The enteric subgroup F adenoviruses type 40 (Ad40) and 41 (Ad41) are the second most important cause of acute infantile gastroenteritis after rotaviruses. Repeated community outbreaks have been associated with antigenic changes among the Ad40 and Ad41 strains due to host immune pressure. Therefore large field epidemiological surveys and studies on the genetic variations in different isolates of Ad40 and Ad41 are important for disease control programs, the design of efficient diagnostic kits and vaccines against subgroup F adenoviruses. A novel method using sodium dodecyl sulphate SDS/EDTA-pretreated chromatography paper strips was evaluated for the collection, storage and shipping of Ad40/41 contaminated stool samples.This study shows that adenoviral DNA can be successfully detected in the filter strips by PCR after four months storage at -20°C, 4°C, room temperature (20–25°C) and 37°C. Furthermore no adenoviral infectivity was observed upon contact with the SDS/EDTA-pretreated strips.Collecting, storing and transporting adenovirus type 40 and 41 positive stool samples on SDS/EDTA-pretreated chromatography filter strips is a convenient, biosafe and cost effective method for studying new genome variants and monitoring spread of enteric adenovirus strains during outbreaks. Enteric adenoviruses (EAds) are considered to be the second most important causative agent of acute infantile gastroenteritis after rotaviruses. The fastidious subgroup F adenoviruses type 40 (Ad40) and 41 (Ad41) account for the majority of cases of severe acute diarrhea in children less than 2 years of age -5. The cPrevious studies have demonstrated the application of different filter papers for the collection and storage of blood , saliva This study describes the use of SDS/EDTA-pretreated filter paper strips in collection, transportation and storage of adenovirus type 40/41 positive stool samples for subsequent genetic analysis.In the current study we describe the use of chromatography paper strips for the collection, transportation, and storage of EAds type 40/41 positive stool samples. In order to inactivate the adenoviruses and other microorganisms upon contact with the strips, the latter were pretreated with SDS, a surfactant with protein denaturising ability. This can allow safe transportation of the strips without extensive biohazard precautions. To protect the viral DNA from degradation by deoxyribonucleases (DNases) the chromatography strips were also preincubated with EDTA, and Tris-HCl. EDTA chelates magnesium ions, a necessary co-factor for most nucleases and the weak organic base Tris-HCl ensures the proper action of the chelating agent in binding the divalent cations.6 adenoviral particles per ml was serially diluted 1:8 (dilution a), 1:80 (dilution b), 1:800 (dilution c), 1:8000 (dilution d) and 1:80,000 (dilution e). The SDS/EDTA-pretreated filter paper strips were infected with each stool dilution and stored at -20°C, 4°C, room temperature (20 to 25°C), and 37°C. The presence of adenoviral DNA on the chromatography filter strips was detected by PCR amplification of a 301 bp fragment of the adenoviral hexone gene after storage for 7 days, 14 days, 56 days and 120 days  after three passages of the infected cell line. The eluate of the untreated chromatography strips loaded with adenovirus type 1 caused cytopathic effect in the HeLa cell line. It can be concluded that the SDS/EDTA-pretreated strips can be used for the collection and shipping of adenovirus positive stool samples from remote areas to reference laboratories in a biosafe way.To be sure that the EAd40/41 contaminated filter strips are not infectious, we carried out a biosafety test to find out if any adenovirus could survive onto the SDS/EDTA-pretreated strips. Previously we showed that pathogenic bacteria such as A strips . The bioWe conclude that the use SDS/EDTA-pretreated filter strips for retrieval and subsequent analysis of adenoviral DNA from EAds type 40/41 positive stool specimens is a feasible method for sample collection. The described filter paper strips facilitate collection, transport and storage of adenoviral positive stools because they are biosafe, cost effective and require minimal storage space. This study shows that adenoviral DNA can remain stable for at least 4 months at 37°C temperature conditions making this method especially attractive for field research or population screening in tropical countries where