the hyperdiploid karyotype and the translocations: t[To test the comparability between different array platforms, we have accumulated all Affymetrix ALL array data that is available in the public domain, as well as two sets of cDNA array data. In addition, we have supplemented this data pool by profiling additional diagnostic pediatric ALL samples in our lab. Lists of genes that are differentially expressed in the six major subclasses of ALL have previously been reported in the literature as possible predictors of the subclass.We validated the predictability of these gene lists on all of the independent datasets accumulated from various labs and generated on various array platforms, by blindly distinguishing the prognostic genetic variables of ALL. Cross-generation array validation was used successfully with high sensitivity and high specificity of gene predictors for prognostic variables. We have also been able to validate the gene predictors with high accuracy using an independent dataset generated on cDNA arrays.Interarray comparisons such as this one will further enhance the ability to integrate data from several generations of microarray experiments and will help to break down barriers to the assimilation of existing datasets into a comprehensive data pool. The advent of DNA microarrays has provided the science community with a tool to concurrently examine the expression of thousands of genes within a given cell or tissue type, thus providing a platform for future diagnoses and prognostic analyses of disease with gene-level specificity ,2. Microet al. generated a list of genes that have distinct expression levels for various karyotypic and phenotypic aberrations common to pediatric ALL , t[MLL-AF4], t[BCR-ABL], and t[E2A-PBX] [TEL-AML1 fusion gene have a better prognosis than patients in the other subclasses [ALL is the most common pediatric malignancy comprising over 75% of the annual diagnoses of leukemias in children . In the L-AF4], t;22[BCR-Abclasses . The iniML1], t4;[MLL-AF4]et al. (2002), for each of the aforementioned prognostic genetic variables. Cross-platform array validation was used successfully to ascertain the accuracy, sensitivity and specificity of the gene predictors for the prognostic variables. In addition, we have demonstrated the ability to compare datasets from different microarray platforms. To our knowledge, this is among the first known successful applications of this technique, along with the validation of the Yeoh et al pediatric ALL gene lists on adult ALL by Kohlmann, et al (2004) [In this study, we sought to determine if datasets from different microarray platforms could be compared in a useful manner. We chose to study pediatric ALL because there is already a substantial pool of datasets freely available in the public domain. First, we collected pediatric ALL array data and cDNA array data generated in experiments from various laboratories. In addition to the collection of public data, we supplemented the hyperdiploid karyotype and the T-cell lineage ALL subclasses by expression profiling additional diagnostic pediatric ALL samples from a tumor bank in Children's National Medical Center in Washington, D.C. We used these independent datasets, including the one generated in our lab, to validate the gene predictors, as defined by Yeoh l (2004) . InterarTo supplement the ALL subclasses that are under-represented in expression profiling thus far, we collected and extracted the total RNA from sixteen diagnostic bone marrow samples housed at Children's National Medical Center; seven of the hyperdiploid karyotype and nine with T-cell lineage. The extracts were then hybridized to Affymetrix U133A arrays and expression profiled as an independent training data set.et al. in 2002 [et al. used both a training and test dataset in their analysis to first uncover the subclass-specific gene expression profiles and then to test their predictability on independent samples [. We then accumulated the ALL diagnostic bone marrow array data available in the public domain , which were provided by the comprehensive training ALL sample set analyzed and published by Yeoh 0 genes, in Table .. We then used the discriminating gene list for each subclass to extract the appropriate probes and their intensity values from the expansive expression data for each sample of the validation datasets independently. The level of similarity between the probe sets of the two different array platforms was evident through the number of genes within the 40 discriminators that could be found within the validation data  was generated on the U95Av2 array platform [MLL-AF4, TEL-AML1, and hyperdiploid datasets. Similarly, the U133A arrays that were used to generate expression data in our lab for the hyperdiploid karyotype and the T-cell lineage ALL contained probes representing the majority of the 40 discriminators, with 38 and 35 genes, respectively. The HuGene FL arrays contain significantly fewer probe sets in common with the selected predictors (from the later-generation U95Av2 microarrays). Accordingly, of the 40 original predictor probes, only 25 were present in the TEL-AML1 dataset, 26 in the E2A-PBX1 dataset and 13 in the T-cell dataset. The difficult task of matching probes from the Affymetrix gene chips with cDNA arrays was illustrative of the disparities between the probe sets within these two platforms. For example, the five cDNA predictors built using the datasets produced by Moos et al. (2002) and Catchpoole et al. (unpublished data) contained data for only ten genes or less from the predictor set gene list.The independent datasets that we accumulated, including the one generated in our lab, spanned four different microarray platforms: Affymetrix HuGene FL, U95Av2, U133A and custom cDNA microarray platforms , using the aforementioned independent test datasets from various array platforms, we employed supervised learning methods using GeneCluster2 software. Prior to analysis, we formatted the discriminating gene expression values from the test datasets onto spreadsheets according to software instruction, and subsequently applied the data to the software. Genecluster2 then generated blinded predictions on the ALL samples of the test datasets through weighted voting with a leave-one-out methodology. This is accomplished by randomly removing one sample at a time from the test dataset of ALL samples and "training" a predictor gene profile to recognize similarities or disparities between the two classes based on the expression profiles of the samples for the genes of interest [To validate the gene predictors from Yeoh interest . In thiset al. 002, usinE2A-PBX1 predictor (96%) and the MLL-AF4 predictor (98%), the sensitivities were only 50%. In both cases there were only 2 samples out of the sample pool expressing the respective translocation and in both analyses one of the two was continuously classified incorrectly. This could be due to many factors, misdiagnosis or mislabelling of the sample, poor sample quality or differences in sample handling. It is difficult to draw a conclusion due to the fact that the samples were collected and processed in a laboratory outside of our own. Another problem with these two predictors may simply be the low sample number. Two samples may not provide enough strength to the classification by Genecluster2 simply due to the inability of such a low sample number to account for the biological variability that exists between patients that is independent of their subclass of ALL. The most surprising result was the high accuracy with which the gene lists could classify T-ALL, TEL-AML1 and MLL-AF4 from cDNA data considering the disparity between the probe sets of cDNA arrays and oligo arrays. The accuracies of the classifiers were: T-ALL (Catchpoole data), 100%; T-ALL (Moos data), 98%; MLL-AF4, 98%; and TEL-AML1, 87%. Therefore, it appears that the number of genes in the predictor gene list is much less of a factor in the predictor's classification accuracy than the number of samples representing the phenotype of interest, which supports the argument that being able to do cross-platform analyses to increase sample size is crucial for sensitive and specific class prediction using expression data. This is strongly illustrated by the cDNA predictors, which have few probes in common with the arrays used to generate the predictor gene list, but still classify the ALL samples with high sensitivity, specificity and accuracy. On the other hand the E2A-PBX1 predictor and the MLL-AF4 predictor had low sensitivities correlating to a low sample number in these groups. The high number of probes in common between the arrays used to generate these independent datasets and the arrays used to generate the predictor gene list, 26/40 and 40/40, respectively, were not able to rescue the low sensitivity of the predictor.We saw a high accuracy from the predictors employing data from both U95Av2 and U133A arrays, attesting to the fact that nearly all of the 40 discriminating genes were present in the datasets, thus maximizing the possible prediction strength. In the case of the et al. (2002) withstands validation by testing the predictors using a leave-one-out strategy on all publicly available datasets as well as a dataset generated in our own lab regardless of the array platform used. This meta-data analysis of over 200 arrays from diagnostic ALL samples with hyperdiploidy, T-cell lineage and translocation status (previously confirmed through gold standard techniques), shows that expression profiling as an integrated platform is robust and that ALL data, and presumably other disease models, can be interplatform comparable. By validating the comparability between data from distinct microarray platforms we have demonstrated a tool that can enhance the statistical power provided by large sample sets. Thus, we can potentially develop and validate sensitive diagnostic tools based on large training sample sets, to allow for the rapid assignment of individualized therapy to improve disease outcome in pediatric ALL and other diseases.Currently the vast majority of expression data from numerous labs is not being used to its highest potential as independent labs continue to move to more expansive array platforms rendering older datasets less informative in the context of new data. Increasingly, progressive technologies in genome databasing and chip construction are prompting this inevitable evolution of microarrays. Until data can be analyzed and directly compared across array platforms, the size of the data pools will remain small and isolated according to platform . Here weALL diagnostic bone marrow samples were housed in a tumor bank in Children's National Medical Center in Washington, D.C. Mononuclear cells from diagnostic bone marrow aspirates were separated using density centrifugation on Cappel Lymphocyte Separation Medium  and immediately flash frozen according to manufacturer's instruction. A total of sixteen samples were obtained with IRB approval; seven with a hyperdiploid karyotype and nine samples of a T-cell lineage as confirmed by immunophenotyping. The frozen samples were placed directly in TRIzol reagent for RNAse-free thawing for total RNA extraction. We extracted a 10 μg–20 μg pellet of total RNA from each sample by centrifugation following phenol-chloroform extraction. The integrity of the resultant total RNA from each sample was quantified by gel electrophoresis before it was considered to be of good quality for cDNA synthesis. Samples were re-extracted if ribosomal bands were not visible.et al. (2002) [et al. (2002) from Affymetrix U95Av2 microarrays. The 40 genes that showed the greatest mean difference in expression between the subclass of interest and the remaining subclasses was used as our predictor gene set. The gene lists and additional information, including the statistical metrics used to generate the gene list from the training set, can be viewed at: . To identify comparable data points between the gene lists from the training set (produced on the U95Av2 Affymetrix chip), and the expression values of samples provided by other public datasets on different generation Affymetrix arrays, we used the probe match function within NetAffx . Data for these probe pairs in the validation sets were extracted and expression values were linearly adjusted to fall within 1–45000 [ and GenBank accession number queries. Ratios were log-transformed prior to analysis. GeneCluster2  was used to perform blinded predictions on the validation dataset using weighted voting with a leave-one-out methodology. Accuracy, specificity and sensitivity values were then generated for each predictor, as a measure of the predictor's ability to correctly group the samples into their respective class in the validation sets.10 μg of the extracted RNA from each sample was labelled and hybridized to an Affymetrix U133A array  according to protocol as previously described [. (2002) . To creaSAM carried out the accumulation of datasets, preparation of the predictors, data analysis and drafted the manuscript. SAM with the help of KMB and MMH participated in the sample preparation and hybridization to arrays of the in-house gene expression data. KMB also trained SAM and participated in the preparation of the predictors with SAM and also provided essential mentor support. MM participated in finding and extracting the public data and aided in drafting the manuscript. MMH was a tremendous help in editing the final manuscript. BL provided statistical guidance throughout the project. DC provided us with cDNA array data and aided in the analysis of the cDNA expression data. DC also provided much appreciated critical input throughout the entire project. DAS initiated and crafted the idea and provided the necessary mentorship along the way.


Escherichia coli is an important and frequently used tool within malaria research, however, this method remains problematic. High A/T versus C/G content and frequent lysine and arginine repeats in the Plasmodium falciparum genome are thought to be the main reason for early termination in the mRNA translation process. Therefore, the majority of P. falciparum derived recombinant proteins is expressed only as truncated forms or appears as insoluble inclusion bodies within the bacterial cells.The expression of recombinant proteins in P. falciparum strains were expressed in E. coli as GST-fusion proteins. Expression was carried out under various culture conditions with a main focus on the time point of induction in relation to the bacterial growth stage.Several domains of PfEMP1 genes obtained from different E. coli recombinant proteins derived from P. falciparum sequences are often truncated and tend to aggregate what in turn leads to the formation of insoluble inclusion bodies. The analysis of various factors influencing the expression revealed that the time point of induction plays a key role in successful expression of A/T rich sequences into their native conformation. Contrary to recommended procedures, initiation of expression at post-log instead of mid-log growth phase generated significantly increased amounts of soluble protein of a high quality. Furthermore, these proteins were shown to be functionally active. Other factors such as temperature, pH, bacterial proteases or the codon optimization for E. coli had little or no effect on the quality of the recombinant protein, nevertheless, optimizing these factors might be beneficial for each individual construct. In conclusion, changing the timepoint of induction and conducting expression at the post-log stage where the bacteria have entered a decelerated growth phase, greatly facilitates and improves the expression of sequences containing rare codons.When expressed in  Different organisms have been applied for the production of malaria proteins, including Escherichia coli [Pichia pastoris and Saccharomyces cerevisiae) [E. coli expression system is the most attractive and most frequently used, because it quickly produces large amounts of biomass without sophisticated laboratory equipment and at low costs. However, the quality of many proteins expressed in E. coli has not been satisfactory. In many cases, the recombinant proteins are either expressed as truncated forms or precipitate in insoluble inclusion bodies in the bacterial cells. Although methods have been developed to obtain correctly folded proteins from these inclusion bodies, the process of refolding cannot be successfully applied to all proteins [Qualitative and quantitative production of proteins in heterologous systems is essential for the characterization of any molecule, from determination of antigenicity, functional and structural analysis to vaccine development. Malaria antigens are among the most difficult proteins to express with hia coli ,2, baculhia coli ,4, yeastevisiae) -8, transevisiae)  and tranevisiae) . Among tproteins ,12.P. pastoris and S. cerevisiae has been established in several laboratories [Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) have been produced in P. pastoris for malaria vaccine studies in either primates or pre-clinical trials in humans [P. pastoris, the codon sequences of these antigens need to be optimized. In most cases, sequences encoding for the amino acids of potential glycosylation sites have to be removed. So far, this system is the most promising one and might be the favourite choice when it comes to the production of recombinant malaria proteins under GMP conditions. It is nevertheless unlikely that this system will replace E. coli as a routine bench bioreactor due to its complicated manipulation and relatively long cultivation times. The use of long synthetic peptides (LSP) has been explored in malaria vaccine antigen production in recent years [Proteins expressed in insect cells using the baculovirus system are generally correctly folded . Howeverratories -8. Recomn humans . Howevernt years ,15. The P. falciparum genome is one of the most A/T-rich genomes. Surface exposed molecules expressed by the parasite such as members of the PfEMP1 family are positively charged, caused by the abundance of arginine and lysine residues in their sequences, which complicates their expression in heterologous systems such as E. coli. The high content of A/T repeats in the mRNA template is a reason for early translation termination and results in heterogeneity of recombinant proteins. Members of the PfEMP1 family are of great interest since they are virulence factors that mediate adhesion of P. falciparum-infected erythrocytes in the post-capillary microvasculature, a process that leads to severe malaria [in vitro production of the correctly folded protein, and production of these proteins in the native conformation remains particularly difficult.The  malaria . Each Pf malaria . MoleculE. coli was used as a bioreactor, it has been the main goal to reach as high expression levels of the recombinant protein as possible. However, high expression levels will not guarantee a high quality of the final product. Efficient expression of heterologous proteins in E. coli is impaired by the rarity of certain tRNAs that are abundant in the organisms from which the heterologous protein is derived [In most studies where  derived . When thE. coli. Induction at the post-log stage of bacterial growth leads to the production of considerably larger amounts of soluble protein of a higher quality compared to standard conditions. Moreover, the method is easily applicable in laboratories where a sophisticated cultivation facility is not available.This study describes a simple method for optimizing the cultivation conditions and especially the timepoint of induction exemplified by the expression of several PfEMP1 domains in P.falciparum parasite strains FCR3S1.2 and TM284S2 were cultured according to standard methods with 10% AB+ Rh+ serum added to the buffered medium . Genomic DNA from these parasites was purified using the EasyDNA purification kit (Invitrogen) according to the manufacturer's protocol.The var1PfEMP1, GST-DBL1α and GST-DBL2β of TM284S2var1PfEMP1 were generated as described earlier [Plasmid constructs for the expression of the recombinant proteins GST-DBL1α and GST-CIDR1α of FCR3S1.2 earlier ,19.var1PfEMP1 was optimized for codon adaptation in E. coli. The genes were re-synthesized chemically . The re-synthesized DBL1α was amplified with oligonucleotide primers  and cloned into the pGEX4T-1 vector as described [The sequence of the DBL1α domain of FCR3S1.2escribed .U-, leucineW-and isoleucineY-tRNA genes for optimal expression of heterologous proteins of organisms with A/T-rich genetic sequences.The BL21-CodonPlus-RIL strain purchased from Stratagene  was used for protein expression. This bacterial strain has been engineered to contain a high copy number of argenine600 of the cultures were monitored systematically. Aliquots (50 ml) of each culture were sequentially taken after the OD A600 reached 0.5 and IPTG  was added to a final concentration of 0.1 mM to induce the expression. The expression was carried out for three hours at 37°C and the bacteria were harvested afterwards by centrifugation at 4000 rpm for 15 minutes. The recombinant proteins were purified on Glutathione-sepharose  as described earlier [BL21 competent cells were transformed with recombinant pGEX4T-1 plasmids containing FCR3S1.2 DBL1α, TM284S2 DBL1α or TM284S2 DBL2β as inserts. The transformed bacteria were selected on LB agar plates containing ampicillin (100 μg/ml). A single colony of the transformed bacteria was inoculated in 30 ml LB medium containing ampicillin (100 μg/ml) and chloramophenicol (50 μg/ml) for cultivation at 37°C overnight. Aliquots of the culture were inoculated into one litre LB medium with ampicillin (100 μg/ml). The cultivation was carried out with a shaking speed of 225 rpm. The pH value and the optical density at A earlier ,19.To analyse the recombinant proteins, aliquots of the soluble and insoluble fractions of the expressed proteins from each purification were mixed with an equal volume of SDS-PAGE loading buffer containing β-mercaptoethanol and boiled at 100°C for 5 min. The denatured proteins were resolved in 10% acrylamide gels containing 1% SDS and visualized by staining in Coomassie brilliant blue solution.Purified recombinant DBL1α of FCR3S1.2 expressed with pGEX plasmids containing either the wild-type DBL1 sequence or the codon-optimized sequence was further passed through a heparin-HiTrap column . After washing with PBS tween-20 buffer, the bound protein was released from the column with 2M NaCl and dialyzed immediately against cold PBS. Aliquots of the eluted proteins were subjected to SDS-PAGE.The binding of recombinant DBL1α of FCR3S1.2 to blood group A antigen was studied using a solid phase assay system as described earlier .E. coli was induced either at an OD A600 of 0.6, which is commonly recommended or at an OD A600 higher than 2.0. SDS-PAGE analysis . Both soluble and insoluble fractions of the same culture were compared. The results  and, therefore, has little influence on the quality of the expressed protein. However, temperature is an important factor to consider. Keeping the culture at 16°C before and after induction slightly improves the protein quality (data not shown), but, on the other hand, slows down bacterial growth considerably and therefore minimizes the final yield of the recombinant protein.E. coli will improve expression quality. Here we show that C/G versus A/T contents of the heterologous gene sequence are not among the most important factors that determine the quality of the recombinant protein. The expression of GST-DBL1α of FCR3S1.3 .To further demonstrate functionality of the proteins expressed at high OD AP. falciparum derived proteins, especially membrane-bound proteins is still a great challenge due to the high content of amino acids encoded by rare codons in the P. falciparum genome. The method reported here presents an easily applicable tool to express sequences containing rare codons. The key factor for the expression of such proteins is to decelerate the translation machinery inside the bacteria. Low expression speed will not only allow the ribosomal unit to smoothly pass through the mRNA templates and synthesize full-length polypeptide chains, but also enable the proteins to slowly transfer from the unstable intermediate phase to the correctly folded phase. The described expression approach will result in a final product that is soluble, intact and functional, nevertheless, additional factors might influence the expression and need to be optimized for each individual construct.The expression of E. coli is one of the most frequently used tools in modern science. Numerous approaches have aimed at achieving the highest possible level of expression by having a maximum amount of protein expressed per bacterial cell. Our studies suggest on the contrary that increasing the number of bacterial cells in the culture while at the same time keeping the expression process at a low profile, might considerably improve the quality and quantity of the protein. That way, high level expression can simply be achieved by increasing the bacterial density of a culture, whereby problems in form of truncated or insoluble protein factions are almost completely eliminated.The expression of eukaryotic genes in KF carried out the expression assays. SA and AC participated in the expression and optimization experiments. MTB participated in sequence design. QC coordinated the experiments and helped to draft the manuscript. All authors read and approved the final manuscript