freezers are not available.2) Whatman grade 17 chr pure cellulose chromatography paper with thickness of 0.92 mm and a flow rate of 190 mm/30 min (Whatman, Kent, United Kingdom) was used. Strips of 80 mm × 4 mm were cut from the chromatography paper and soaked for two minutes in a solution of 2% (w/v) sodium dodecyl sulphate (SDS), 10 mM EDTA and 60 mM TrisHCl. The chromatography paper strips were left to dry overnight at room temperature. Disposable gloves were used during the preparation of the filter paper strips.Highly absorbent (870 g of water/m®-Type 40/41 solid-phase sandwich enzyme immunoassay (Meridian Bioscience, Cincinnati, Ohio) was used for this study. The undiluted feces sample contained approximately 2.6 × 106 particles per ml of stool, as calculated from a standard curve supplied with the antigen enzyme immunoassay kit. The stool sample was diluted in 1 ml (dilution 1:8) DNase/RNase free water (Sigma) and the following dilutions were used: 1:8 (dilution a), 1:80 (dilution b), 1:800 (dilution c), and 1:8000 (dilution d), and 1:80000 (dilution e). The pretreated chromatography strips were infected with 100 μl of the different dilutions of stool sample and were left to air dry overnight at room temperature. After complete drying, the infected strips were stored under four different temperature conditions: -20°C, 4°C, room temperature (20 to 25°C) and 37°C.A diarrhea stool sample that was positive for adenovirus type 40/41 hexon antigen by the Premier Adenoclone2) was used for the DNA extraction performed at the following storage time intervals: 7, 14, 56 and 120 days. The filter paper was inserted into an Eppendorf tube with 500 μl of Dnase/Rnase free water (Sigma) and thoroughly squeezed out. An aliquot of 200 μl of the squeezed eluate was used for DNA extraction using the QIAamp DNA Blood Mini Kit (Qiagen/Westburg, Leusden, The Netherlands) according to the manufacturer's instructions. A set of degenerate consensus primers (forward primer 5'-GCCSCARTGGKCWTACATGCACATC-3' and (reverse primer 5'-CAGCACSCCICGRATGTCAAA-3') were used to amplify a 301 bp fragment of the adenoviral hexone gene [2, and 1 unit Taq polymerase (Applied Biosystems, Foster City, CA). The PCR was conducted in a Geneamp PCR System 9600 thermal cycler (Applied Biosystems). The thermocycling conditions consisted of denaturation at 94°C for 3 min, followed by 35 cycles of 30 s at 94°C, 30 s at 55°C and 1 min at 72°C and 5 min of final elongation at 72°C. PCR products were visualized using polyacrylamide gel electrophoresis and ethidium bromide staining.Half of the strip (160 mmone gene . The PCRin vivo. Therefore adenovirus type 1 was used for the biosafety experiments. The SDS/EDTA-pretreated filter stips were first infected with 100 μl of the HeLa cell cultured adenovirus type 1 (106 TCID 50/ml) and allowed to dry at room temperature for 60 min. The strips were then placed into an eppendorf tube containing 500 μl Dulbecco's Modified Eadle Medium (DMEM) (Invitrogen, Merelbeke, Belgium) supplemented with 200 mM L-glutamine (Sigma-Aldricht, Bornem, Belgium). The strips were thoroughly squeezed in the medium and the eluate was dialyzed using 3,500-Da Slide-A-Lyzer dialysis cassettes (Pierce Biotechnology, Rockford, IL, USA) to remove the cytotoxic SDS. The dialyzed eluate was inoculated on a confluent monolayer of HeLa cells and was incubated at 37°C in a humified incubator with a 5% CO2 environment. Untreated strips infected with adenovirus type 1 and noninfected SDS/EDTA-pretreated strips were used as positive and negative controls respectively. The presence of cytopathic effect indicated the presence of live replicating virus on the strip. Cytopathic effects were monitored up to the third passage of the tissue culture supernatant.A biosafety experiment was performed to check if adenoviral particles are still infectious after contact with the SDS/EDTS-pretreated chromatography paper strips. Since Ad40 and Ad41 grow poorly in cell culture it is difficult to detect these viruses Eads – enteric adenovirusesEDTA – ethylenediamine tetra-acetic acidSDS – sodium dodecyl sulphateThe author(s) declare that they have no competing interests.KZ conducted the study, carried out the experiments and wrote the manuscript. PM carried out the biosafety experiments. MR developed the filter paper strip sampling method. MVR supervised the study and revised the manuscript. All authors read and approved the manuscript.


Male breast cancer is a rare but aggressive and devastating disease. This disease presents at a later stage and in a more advanced fashion than its female counterpart. The immunophenotype also appears to be distinct when compared to female breast cancer. Angiogenesis plays a permissive role in the development of a solid tumor and provides an avenue for nutrient exchange and waste removal. Recent scrutiny of angiogenesis in female breast cancer has shown it to be of significant prognostic value. It was hypothesized that this holds true in invasive ductal carcinoma of the male breast. In the context of male breast cancer, we investigated the relationship of survival and other clinico-pathological variables to the microvascular density of the tumor tissue.Seventy-five cases of primary male breast cancer were identified using the records of the Saskatchewan Cancer Agency over a period of 26 years. Forty-seven cases of invasive ductal carcinoma of the male breast had formalin-fixed paraffin-embedded tissue blocks that were suitable for this study. All cases were reviewed. Immunohistochemical staining was performed for the angiogenic markers (cluster designations 31 (CD31), 34 (CD34) and 105 (CD105), von Willebrand factor (VWF), and vascular endothelial growth factor (VEGF)). Microvascular density (MVD) was determined using average, centre, and highest microvessel counts (AMC, CMC, and HMC, respectively). Statistical analyses compared differences in the distribution of survival times and times to relapse between levels of MVD, tumor size, node status and age at diagnosis. In addition, MVD values were compared within each marker, between each marker, and were also compared to clinico-pathological data.Advanced age and tumor size were related to shorter survival times. There were no statistically significant differences in distributions of survival times and times to relapse between levels of MVD variables. There was no significant difference in MVD between levels of the different clinico-pathological variables. MVD was strongly and significantly correlated between AMC, CMC and HMC for CD31, CD34, and CD105 (p < 0.01) and remained moderate to weak for VWF and VEGF.Microvascular density does not appear to be an independent prognostic factor in male breast cancer. However, the likelihood of death for men with breast cancer is increased in the presence of increased age at diagnosis and advanced tumor size. This is perhaps linked to inherent tumor vasculature, which is strongly related throughout a tumor section. Invasive ductal carcinoma of the male breast is distinct from invasive ductal carcinoma of the female breast in both presentation and immunophenotype. Male breast cancer generally presents in older patients and at a more advanced stage than its female counterpart . In conte in men ,5. Male e in men ,7. The ce in men -10.3 is dependant upon the formation of new vasculature [Angiogenesis is the growth and proliferation of blood vessels from existing vasculature. This process is quiescent in normal tissues and becomes active in rapidly growing tissues – including solid tumors. It has been shown that, in order to overcome tissue death by hypoxia, tumor growth beyond 1–2 mmculature . Angiogeculature  non-smalculature ,14, hepaculature , melanomculature  prostateculature , breast culature -24 and bculature .Most assessments of angiogenesis in female breast carcinoma have shown it to be of significant prognostic value -22. HoweInvasive ductal carcinoma of the male breast appears to be a unique and biologically different carcinoma ; it is nIn an attempt to further characterize this rare tumor, the aim of the current study was to evaluate angiogenesis in invasive ductal carcinoma of the male breast by the assessment of microvascular density in tumor samples. Specifically, we investigated three questions: (1) do survival times and times to relapse differ between levels of MVD, demographic, and clinico-pathological variables; (2) do MVD measures differ between levels of demographic and clinico-pathological variables, and finally; (3) are different measures of MVD correlated within a section of tumor tissue? This study is an extension of our established work on immunophenotypic characterization of male breast carcinoma in Saskatchewan .After obtaining appropriate ethics approval from the University of Saskatchewan Advisory Committee on Human Experimentation, all cases (n = 75) of invasive ductal male breast cancer diagnosed between 1975 and 1997 were selected from the records of the Saskatchewan Cancer Agency. Detailed chart review was performed for cases where paraffin-embedded tissue samples were available (n = 59). After the removal of all cases with inadequate tissue sample, tissue staining and chart data, there remained 47 cases.Sections were cut from paraffin-embedded tissue samples. The sections were stained with hematoxylin and eosin (H & E). A detailed histopathological assessment was performed. Clinical features were recorded including age