PLoS Biology,c oxidase I (COI), for a sample of North American birds. Before turning to details of this study, it is useful as context to consider the following questions: What is DNA barcoding, and what does it promise? What is new about it? Why is it controversial? What are the potential pitfalls?In this issue of Put simply, the intent of DNA barcoding is to use large-scale screening of one or a few reference genes in order to (i) assign unknown individuals to species, and (ii) enhance discovery of new species . ProponeTrends in Ecology and Evolution). The former view reflects a real need to connect different life history stages and to increase the precision and efficiency of field studies involving diverse and difficult-to-identify taxa. The criticisms are mainly in response to the view that single-gene sequences should be the primary identifier for species  and its validity as a test of the barcoding concept. Their aim is to test “the correspondence between species boundaries signaled by COI barcodes and those established by prior taxonomic research.” North American birds are an interesting choice because their species-level taxonomy is relatively well resolved and there has been extensive previous analysis of levels of mtDNA sequence divergence within and among described species . HerbertAlthough Herbert et al. sampled a large number of species, a true test of the precision of mtDNA barcodes to assign individuals to species would include comparisons with sister species—the most closely related extant relatives. This would require that all members of a genus be examined, rather than a random sample of imprecisely defined close relatives, and that taxa be included from more than one geographic region. Empidonax flycatchers, which are renowned for their morphological similarity and could thereby benefit from DNA-based identification tools, provide an example of the importance of a more detailed analysis. A complete molecular phylogeny for this group  to 4.6% ; notably, the genetic distance between mainland and island populations of E. difficilis  was greater than that between sister species (Empidonax (E. traillii and E. virescens), which are not sisters but members of divergent clades. Because E. virescens is genetically distant from all other species of Empidonax , and pooled pairwise genetic distances were found to be uncorrelated with geographic distances, leading Hebert et al. to conclude that “high levels of intraspecific divergence in COI in North American birds appear uncommon.” However, this makes the assumption that there is a common underlying pattern of phylogeographic structure, which is unlikely for North American birds , but, for the reasons outlined here, they are not yet a definitive test of the utility of DNA barcoding for either diagnosis of individuals or discovery of species. We also question whether the results for North American birds can be extrapolated to the tropics, where DNA barcoding could have maximum value. In general, among-population sequence divergence increases with decreasing latitude, even excluding previously glaciated regions  hybridization, recent radiations, and high rates of gene transfer from mtDNA to the nucleus. Only then will the skeptics be satisfied.


Self-administrated patient outcome scores are increasingly recommended for evaluation of primary outcome in clinical studies. The VISA-P score, developed at the Victorian Institute of Sport Assessment in Melbourne, Australia, is a questionnaire developed for patients with patellar tendinopathy and the patients assess severity of symptoms, function and ability to participate in sport. The aim of this study was to translate the questionnaire into Swedish and to study the reliability and validity of the translated questionnaire and resultant scores.The questionnaire was translated into Swedish according to internationally recommended guidelines for cross-cultural adaptation of self-report measures. The reliability and validity were tested in three different populations. The populations used were healthy students (n = 17), members of the Swedish male national basketball team (n = 17), considered as a population at risk, and a group of non-surgically treated patients (n = 17) with clinically diagnosed patellar tendinopathy. The questionnaire was completed by 51 subjects altogether.The translated VISA-P questionnaire showed very good test-retest reliability (ICC = 0.97).The mean (± SD) of the VISA-P score, at both the first and second test occasions was highest in the healthy student group 83 (± 13) and 81 (± 15), respectively. The score of the basketball players was 79 (± 24) and 80 (± 23), while the patient group scored significantly (p < 0.05) lower, 48 (± 20) and 52 (± 19).The translated version of the VISA-P questionnaire was linguistically and culturally equivalent to the original version. The translated score showed good reliability. Patellar tendinopathy affects athletes in many sports and at all levels of participation, but is of particular concern for elite jumping athletes . Many diThere are few studies on non-surgical treatment of patellar tendinopathy and there is a lack of evidence-based knowledge evaluating the therapy . In-vivoPatient-administrated questionnaires are frequently applied as primary outcome measures in clinical trials and several inventories have been translated from English into Swedish -17. The The purpose of this study was to translate and cross-culturally adapt the VISA-P score for a Swedish population and to perform a psychometric analysis as well as reliability and initial validity testing of the Swedish VISA-P score.Fifty-one subjects gave informed consent to participate in this study. The VISA-P score was administered to 17 healthy students ; a population at risk, the Swedish male national basketball team , and patients with the diagnosis patellar tendinopathy .The study was approved by the Ethical Committee at the Medical Faculty of the Karolinska Institute, Stockholm (Dnr 00-103).The VISA-P score consists of eight questions , of whicThe original VISA-P score lacks information about the selection of items, weighting of each answer and the ranking of the options in the subscales in question 8.The aim of the present investigation was to get a "working tool" for further studies of the usefulness of the instrument for patellar tendinopathy patients in Sweden.The VISA Tendon Study Group at the University of Melbourne in Australia was informed and gave their consent to a Swedish translation of their original VISA-P score .The translation process followed the method described by Beaton et al . This meInitially, two physiotherapists performed two independent translations (I) from English into Swedish. A synthesis (II) of these translations was made, and the consensus of the two translated Swedish versions was documented. Reverse translations (III) were performed independently by three native Anglophones fluent in Swedish. One of the reverse translators was a physiotherapist, one was an economist and the third was a teacher. The three physiotherapists in the expert committee (IV) then made a semantic and idiomatic equivalence analysis between the original source and target Swedish version of the VISA-P questionnaire. The translated questionnaire was pre-tested (V) on 12 individuals, six patients with patellar tendinopathy and six physical education students.The Swedish VISA-P score (Appendix B) see  was admiThe principal investigator administrated the questionnaires at all test occasions, with the exception of six of the tendinopathy patients.For validity, the factor structure of the VISA-P score was analyzed with a principal component analysis, Varimax rotation. The number of extracted factors was equal to the number of eigen values above 1.00. Internal consistency of subscales, based on the factor analysis, and the total scale was calculated as a Cronbach α coefficient .For discriminative validity of the VISA-P questionnaires were compared between three groups, each of which were expected to have different levels of scoring.All variables were summarized according to standard descriptive methods [mean and standard deviation (SD)] and checked for outliers. No significant deviations from the normal distribution criterion were found. The test-retest reliability was analyzed according to the method described by Bland and Altman, which yields an intra-class correlation (ICC) . DiffereThe expert committee considered the translation and reverse translation satisfactory.The test-retest of the Swedish VISA-P score showed high reliability and significance . In Figure The score was easy to use and it took about five minutes to complete.The internal consistency of the total scale was high for the scores both at the first and second occasion, 0.83 and 0.82, respectively.The principal component analysis yielded a two-factor solution. The communality, i.e. the degree of explained variance, of one of the questions (Table At the first test occasion (A) the mean (± SD) of the VISA-P score in the healthy student group was 83 ± 12), in the basketball players 79 (± 23), and 47 (± 20) in the patient group . During the translation procedure of the VISA-P score, the translation for "pain" was debated. Different Swedish words were discussed and compared between the different translators. Translations into the mother tongue, or the first language, more accurately reflected the nuances of the language. Reverse translation into English of the Swedish VISA-P version was without remarks. Thus, the original and translated versions were judged by the expert committee to be congruent.Over a time interval of one week (range 4–7 days), the Swedish version of the VISA-P score showed high reliability (ICC = 0.97). As compared to other test-retest investigations of this score, this interval is the longest that has been studied .A factor analysis yielded three factors, of which the first showed the highest correlations with two questions  see  it wouldThe significantly higher scores of the basketball players in question 7, "currently undertaking sport" Table  were triGenerally, there is a debate concerning scores about the relevance of using the total score or dividing the score in different subgroups. A short clinical scale is often an advantage. The factor analysis as well as the analysis of differences between the groups suggests that the VISA-P score could be abbreviated to two or three items without losing significant clinical information Table .An important aspect of a clinical scale is its sensitivity for change or its ability to follow amelioration or exacerbation during treatment. The theoretical range of the VISA score, i.e. the floor and ceiling, is 0–100. The mean total score of the patients was approximately 50  and for the control groups 80  see  of the oAF initiated the study, led the translation process and conducted all test occasions. TS and PR helped with general analysis and writing the article. GE guided and helped the main author with the statistical analyses of the data collected. All four of the authors read and approved the article.The pre-publication history for this paper can be accessed here:Appendix A. The original VISA-P score.Click here for fileAppendix B. The translated and cross-culturally adapted Swedish VISA-P score.Click here for file


Streptococcus pneumoniae is the leading cause of bacterial meningitis. Pneumococcal meningitis is associated with the highest mortality among bacterial meningitis and it may also lead to neurological sequelae despite the use of antibiotic therapy. Experimental animal models of pneumococcal meningitis are important to study the pathogenesis of meningitis, the host immune response induced after infection, and the efficacy of novel drugs and vaccines.S. pneumoniae were 3.2 × 10, 2.9 × 10 and 1.9 × 102 colony forming units, respectively. Characterisation of the disease caused by the type 4 strain showed that in moribund mice systemic dissemination of pneumococci to blood and spleen occurred. Histological analysis of the brain of animals infected with type 4 S. pneumoniae proved the induction of meningitis closely resembling the disease in humans.In the present work, we describe in detail a simple, reproducible and efficient method to induce pneumococcal meningitis in outbred mice by using the intracranial subarachnoidal route of infection. Bacteria were injected into the subarachnoid space through a soft point located 3.5 mm rostral from the bregma. The model was tested with several doses of pneumococci of three capsular serotypes , and mice survival was recorded. Lethal doses killing 50 % of animals infected with type 2, 3 and 4 The proposed method for inducing pneumococcal meningitis in outbred mice is easy-to-perform, fast, cost-effective, and reproducible, irrespective of the serotype of pneumococci used. Neisseria meningitidis, Haemophilus influenzae and Streptococcus pneumoniae [S. pneumoniae in humans and especially in children [S. pneumoniae in mice may also occur through retrograde axonal transport along olfactory neurons [Bacterial meningitis is an important infection of the central nervous system (CNS), and the three major responsible bacteria are eumoniae . Despiteeumoniae ,3. Develchildren . The patchildren ,2. Howev neurons . Once th neurons ,2.S. pneumoniae infection, and (iv) test the efficacy of novel antibiotics and vaccine candidates. Both infant and adult rats [S. pneumoniae [S. pneumoniae in humans. However, those models present the disadvantage that PM is induced in about half of the animals, while the remaining mice may die of sepsis without developing meningitis. Models of meningitis induced by the i.p. route were employed to carry out therapeutic studies [et al. [et al. is an useful and reliable system for causing PM in the mouse [et al., who induced meningitis via inoculation of S. pneumoniae (type 3) into the cisterna magna of C57BL/6 mice and investigated the function of nitric oxide in the disease [Animal models of PM have been developed in order to: (i) characterise the pathogenesis of meningitis, (ii) analyse the role of pneumococcal virulence factors in the disease, (iii) understand the host immune response to ult rats -9, and ault rats -13 have eumoniae . Howevereumoniae -17, (ii)eumoniae ,19, or  as a model strain. The proposed method is precise, simple, cost-effective, fast and reproducible, and the disease induced closely resembles PM in humans.i.e. lethargy, paralysis) for several weeks after inoculation (data not shown).The bregma is the intersection of the coronal and sagittal sutures of the skull and can be recognised in mice by visual examination of the exposed skull Fig. . We inocThese data allowed localisation of the anatomical coordinates of the inoculation site and proved the suitability of the i.c. subarachnoidal infection technique.After characterisation of the inoculation site and technique, mice were infected with pneumococci, and the establishment of PM was evaluated and clearly evidenced by histological analysis (see below).50) were calculated. We chose three commonly used S. pneumoniae strains, such as D39, HB565, and TIGR4. The D39 strain (type 2) is the encapsulated parent of the rough type 2 R36A strain used by Avery [50 of 3.2 × 10 and 2.9 × 10 colony forming units (CFU), respectively (data not shown). The TIGR4 strain was less virulent in the i.c. subarachnoidal infection model compared to D39 and HB565, as its LD50 was 1.9 × 102 CFU (data not shown).In order to test the model with different pneumococcal serotypes, dose-dependent survival studies were performed, and the lethal doses killing 50% of animals , while it considerably decreased at the highest dose (105 CFU) leading to rapid death of all mice.To describe the features of PM in detail following i.c. subarachnoidal infection of mice, we chose to characterise PM induced by the TIGR4 genome strain. For this purpose, we performed time-dependent survival studies, bacterial counts in different organs/tissues, and histological analysis of brain and spleen (see below). In order to study animal survival, five groups of MF1 mice were infected with doses of TIGR4 increasing from 10 to 10rve Fig. . After i5 CFU of TIGR4 were sacrificed, samples collected and appropriately treated, and viable counts were carried out. Moribund animals showed comparable bacterial counts in the brain (3.1 × 106 ± 1.3 × 106 CFU/organ). Similarly, dissemination from the brain to vital organs occurred and was consistent in all animals, with bacterial counts of 3.8 × 106 ± 4.8 × 106 CFU and 2.1 × 108 ± 3.0 × 108 CFU in the spleen and blood, respectively (data not shown).To determine the number of pneumococci in brain, spleen and blood at the final stages of the disease, moribund mice infected with 10i.e. hunchbacked, photophobic, lethargic), but they did not develop hemiparesis or plegia. Moribund mice that had received 102, 103, 104, and 105 CFU were humanely killed at 72, 72, 48, and 24 hours post-infection, respectively. Two animals for each pneumococcal dose and three additional control mice were sacrificed. Brains and spleens were excised and treated for both haematoxilin-eosin and Gram staining.In order to prove the establishment of PM and study the features of the disease, we performed histological analysis on the brain of moribund mice inoculated with several doses of the TIGR4 strain and sacrificed at various time-points after infection. Animals at the final stages of the disease showed typical signs of meningitis , edema, and wisps of fibrin Fig. . Inflammulp Fig. , compareulp Fig. . These dS. pneumoniae is one of the causative agents of bacterial meningitis responsible for death and sequelae worldwide. The mouse has largely proved to be a reliable animal model for studying two major pneumococcal diseases such as pneumonia [neumonia -44 and sneumonia -47. In tneumonia -9,14 andneumonia -13 have neumonia , only reneumonia ,23.Cryptococcus neoformans [S. pneumoniae, and because the use of outbred strains is cost-effective. Another research group had previously used CD-1 outbred mice in a study on the efficacy of clinafloxacin against PM; however, the authors did not provide a detailed description of the model [In the present work, starting from an experimental method used to study meningitis caused by oformans , we deveoformans  and ratsoformans , as fronoformans ,51,52 anoformans  challenghe model .S. pneumoniae strains. The strains chosen are the serotypes routinely employed by researchers in the pneumococcal field, and have proved to be highly pathogenic in different mouse infection models [50 ranging from 2.9 × 10 to 1.9 × 102 CFU. Then, PM was further characterised and standardised by using TIGR4 as a model strain. Kaplan-Meier survival analysis of animals inoculated with different doses of TIGR4 showed that 105 CFU was lethal for all mice within 48 hours from infection  in the hippocampus of a few animals.The actual induction and subsequent characterisation of PM caused by the TIGR4 strain was then carried out by histological analysis of the brain tissue from moribund animals. We chose not to analyse PM by viable counting of pneumococci in the CSF, due to the difficulty of sample collection  and to the brain . In thathe brain . In anotna magna . In thatna magna . This firocesses . In our The present work proposes a method to induce experimental PM in outbred mice by using an i.c. subarachnoidal route of infection. The stereotaxic coordinates of the injection site are provided to allow easy recognition of the inoculation point in the mouse. The model is simple and fast, and the technique assures the development of meningitis, as demonstrated by histological analysis, survival data, and microbiological parameters. No significant differences were observed in the ability of the three pneumococcal