at diagnosis, date of birth, node status, tumor size, treatment method, date of relapse, and date of death.Age at diagnosis, tumor size, node status, disease-free survival and overall survival were the clinical variables of interest in this study. Age at diagnosis was determined from the patient chart. Tumor size and node status were determined from the pathology report. Overall survival (number of years patient survived since the diagnosis of invasive breast carcinoma) and disease-free survival  were calculated from the information gathered in the chart review. In cases where multiple tissue blocks were available, all H & E sections were examined in order to select a representative tissue block with a large area of invasive tumor and satisfactory tissue integrity.et al., [et al., [MVD determination was modeled after the method described by Kato et al.,  and Weid[et al., . ImmunohBrown-staining areas, whether single endothelial cells or clusters of endothelial cells, regardless of the absence/presence of a lumen were counted as individual microvessels. Vessels that had a thick muscular layer were excluded from the count. Cases were evaluated in a random order. Two observers using a double-headed light microscope simultaneously performed all counts for CD31, CD34, VWF and VEGF. A single experienced observer assessed CD105. Observers were blinded to all clinical and pathological data. Average, central and highest microvessel counts (AMC, CMC, and HMC, respectively) were performed.Ten high power (200×) fields along the border between cancer nests and the stroma were evaluated for each section figure . The aveAfter scanning at low power (40×), the central area of the tumor was estimated. From this area, six high power 200×) fields were evaluated for each section (figure 00× fieldAfter scanning at low power (40×), three areas with the highest concentration of microvessels (vascular hot spots) were selected. Each area was evaluated with one high power (200×) field in such a way as to include the maximum number of microvessels figure . The higAnalysis was completed using the Statistical Package for the Social Sciences (SPSS) version 11.0 on an IBM PC 300PL computer. All tests were two tailed with the level of statistical significance set at p < 0.05. The demographic and clinico-pathological variables of interest included age at diagnosis (<65 and ≥ 65 years), tumor size (T1 is ≤ 2 cm, T2 is >2 cm but ≤ 5 cm, and T3 is >5 cm) and node status (positive and negative).To compare the distribution of survival times and disease free survival times (time to relapse) we produced Kaplan-Meier curves and made statistical comparisons using the log-rank test between levels of demographic and clinico-pathological variables. In addition to this we dichotomized the MVD variables based on the median and repeated the Kaplan Meier with log-rank analysis to compare survival times and times to relapse between levels of MVD. For comparison of survival times, the outcome of interest was death while the remaining subjects (those surviving to the end of the study period) were censored. For comparison of time to relapse, the outcome of interest was relapse while the remaining subjects (those surviving to the end of the study or those who died before relapse) were censored.Levels of MVD were also compared with levels of demographic and clinico-pathological variables using the Mann Whitney test or Kruskal Wallis test when MVD was considered as a continuous variable and chi squared or Fisher's Exact test when MVD was considered as dichotomous variable.Finally, for each vascular marker (CD31, CD34, CD 105, VWF, and VEGF), correlation between the different measures of MVD (i.e. AMC with CMC, AMC with HMC, and CMC with HMC) was assessed using the Spearman's correlation coefficient. Correlations with a coefficient (ρ) of ≥ 0.80 were considered strong, moderate-strong correlations had coefficients that were <0.80 but ≥ 0.50, moderate-weak correlations had coefficients that were <0.50 but ≥ 0.30, weak correlations had coefficients that were <0.30.In this study of 47 cases of male breast cancer, the median age of diagnosis was 65.9 years with the youngest being 32 years and the oldest being 94 years. The frequency of male breast cancer cases by age is illustrated in figure All patients underwent some form of surgical resection – most frequently a modified radical mastectomy. In 31 out of 47 cases, surgical resection was followed by some form of adjuvant therapy , or some combination of the aforementioned). Specifically, 6 patients received only radiotherapy, 7 patients received only hormonal therapy and 2 patients received only chemotherapy. For combined therapies, 6 patients received radiotherapy with hormonal therapy, 3 patients received radiotherapy with chemotherapy, 4 patients received hormonal therapy with chemotherapy and 3 patients received all three methods of adjuvant therapy.All cases reviewed in this study came from the records of the Saskatchewan Cancer Agency