strains used to cause disease, emphasising the value of the model. It is worth noting that the use of outbred mice still results in data reproducibility, as replicates in this model closely paralleled each other in terms of survival, CFU counts per organ, and histopathological features. In addition, experiments in outbred mice are cost-effective and can be performed in larger animal groups thereby improving statistical significance. This experimental PM model may be particularly useful for all researchers involved in studies that will investigate the host-pathogen interaction at the cerebral level, with emphasis on both pathogen-associated virulence factors and host-specific brain defences.S. pneumoniae was cultured in Tryptic Soy Broth  at 37°C with 5 % CO2. Solid media were obtained by addition of 1.5 % agar and 3 % defibrinated horse blood  to TSB. When necessary, streptomycin was used at the final concentration of 500 μg/ml.Survival studies were performed with the D39, HB565 and TIGR4 strains. TIGR4 was chosen as a model strain for histological characterisation of PM and CFU counts in organs. ad libitum. All animal experiments were approved by the Local Ethical Committee . Animals were allowed to settle in the new environment for one week before performing the experiments, they were caged and given food and water S. pneumoniae strains were prepared by using a modified version of a previously described method [2O. Passaged bacteria were grown to mid-exponential phase, centrifuged for 20 minutes at 1500 × g, resuspended in fresh TSB containing 10 % glycerol, and stored in aliquots at -70°C. Numbers of bacteria were determined by viable counting of serial dilutions in sterile phosphate-buffered saline, pH = 7.4 (PBS), and plating onto blood-agar plates. Before inoculation, bacteria were thawed at room temperature, harvested by centrifugation, and resuspended in sterile PBS at the appropriate dilutions.Mouse-passaged d method . BrieflyC. neoformans in mice [PM was induced in lightly anaesthetised mice (50 mg/kg ketamine and 3 mg/kg xylazine) by modifying a method previously used to establish meningitis by  in mice ,55. Anim4 CFU per mouse were used to infect mice (n= 4) with strains D39 and HB565. In the case of TIGR4, groups of 6 to 10 animals each were inoculated with doses ranging from 10 to 105 CFU per animal. Control mice were inoculated with PBS (30 μl). Mice were closely monitored twice a day for clinical symptoms . Mice were humanely killed before reaching the moribund state. Survival was recorded for 10 days (240 hours).Different bacterial doses ranging from 10 to 10Infected mice were sacrificed either for microbiological or histological analysis. Animals were humanely killed before being moribund, and various samples were collected. For CFU counts, blood was withdrawn by cardiac puncture before sacrifice and added to a tube containing 3.8 % of sodium citrate. Brains and spleens were excised and homogenised in 2 ml of sterile PBS. Bacterial counts in blood, brain and spleen were performed by plating 10-fold dilutions onto blood-agar plates. For histopathological analysis of tissues after infection with TIGR4, brains and spleens were immediately fixed in formalin for 24 hours and then embedded in paraffin according to standard procedures. The brains were entirely sectioned along a coronal plane. Sections were stained with both haematoxilin-eosin and Gram according to standard techniques. Morphological changes were assessed by using routine light microscopy. The presence and degree of inflammation were carefully evaluated.50 values were performed by using both the method by Reed and Muench [Calculations of LDd Muench  and Probd Muench . SurvivaDC, animal experiments, microbiological analysis, writing of manuscript. ST, histological analysis, writing of manuscript. RP, animal experiments, microbiological analysis. MRO, experimental design. EB, development of methodology for induction of meningitis. MC, co-ordination of histological analysis. GP, co-ordination and design of the study, data evaluation. SR, co-ordination and design of the study, data evaluation, direct supervision of experimental work, writing of manuscript.All authors read and approved the manuscript.Document stating the ethical approval for animal experimentation conceded to the Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.) from the University Hospital of Siena, the Medical Faculty and the Local Ethical Committee .Click here for file


A population-based case-control study was undertaken in 1997 to investigate the association between tetrachloroethylene (PCE) exposure from public drinking water and breast cancer among permanent residents of the Cape Cod region of Massachusetts. PCE, a volatile organic chemical, leached from the vinyl lining of certain water distribution pipes into drinking water from the late 1960s through the early 1980s. The measure of exposure in the original study, referred to as the relative delivered dose (RDD), was based on an amount of PCE in the tap water entering the home and estimated with a mathematical model that involved only characteristics of the distribution system.In the current analysis, we constructed a personal delivered dose (PDD) model that included personal information on tap water consumption and bathing habits so that inhalation, ingestion, and dermal absorption were also considered. We reanalyzed the association between PCE and breast cancer and compared the results to the original RDD analysis of subjects with complete data.th and >75th percentile when shorter latency periods were considered, and for exposures < 50th and >90th percentile when longer latency periods were considered. Overall, however, the results from the PDD analysis did not differ greatly from the RDD analysis.The PDD model produced higher adjusted odds ratios than the RDD model for exposures > 50The inputs that most heavily influenced the PDD model were initial water concentration and duration of exposure. These variables were also included in the RDD model. In this study population, personal factors like bath and shower temperature, bathing frequencies and durations, and water consumption did not differ greatly among subjects, so including this information in the model did not significantly change subjects' exposure classification. In 1988, an unusually high incidence of cancer in the Cape Cod region of Massachusetts prompted a series of epidemiological studies to investigate possible environmental risk factors associated with the region, including tetrachloroethylene-contaminated drinking water . TetrachA population-based case-control study was undertaken to investigate the association between tetrachloroethylene exposure from public drinking water and breast cancer . The stuBecause PCE is a volatile organic chemical that readily escapes from water into air, the amount of PCE inhaled during showers and baths, as well as the amount ingested and dermally absorbed, was relevant. The RDD measure does not consider these exposure pathways, which could potentially result in bias from exposure misclassification. Using personal exposure factors such as tap water consumption and bathing habits, we constructed a dose model to quantify the relative amount of PCE taken in by each subject, which we refer to as the personal delivered dose (PDD). The dose values calculated by the PDD model were subsequently used to measure the strength of the association between PCE exposure and the risk of breast cancer. The objective was to see if additional information contained in individual survey data affected associations between breast cancer and PCE exposure.The population-based case-control study was designed to evaluate the association between breast cancer and tetrachloroethylene (PCE) exposure from public drinking water . During Female controls were chosen to represent the underlying population that gave rise to the cases. Selection criteria required controls to be permanent residents of the same towns during 1987–1993. Controls were frequency matched to cases on age and vital status. Because many of the cases were elderly or deceased, three different sources of controls were used: (1) random digit dialing identified living controls less than 65 years of age; (2) Centers for Medicare and Medicaid Services, formerly the Health Care Financing Administration, identified the living controls 65 years of age or older; and (3) death certificates identified controls who had died from 1987 onward. The resulting 616 controls provide an estimate of the exposure distribution in the underlying population.Subjects or their next-of-kin completed extensive interviews, which provided information on demographics , a 40-year residential history, and potential confounders . Next-of-kin served as proxies for cases and controls who were deceased or too ill to participate in the interview. "Index years" were randomly assigned to controls to achieve a distribution similar to that of cases' diagnosis years and only exposures before the diagnosis year (for cases) and index year (for controls) were counted. The analysis considered a range of latent periods: 0, 5, 7, 9, 11, 13, 15, 17, and 19 years. For a detailed description of the methods, see Aschengrau et al. [If individual behavior in water use is an important element in a person's exposure, using the relative delivered dose (RDD) could bias the results. The RDD quantifies the amount of PCE in the drinking water, but does not consider exposure from inhalation, dermal absorption, and ingestion. PCE is a volatile organic compound and daily indoor inhalation exposure to contaminated water from showering can be up to six times greater than exposure from ingestion . To furtNon-proxy cases and controls were interviewed about many of these factors: the number of glasses of tap water consumed per day, including drinks made with tap water, such as coffee or lemonade; the use of bottled water; and the temperature, frequency, and duration of showers and baths. Information on a subject's physical characteristics, such as height and usual weight, was also obtained. Certain model parameters not provided by the questionnaire were obtained from the current scientific literature .We used this information to construct a personal delivered dose (PDD) model that considered three exposure routes: inhalation, dermal absorption, and ingestion. The RDD value was converted into an annual concentration and used as the initial water concentration for the PDD model (mg/L). The amount of PCE contributed by inhalation is a function of the temperature, frequency and duration of baths and showers, and the concentration of PCE in the bathtub/shower stall air. To determine the amount of PCE that volatilized from the water, the two-resistance theory was applied to temperature dependent physical and chemical properties of PCE . The derQuestions regarding tap water use and bathing habits were not asked in proxy interviews so the PDD analysis was restricted to non-proxy subjects ; race; marital status; religion; education level; and physical activity level. None of these additional variables changed the adjusted estimates by more than 10%, and so the final models included only the core confounders. Adjusted analyses were not performed if there were fewer than three exposed cases and three exposed controls in an exposure level [Exposure groups were further categorized for latent periods that ranged from 0 to 19 years. Each exposure level was treated as a binary variable in separate multiple logistic regression models. Odds ratios (ORs) were calculated for each exposure level relative to never-exposed cases (n = 360) and controls (n = 336). The adjusted analysis controlled for a group of core confounders: age at diagnosis or index year, family history of breast cancer, personal history of breast cancer (before current diagnosis or index year), age at first live birth or stillbirth, and occupational exposure to PCE. These factors were chosen as confounders re level . We calcre level .th percentile, greater than the 50th percentile, greater than the 75th percentile, and greater than the 90th percentile. The referent category remained never exposed cases and controls.We then repeated the crude and adjusted analyses using each subject's personal delivered dose (PDD) as an exposure measure. The PDD distributions of the exposed controls were used to define the same four exposure levels: less than or equal to the 50We also conducted a goodness-of-fit analysis to compare the RDD and PDD exposure measures and to determine which model performed better . We compth percentile, and 90th percentile RDD values for the non-proxy exposed controls were similar to the values for the exposed controls among all subjects .th and 75th percentiles at shorter latency periods  declare they have no competing interests.VV created the dose model, conducted the statistical analyses, and drafted the manuscript. AA provided the data and assisted in epidemiologic analysis and editing. DO participated in the design of the study and the editing of the manuscript. All authors read and approved the final manuscript.This document describes the dose model in more detail.Click here for fileThis document provides a table of adjusted odds ratios for breast cancer by tetrachloroethylene exposure levels in RDD and PDD analyses.Click here for fileThis document provides a table of deviance measures for logistic regression models by tetrachloroethylene exposure levels in RDD and PDD analyses.Click here for file


Several supervised and unsupervised learning tools are available to classify functional genomics data. However, relatively less attention has been given to exploratory, visualisation-driven approaches. Such approaches should satisfy the following factors: Support for intuitive cluster visualisation, user-friendly and robust application, computational efficiency and generation of biologically meaningful outcomes. This research assesses a relaxation method for non-linear mapping that addresses these concerns. Its applications to gene expression and protein-protein interaction data analyses are investigatedC. elegans interactome were analysed. Results suggest that this method might represent an effective solution for detecting key network hubs and for clustering biologically meaningful groups of proteins.Publicly available expression data originating from leukaemia, round blue-cell tumours and Parkinson disease studies were analysed. The method distinguished relevant clusters and critical analysis areas. The system does not require assumptions about the inherent class structure of the data, its mapping process is controlled by only one parameter and the resulting transformations offer intuitive, meaningful visual displays. Comparisons with traditional mapping models are presented. As a way of promoting potential, alternative applications of the methodology presented, an example of exploratory data analysis of interactome networks is illustrated. Data from the A relaxation method for non-linear mapping provided the basis for visualisation-driven analyses using different types of data. This study indicates that such a system may represent a user-friendly and robust approach to exploratory data analysis. It may allow users to gain better insights into the underlying data structure, detect potential outliers and assess assumptions about the cluster composition of the data. Systems biology is a data- and knowledge-driven discipline, which heavily relies on automated tools to support the generation and validation of hypotheses. Such tasks aim to provide novel and meaningful views of the functional relationships between biological components at different complexity levels. Over the past seven years hundreds of methods have been reported to analyse these data, with an emphasis on gene expression data classification ,2. More The ever-increasing number and sophistication of techniques may represent an obstacle to achieve more meaningful and rigorous data analysis and discovery tasks. One important problem is that users may not have the time and knowledge required to adequately understand the dynamics and operation of several tools. These deficiencies have been reflected, for example, in a lack of sound practices for assessing the statistical significance of results and for selecting the most suitable data sets and classification models ,7. On thdata visualisation techniques to support exploratory analysis. It has been shown that information visualisation techniques may support predictive data mining applications, including data clustering [The problem domains mentioned above have mainly concentrated on the application of statistical and machine learning models for classification tasks. Emphasis has been placed on the development of supervised and unsupervised classification methods ,10,11, austering -16. ThesKohonen Self Organising Maps (SOM) and Self-Adaptive Neural Networks (SANN) are relevant examples of techniques belonging to this approach. Their capabilities and applications have been widely reported [S. cerevisiae [One of the traditional approaches to functional genomics information visualisation has been the application of clustering-based visualisation techniques. Such an approach mainly consists of two steps: a) the implementation of a clustering algorithm, and b) the display of the obtained clusters. The resulting clusters may be visualised by generating, for example, dendrograms , other hreported ,15,20. Treported . Moreoverevisiae . Each norevisiae .non-linear mapping techniques. They are based on the idea of transforming the original, n-dimensional input space into a reduced, m-dimensional one, where m<n. These methods are also known as non-linear projection methods or multidimensional scaling (MDS) methods [M, which reflects key aspects of the distance structure of the original n-dimensional space. Thus, these methods aim to preserve such global properties in the transformed, m-dimensional space. This principle has been followed by several models including those proposed by Kruskal [Principal component analysis (PCA) [n-dimensional data. In this case the resulting transformation accounts for the greatest variation of the original space, but without preserving the distances observed between the points in the n-dimensional space. MDS, including Sammon's mapping, applications to gene expression analysis have been reported, which highlight their advantages for supporting the detection of clusters [Another important data visualisation approach, which may be applied to clustering-based analysis, comprises the application of  methods . They ma Kruskal ,24 and S Kruskal . Principis (PCA)  is anothclusters . PCA mayclusters .n-dimensional input space contains noisy or weakly discriminatory information [Although widely investigated techniques, such as SOM and Sammon's mapping, are usually useful to visualise clusters of high-dimensional data, they present several limitations. For example, the SOM may be highly sensitive to its training parameters, which have to be defined by the user. It also requires the user to define map topologies, and it does not provide automated mechanisms for cluster boundary detection. Its limitations for data exploratory analysis, particularly in relation to data topology preservation, have been stressed in . These aormation . Dependiormation . Moreoveormation ,31.This paper assesses an alternative, non-linear mapping technique which aims to address key limitations exhibited by traditional methods. Its application to clustering-driven exploratory analysis of gene expression data is investigated. Furthermore, it provides the basis for an interactome network clustering visualisation system. The following section summarises relevant results.m-dimensional space at every processing step, instead of adapting all points at every step. Thus, the term "relaxation" is taken from the relaxation method for linear equalities [mapping iteration is defined as a complete sequence of adaptation steps involving pairs of points, p, for each i ≠ j (see Methods for a more detailed description). In this study a point may encode a biological sample described by a gene expression profile (e.g. tumour sample), or a protein described by its interaction profile. Figure A relaxation method for non-linear mapping was implemented to visualise relevant similarity relationships in data originating from gene expression and interactome data. Such a method was designed by Chang and Lee  to addreualities . Chang'sAcute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). This data set has been previously validated by several experimental and in silico methods. It may be considered as an adequate example for illustrating basic capabilities of clustering algorithms. The second data set includes samples originating from small, round blue-cell tumours (SRBCT) [Ewing family of tumors (EWS), rhabdomyosarcoma (RMS), Burkitt lymphomas (BL) and neuroblastomas (NB), which are represented by the expression values of 2308 genes with suspected roles in processes relevant to these