between 1975 and 1997. Thirty-three of 47 patients (70%) died in the time period considered. Of the remaining 14 patients (30%), 9 (64%) had been followed for 10 years or more and 5 (36%) patients had been followed for under 10 years. Seventeen patients (36%) had documented relapse. The average age at death for patients with relapse was 72 years while the average age at death for relapse-free patients was 78 years. Although 70% of patients did die in this study, thirty-two patients (68%) survived at least 5-years after the diagnosis of breast cancer.The Kaplan-Meier curves relating prognostic variables to death and relapse are illustrated in figure Regardless of whether MVD was considered as a continuous variable or as a categorical variable, there were no significant differences in MVD by demographic (age group) or clinico-pathological features (tumour size or node status) although some of the differences may be clinically important. Table Measures of MVD (AMC, CMC and HMC) were compared within each marker. These correlations are illustrated in figure Significant correlations (p < 0.01) were observed between all methods of measure (AMC correlates with CMC, CMC correlates with HMC, and HMC correlates with AMC) for CD31. The correlations between AMC and CMC, and AMC and HMC were moderate-strong (ρ = 0.76 and ρ = 0.60 respectively); the correlation between CMC and HMC was strong (ρ = 0.88).Significant correlations (p < 0.01) were observed between all methods of measure (AMC correlates with CMC, CMC correlates with HMC, and HMC correlates with AMC) for CD34. All correlations were moderately-weak or moderate-strong (ρ = 0.45 for AMC and CMC, ρ = 0.41 for AMC and HMC, and ρ = 0.77 for CMC and HMC).Significant correlations (p < 0.01) were observed between all methods of measure (AMC correlates with CMC, CMC correlates with HMC, and HMC correlates with AMC) for CD105. The correlations between AMC and CMC, and AMC and HMC were moderate-strong and moderate-weak (ρ = 0.62 and ρ = 0.49 respectively); the correlation between CMC and HMC was strong (ρ = 0.82).A significant correlation (p < 0.01) was observed between CMC and HMC for VWF. A trend correlation (p < 0.10) was observed between AMC and HMC. Correlations were moderate-weak and weak (ρ = 0.47 and ρ = 0.25 respectively). There was no significant relationship between AMC and CMC for this marker.Significant correlations (p < 0.01) were observed between AMC and CMC, and between CMC and HMC for VEGF. Correlations were moderate-weak and moderate-strong (ρ = 0.43 and ρ = 0.68 respectively). There was no significant relationship between AMC and HMC for this marker.VEGF also called vascular permeability factor (VPF) is an important angiogenic activator, for both physiological and pathological angiogenesis ,30, and VEGF mRNA has been shown to be up-regulated in the majority of human tumors investigated , and caret al., [An excellent review of CD105 and its involvement in angiogenesis has been written by Duff et al., . CD105 , is a plasma protein produced by endothelial cells ,82. VWF VWF is a multifunctional protein. It is known to mediate adhesion/aggregation of platelets in clot formation (reviewed in ). In addMale breast cancer is a disease of older men. The likelihood of this occurring in older men that is illustrated in this study is not surprising as this is the case for most studies of e breast cancer in males ,89. As mIn this study, 70% of the reviewed patients died. Though this number may seem high, only half of those who died had documented relapse prior to the time of death. There is, however, an interesting difference between average age at death for relapsed and relapse-free patients, 72 years versus 78 years respectively. It appears that male breast cancer is contributing to mortality, but this study did not examine the effects of co-morbid conditions. The expected life remaining for a 65 year old male in Saskatchewan between 1995 and 1997 was 16.7 years (expected age approximately 82 years) .Increased tumor size increases the likelihood of death for male breast cancer patients in this study figure . One posIt appears that younger patients had a significantly better chance of not experiencing death figures . This phThis study was not able to clearly demonstrate statistically significant differences in survival for node status. In the available literature axillary node status is an important prognostic factor -94.Microvascular density, though it was the primary focus of this study, did not demonstrate statistically significant association with survival, demographic or clinico-pathological features. However, we cannot discount the importance of angiogenesis in tumor progression. The lack of correlation in this study may have been influenced by the lack of statistical power, the methods used, the age of the tissue, advanced stage of disease at presentation and method of analysis.In most tumors studied, MVD has been identified as a prognostic factor and has had important correlations to clinical