tumours. The third data set offers another example of gene expression analysis complexity: A data set consisting of a relatively small number of samples described by thousands of gene expression values, without incorporating pre-processing by feature selection or transformation procedures. This application comprises 20 samples described by 9504 gene expression values for both normal brain and a pharmacological model of Parkinson's disease [Analyses were performed on three publicly available expression data sets. The first one includes 38 samples from a known leukaemia study , which a (SRBCT) . These d disease . The reaA, where each entry, aij, represents an expression value, j, for a sample, i. The user needs to define only one learning parameter, the number of mapping iterations, as defined above. The output of the algorithm is the projection of the K samples represented in A on the reduced m-dimensional space. Mappings were analysed for m = 2 and m = 3, which allowed the generation of 2D and 3D visual displays. Experiments were also conducted for several numbers of mapping iterations. The results facilitated a high-level understanding of fundamental similarity relationships between the samples, which are also consistent with previous research. Three important exploratory data analysis tasks were accomplished: The automated, unsupervised detection of clusters relevant to the natural class structure of the data sets; the visualisation of a coherent preservation of local similarity (distance) relationships between samples; and the identification of potential outliers.Several experiments were performed on each data set to assess possible, key advantages and limitations of the mapping approach introduced above. Each experiment requires an input file storing a matrix, Figure x: 0.54, y: -1.79) that was located on the left side of Figure Figure Additional experiments using a smaller number of SRBCT classes  were performed. This was mainly done to explore the possibility of obtaining alternative graphical views of the data. Results are in general consistent with the results produced with 4 classes: Samples belonging to the same class were clustered together. However, these experiments allow a clearer graphical differentiation of classes on the resulting maps. Figure SOM Toolbox [unified distance matrix (U-matrix) and the label map (panel on the right side) for a representative result. The section of Methods describes the construction of these maps. The SOM was able to correctly cluster the SRBCT samples. However, these standard SOM visualisation techniques do not provide clear information on sample-to-sample similarity relationships. Moreover, they do not adequately facilitate a direct visualisation of the distribution of samples assigned to each node and their associations. SOM-based analyses using the  Toolbox ,37 were x: 1.2, y: -1.5) suggests another sample '1' as a potential outlier. However, the Sammon's mapping did not clearly depict it as a potential outlier because in this case, unlike Figure x: 0, y: 0.4).Figure 3D Mapping analyses were also implemented for this SRBCT application. Additional files y > 0), and 7 (out of 10) Normal samples are located below that area. Figure Figure N nodes, the mapping tool (from now on referred to as interClust) requires an N × N matrix, B, as the input data. In this symmetrical matrix each element, bij, represents the connection strength between nodes i and j in the graph. Such values may also be interpreted as weights representing the relevance of the interaction between a pair of proteins, e.g.: number of hits observed in interaction experiments or a path distance between two nodes on the graph (indirect interactions). Thus, each row in B may be seen as the connectivity profile for a node, i. That is, the connectivity profile of a node becomes this node's coordinates in the n-dimensional, input space. An accompanying tool, inBuilder, automatically generates such a network representation from a list of pairwise protein-protein interactions and their respective connection strengths predefined by the user. The section of Methods provides more information about design and operation aspects of this approach.As a way of promoting alternative, potential applications of the methodology presented, this section illustrates an example of exploratory data analysis of interactome networks. The approach proposed encodes a network as a graph of interconnected nodes. For a network consisting of interClust to this network with 20 iterations. This map clearly distinguishes the clusters of the network. Moreover, it preserves local interaction relationships: i.e. if two nodes have similar connectivity profiles in the original space  from the other components of the network. Hubs are located in the outer regions of the map. The farther a node is from the centre, the more connections it has. Figure Figure InterViewer tool [Emb) by exhibiting a 6-fold enrichment of this property in relation to genome-wide levels [wer tool . A closewer tool ). Moreove levels .Further analyses on the map suggest that neighboring nodes may reflect functional similarity relationships between the corresponding proteins. One example is illustrated in Figure With regard to gene expression data, the relaxation non-linear mapping method was capable to support an automated, unsupervised detection of relevant clusters of samples. Results demonstrated that it may also be useful for the visualisation of local similarity relationships between samples and the identification of potential outliers. In general its performance was comparable to Sammon's mapping. One cannot of course expect that a single method would always be able to accurately map different types of high-dimensional data. However, the application of both techniques is recommended as a reliable approach to data exploratory analysis. In this study they offered consistent views of the problems under analysis. SOM, as well as many other cluster analysis techniques, may be more suitable for application after first gaining an adequate insight into the structure and organisation of the data. Such a global understanding may be facilitated through the application of different non-linear mapping methods.C. elegans by exhibiting an enrichment of phenotypes obtained from RNA interference. This can be explained by the fact that the partners connected to these hubs are involved in numerous fundamental processes such as mitosis or meiosis. Hence, the modification of the network caused by the absence of a hub may have strong consequences on the topology of the network and the organisation of the cellular processes. Isolation of functional clusters  is essential to investigate relationships between groups of proteins or modules. Modules playing a role in the same process (e.g. chromosome condensation and segregation during the mitosis) also tended to be interrelated in the clustering analysis. This underlies the fact that these modules are also functionally interconnected and are interdependent to stringently regulate the cellular processes. Without these connections the process may be misregulated and generate aberrant behaviour (i.e. oncogenesis if the mitosis is not well regulated). In this way, interactions that connect these modules are likely to be at the intersection of several biological processes and to regulate the correct succession of events in a cellular process (e.g. condensation of chromosomes before segregation). We do not claim that the tool reported can be used as an interaction prediction technique. The example analysed aims to illustrate the application of exploratory data analysis for detecting regions, which may be biologically meaningful and relevant for assessing the outcomes from protein-protein interaction prediction techniques. Such patterns may be useful for guiding future computational or experimental analyses to validate interaction hypotheses. Meaningful patterns may be associated, for example, with functionally enriched regions, as shown in this paper. Moreover, because of the limitations regarding predictive accuracy and coverage exhibited by existing single-source techniques, it is important to offer user-friendly tools that may help scientists to detect possible, spurious associations. Future research should include a more exhaustive statistical description of all possible hub candidates detected by the mapping process for this and other network examples. Statistical and functional attributes represented by this method should be compared with previous findings from large-scale, comprehensive studies. Such an investigation was not implemented here because it is outside the main scope of this paper.Even though a comparison with existing network clustering methods was not implemented, preliminary results suggest that the approach proposed might represent a useful tool for interactome network visualisation and clustering. It distinguished key hubs and facilitated the identification of functionally relevant clusters. It showed that, not surprisingly, most of the hubs detected are essential for the normal development, behaviour and reproduction of dmij, in the transformed m-dimensional space should be very similar to dnij  if dnij is small. However, if dnij is relatively large, dmij is not required to be similar to dnij. A fundamental difference between the relaxation non-linear and the original Sammon's mapping methods is that the former adapts a point-to-point distance at every processing step, instead of adapting all of the distances at every step.The preservation of local distance structures is an important property to interpret the non-linear mapping techniques studied here. This is the main goal of their data transformation mechanisms. It basically means that the distance between two points, Matlab® implementations of SOM and Sammon's techniques, i.e. in the order of seconds. But for larger data sets, e.g. Parkinson's disease data, the non-linear mapping method may run in the order of minutes. This of course depends on the number of mapping iterations and memory resources available. This concern may be addressed by implementing an optimised version of the software, perhaps using another programming language. Another important solution is the implementation of a frame method [TIGR Multiexperiment Viewer [For relatively small data sets the computing times required by the Java-based implementation of the relaxation non-linear mapping method were comparable to those obtained from e method , which ht Viewer .Voronoi spaces