variables -16. In met al., [et al, [In an angiogenesis methodology study of 109 women with breast cancer by Kato et al.,  it was f [et al, .Despite a lack of strong evidence, in our study, to support angiogenesis as an independent prognostic factor, there is no evidence to disprove angiogenesis plays a critical role in tumor development. As angiogenesis remains a likely step in tumor progression, we must continue to recognize this process as a potential target for anti-tumor therapy.There were some important correlations between the different methods of measure for MVD (AMC, CMC and HMC) within the various markers. CD31, CD34 and CD105 were the strongest in this regard with correlations that were very significant (p < 0.01) and correlations that were usually moderate to strong. The correlations within VWF and VEGF were not all significant, and the relationship was moderate to weak.It could also be that VWF and VEGF are differentially expressed in male breast cancer tissue. This seems to be the case for VEGF. In fact, it was observed that VEGF had a propensity to be over-expressed in regions where there were invading lymphocytes. This may produce a patchy pattern of expression, which could have an important effect on microvessel counts.For the most part, this study saw strong correlations between the various microvessel count methods within the markers. Critics may suggest that evaluation of microvascular density for prognosis in tumors is flawed because, within a tumor, microvascular density is heterogeneous ,95. HoweMicrovessel determination, by the methods used in this study, is dependant on a predictable pattern of vasculature within a tumor. Such predictability allows for practical (in terms of time, money and ease of use) application of important clinical prognostic features of the markers. Further research to examine the relationship between these markers in cancer is wanting. Such information may prove important in improving the prognostic value of MVD determination.From this evaluation of angiogenesis in male breast cancer, we can draw the following conclusions:Microvascular density does not appear to be an independent prognostic factor in male breast cancer. Tumor vasculature  is strongly related throughout a tumor section (p < 0.01). Other endothelial markers such as VWF and VEGF appear to have a moderate to weak relationship. Advanced age at diagnosis and increased tumor size increases the likelihood of death for men with breast cancer.AMC Average microvessel countCD# Cluster designation or cluster of differentiation (CD31, CD34, CD105)CMC Central microvessel countHMC Highest microvessel countMVD Microvessel densityTNM Tumour nodes metastasisVEGF Vascular endothelial growth factorVWF Von Willebrand factorThe authors declare that they have no competing interests.EF wrote this manuscript, aided in collection and analysis of data and is the first author.JL provided statistical analysis of the collected data.RK conceived the design of this study, aided in data collection and remains the corresponding and senior author.All authors have read and approved this manuscript.


Cancer is being increasingly recognized as a very heterogeneous disease, both within an individual tumor and within a tumor type and among tumor types. This heterogeneity is manifested both at the genetic and phenotypic level and determines the progression of disease and response to therapy. It is possible to see the heterogeneity in examples of differential disease progression and response to therapy of the same tumor type, as morphology does not always reveal underlying biology. The diagnosis of tumors by histopathological and morphological criteria cannot fully account for the variability seen in prognosis and therapy outcome. Here we review some recent concepts that have emerged from the genetic analysis of metastatic melanoma. Cancer is being increasingly recognized as a very heterogeneous disease, both within an individual tumor and within a tumor type and among tumor types. This heterogeneity is manifested both at the genetic and phenotypic level and determines the progression of disease and response to therapy. It is possible to see the heterogeneity in examples of differential disease progression and response to therapy of the same tumor type, as morphology does not always reveal underlying biology. The diagnosis of tumors by histopathological and morphological criteria cannot fully account for the variability seen in prognosis and therapy outcome. A classic example of this heterogeneity is diffuse large B cell lymphoma (DLBCL) morphologically defined as one tumor type but only 40% of patients respond to treatment suggesting there are at least two distinct tumor groups. Alizadeh et. al. performed microarrays on DLCBL to assess gene expression profiles and identified several genetically distinct groups that correlated with differential survival rates , reviewe]. GenomeWhereas previously, it was possible only to study one or a few genes at a time, microarray technology allows the simultaneous assessment of the expression of thousands of genes within a cell population at a single time. By looking at the full spectrum of the genetic contribution within a tumor, microarray technology has furthered our understanding of the complexity in terms of tumor subclassification. The advantage of a global gene expression analysis is that it assesses many genes within a sample at a given timepoint and allows comparison to a myriad of other samples. This has resulted in the improved classification of tumors, identification of potential new biomarkers, and detection of possible therapeutic targets as in DLBCL. In addition, the same gene expression data can be reanalyzed according to a user defined phenotype or without bias while looking for patterns in the data that correlate with a phenotype such as progression, prognosis, or treatment outcome. As data analysis and data mining become more sophisticated, the information acquired will provide scientists and clinicians with a significant improvement in correlating patient data with tumor diagnosis and enabling us to better select patient groups who will respond (or not respond) to therapy. Microarray technology has become the best hope in developing a global and accurate assessment of the tumor type and all its complexity. However, the road to achieve this goal will be long and hard because we have to learn to ask the right questions, select the appropriate patients, collect their material and then verify the initial results.in vitro. This showed that lesions can be stratified into subtypes by gene expression analysis. Further, microarray gene expression data can be used to define responders and non-responders to known anti-cancer treatments prospectively or retrospectively. By combining clinical data with microarray data, it will be possible to predict patient response based on gene expression profile or biomarkers, which may allow for better, more targeted therapies to be selected. This new information will lead to improved treatments and prolonged survival for cancer patients.Clinical pathological analysis cannot predict clinical outcome or metastatic potential of melanoma, a very heterogeneous cancer with an unpredictable progression rate. In a previous issue, Wang et. al. performed gene expression analysis on RNA from a number of human solid tumor lesions, including melanoma . In theiDNA microarray technology may help us understand the complex pathogenesis of melanoma and will allow us to determine the role of the different genetic profiles in determining different disease outcomes. From this we will be able to identify new biomarkers, leading to the development of more pathologically relevant models. To achieve better prediction for optimal treatment strategies, microarray studies as presented here are only the beginning of a long road, in which we need to drastically par down the markers to be tested. We need to verify and validate biomarker candidates in ways that go beyond the capacity of individual laboratories. Instead we need to establish consortia of scientists from bioinformatics and computational biology, who team up with oncologists, pathologists, and immunobiologists. Any selected biomarker requires validation in independent multi-center analyses. Once the appropriate tools and infrastructures are on hand, we can select better new treatment modalities and may realize that previously unsuccessful regimens would have shown more success, if we would have know how to select most appropriate patients. We have to start now to develop the groundwork for such multidisciplinary, multi-institutional work that will challenge us in the years to come.


DJ-1 were recently described in two families with autosomal recessive inherited PD (Parkinson's disease (PD) pathology is characterized by the degeneration of midbrain dopamine neurons (DNs) ultimately leading to a progressive movement disorder in patients. The etiology of DN loss in sporadic PD is unknown, although it is hypothesized that aberrant protein aggregation and cellular oxidative stress may promote DN degeneration. Homozygous mutations in rited PD . In a corited PD , we show The interaction of the proteins DJ-1 and α- synuclein described here may be important for understanding the molecular basis of Parkinson's disease Parkin, lead to autosomal recessive primary Parkinsonism (Parkinson's disease (PD) is a progressive movement disorder that is characterized pathologically by the relatively selective degeneration of midbrain DNs and the presence of prominent intracytoplasmic neuronal inclusions, termed Lewy bodies . The ideinsonism . Parkin insonism .DJ-1, were recently associated with autosomal recessive primary Parkinsonism , many of which function as molecular chaperones to assist other proteins in folding. Hsp31, an in vitro . We hypoin vitro A. The chin vitro B.DJ-1 similarly functioned as a molecular chaperone in the context of the heat-induced aggregation of GST see . In cont2O2 , and