using clustering techniques such as k-means and SOM, and then independent mapping projections may be performed on each area. It has been suggested that such a hybrid model may be advantageous especially when dealing with massive data sets [An important aspect of future research is the adaptation of the relaxation non-linear mapping method to perform tasks beyond exploratory data analysis. A desirable property would be its capacity to generalise solutions for new samples in an incremental fashion. That is, the system should be able to add new samples to a map without having to re-generate it. One possible solution is the application of an artificial neural network to interpolate and extrapolate the mapping as illustrated by Mao and Jain . It is aata sets . Other iata sets ,47bij, may also reflect non-direct, shared interactions. This may be done, for example, by defining a graph distance function between network nodes. Another input representation scheme may exploit information relevant to the significance or confidence assigned to the interactions based on experimental evidence.The mapping algorithm successfully recognised key topological properties and functional relationships in an interactome network based on a graph encoding scheme, which only considers direct interactions. Moreover, it considered all graph connections as equals. Nevertheless, it would be important to implement applications in which the network encoding values, interClust, may represent a useful technique to analyse other protein-protein interaction networks including a future human interactome. For instance, it may be applied to isolate proteins linked to a human pathology and to associate them with a cluster or functional modules . Another important component of future research is the adaptation of network clustering methods to take into account spatio-temporal aspects of interactions based on, for example, microarrays, in-situ hybridization or protein localization data. Non-linear mapping methods may also be applied to support the annotation of unknown proteins. This may be done by assigning a protein to a functional role that is significantly associated with the cluster under consideration. Furthermore, it is of course fundamental to compare this network clustering methodology with existing techniques. Thus, such approaches may aid researchers in the design of further experiments and the selection of more sophisticated bioinformatics analyses.Since cellular networks are organized in a modular fashion, the identification of these modules is crucial to understand relationships between biological processes and offer a higher-order, more accessible representation of the interactomes. The clustering approach proposed in this paper provides a meaningful, simplified representation of complex interactomes. This representation may significantly facilitate exploratory analysis of networks for non-specialists in bioinformatics. This type of analysis is fundamental to detect key network components, such as hubs, which are implicated in many physiological disorders. Identifying these hubs and their associated clusters is also an important step toward the functional annotation of these proteins, as well as for obtaining possible explanations of their involvement in a specific disease. The cluster visualisation tool, This research studied a user-friendly cluster visualisation approach that is able to support the generation of biologically meaningful outcomes. It represents an effective and robust exploratory data analysis technique. Comparisons indicate that applying more than one mapping approach may improve the confidence of results. Moreover, this may facilitate the generation of alternative, meaningful views of the data. Relaxation non-linear and Sammon's mapping techniques may be more suitable for exploratory data analysis tasks than SOM.This study did not aim to add another algorithm to the existing collection of supervised and unsupervised classification tools. This methodology is not reported as a competing solution to clustering algorithms. Our study shows how an exploratory data analysis approach based on non-linear mapping can support the identification of relevant, biologically-meaningful patterns. We do not argue that the methodology proposed should necessarily offer more accurate results in relation to existing classification solutions. We recommend this methodology as a first step towards understanding complex data mining problems in functional genomics. Such an exploratory approach may also facilitate the selection of more sophisticated methods and highlight possible, critical features for successfully implementing clustering-based studies. This research indicates that the outcomes originating from an exploratory, pattern visualisation method may be as meaningful as those produced by more sophisticated classification approaches, i.e. SOM. Moreover, the methodology proposed does not require the user to define multiple learning parameters.Exploratory analysis frameworks may facilitate a better insight into a data set before applying more sophisticated, problem-specific classification or predictive models. Such an insight may be achieved by helping users to recognise key features of the underlying structure of the data, detect potential outliers or anomalies and test assumptions about the cluster composition of the data.interClust, represents a promising solution to aid researchers to recognise key connectivity and functional patterns in interactome networks. Further research is underway to continue assessing its application to this area.An adaptation of the relaxation non-linear mapping technique, Acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). The original data sets and experimental protocols can be found at the Broad Institute Web site [The leukaemia data set includes 38 samples originating from . Each saWeb site . For eacEwing family of tumors (EWS), rhabdomyosarcoma (RMS), Burkitt lymphomas (BL) and neuroblastomas (NB), which were represented by the expression values of 2308 genes. The dimensionality of the SRBCT expression samples was reduced by applying PCA. It has been shown that the application of PCA is important to facilitate an adequate discrimination of samples in this data set. The 10 dominant PCA components for each case were used as the input to the analysis techniques as suggested by [The SRBCT data consisted of 63 samples categorised into four classes: ested by , who appested by .normal brain (10 samples) and a pharmacological model of Parkinson's disease [M. musculus was the organism studied in this disease model. The data are available at The Gene Expression Omnibus [The Parkinson's disease data include 20 samples described by 9504 gene expression values for both  disease . M. muscAdditional files C. elegans. It contains 303 proteins and 349 interactions. It comprises components of the Polymerase II holoenzyme, histones modifying enzymes, nucleosomes positioning proteins and several proteins containing domains known to be essential to this process such as the chromodomain, the bromodomain or the SET domain. This is an early version of the chromatin interactome, which includes a number of retested [The network of interactions was derived from the chromatin interactome in retested  as well retested ,51. A coi and j, in the transformed, m-dimensional map is implemented as follows. Given two points, Pmi and Pmj, in the m-dimensional map, the adjusted new values, Pmnew, i and Pmnew, j, are calculated using:The relaxation non-linear mapping algorithm is summarised in Figure dnij and dmij represent the Euclidean distances between the points, i and j, in the n- and m-dimensional spaces respectively.where SOM Toolbox, which is a Matlab® implementation [The SOM results were obtained using the entation ,37. EachinBuilder was used to transform it into interClust input format. The cross-platform tools interClust and inBuilder are available for academic researchers on request from the authors.For the interaction data set, the tool Statistica©. InterViewer [Pentium® 4 CPU.Graphical outputs for the relaxation maps were obtained with the proprietary software erViewer , which ierViewer . AnalyseFA designed the study, implemented the relaxation non-linear mapping algorithm, performed analyses and wrote the manuscript. HW did the SOM and Sammon's mapping experiments and helped with the preparation of the manuscript. AC selected the interactome data, interpreted results and helped with the preparation of the manuscript.SOM frequency map for SRBCT data It shows the distribution of samples, X(Y), over each node in Figure X represents the class label and Y stands for the number of Class X samples assigned to the corresponding node.Click here for file3D visual display originating from relaxation non-linear mapping – SRBCT data EWS, RMS and BL samples are represented by symbols '1', '2', '3' respectively.Click here for file3D Sammon's mapping results – SRBCT data Symbols "1", "2" and "3" represent classes EWS, RMS and BL respectively.Click here for fileSOM frequency map for Parkinson's disease data It shows the distribution of samples, X(Y), over each node in Figure X represents the class label and Y stands for the number of Class X samples assigned to the corresponding node.Click here for file3D Relaxation non-linear mapping of the Parkinson's disease model data Parkinson's disease and Normal samples are identified by symbols '1' and '2' respectively.Click here for file3D Sammon's mapping of the Parkinson's disease model data Symbols "1" and "2" represent Parkinson's disease and Normal samples respectively.Click here for fileExamples of key hubs in the interactome It depicts some of the hubs automatically isolated by the mapping algorithm, as well as a few nodes located near the centre of the map .Click here for fileDescription of protein cluster obtained from Figure 14Click here for fileLeukaemia data set analysed in this paper Log ratios are used to represent the expression levels. The last column shows the class labels.Click here for fileSRBCT data set analysed in this paper Log ratios are used to represent the expression levels. The last column shows the class labels.Click here for fileParkinson's disease data set analysed in this paper Log ratios are used to represent the expression levels. The last column shows the class labels.Click here for fileC. elegans Chromatin interaction network in The last column shows the connection strength in the graph. All connections are considered equally.Click here for file