subsequently chaperone activity was measured in the CS thermal aggregation assay. H2O2 effectively reactivated the chaperone activity of DTT-treated DJ-1 (2O2 on CS aggregation, as H2O2 treatment of CS increased aggregation (unpublished data). These results suggest that redox regulation of DJ-1 is reversible and is regulated by the redox environment.To test the redox regulation of DJ-1, we assayed chaperone activity in the CS aggregation assay in the presence or absence of the reducing agent DTT. DJ-1 chaperone activity in the CS aggregation assay was completely abrogated by preincubation of DJ-1 with 0.5 mM DTT in aggregation buffer for 10 min at 4 °C D. DTT dited DJ-1 D. This wMolecular chaperones typically display marked stability to thermal stress . ConsistWe extended the analysis of DJ-1 chaperone function to a candidate DJ-1 substrate, αSyn . The aggαSyn protofibrils have been shown to be an intermediate in the formation of mature amyloid fibrils. Because DJ-1 chaperone activity is effective at inhibiting the accumulation of αSyn protofibrils, we sought to investigate the role of this activity in the generation of Congo red–positive mature fibrils. Congruently, WT DJ-1 inhibited formation of Congo red-positive αSyn fibrils, while L166P DJ-1 and GST did not C. Thus, 2 treatment [2 (2 mM) for 18 h. Treatment with FeCl2 induced accumulation of αSyn in the Triton X-100-insoluble fraction, which has been shown to correlate with αSyn protofibrils (We sought to investigate the chaperone activity of DJ-1 toward αSyn in vivo. αSyn has been shown to form aggregates that consist of both protofibrils and mature amyloid fibrils in the context of oxidative stress (such as FeClreatment ) in neurofibrils . Overexpofibrils A and 3B.ofibrils A or halfofibrils . Thus, DDJ-1 “knockout” embryonic stem (ES) cells, which display increased sensitivity to oxidative stress. DJ-1 homozygous knockout or control heterozygous ES cells (heterozygous cells were used as controls because they were the source of the knockout subclones) were differentiated in vitro using the embryoid body protocol (2 treatment further promoted the accumulation of insoluble αSyn in DJ-1-deficient cells but not in control heterozygous cells (2 treatment did not lead to the accumulation of insoluble Flag-αSyn (see To investigate whether DJ-1 is necessary to inhibit αSyn aggregation in vivo, we utilized protocol  and tranprotocol . As predus cells C. IntereαSyn see , consistαSyn see .2-treated CAD cells transfected with DJ-1 and Flag-αSyn (or control vector) as above. Triton X-100-soluble cell lysates were immunoprecipitated with a mouse monoclonal antibody for the Flag epitope, and Western blots were probed with a rabbit polyclonal antibody for DJ-1. DJ-1 failed to interact with Flag-αSyn in the absence of pretreatment with FeCl2, but an association was evident in FeCl2-treated cell lysates . Overexpression of a mutant form of human NFL, Q333P, by transient transfection of CAD murine neuroblastoma cells, leads to the accumulation of intracytoplasmic inclusions . Co-overThe DJ-1 crystal structure suggests the presence of two highly reactive cysteines, cysteine 106  and cyst2O2 toxicity, consistent with the in vitro chaperone activity assay . Interestingly, it has been suggested that such protofibrils, rather than the large fibrillar aggregates, may underlie αSyn toxicity in vivo . DJ-1 in2O2) but not reducing conditions in the described chaperone assays , 150 mM sodium chloride, 0.2% Triton X-100, and protease inhibitor cocktail (Sigma, St. Louis, Missouri, United States). Cells were incubated on ice for 20 min and Triton X-100-soluble and -insoluble fractions were separated via centrifugation at 13,000 rpm for 15 min.For in vivo αSyn aggregation assays, CAD cells were transfected with Flag-αSyn (pcDNA3) or DJ-1 (pCMS), and medium was replaced with medium without serum. Cells were cultured without serum to induce differentiation for 48 h post-transfection, at which time the medium was exchanged for medium alone or containing 2 mM FeClQuantification of CAD cell aggregates was performed using a Zeiss LSM Pascal confocal microscope (Zeiss, Oberkochen, Germany) with a 20× long working distance lens. Images were imported to NIH Image J for analysis. Images from tenrandomly selected fields in each of three wells were quantified for each condition. Cells containing at least one intracytoplasmic aggregate, independent of size or number per cell, were scored as positive for aggregates. This number was divided by the number of transfected cells per field, determined by GFP fluorescence.2  for 18 h.Mouse ES cells were propagated and differentiated as described . ES cellAn anti-DJ-1 rabbit polyclonal antibody was generated against the synthetic polypeptide QNLSESPMVKEILKEQESR, which corresponds to amino acids 64–82 of the mouse protein. Antiserum was produced using the Polyquick polyclonal antibody production service of Zymed Laboratories . The antiserum was affinity purified on a peptide-coupled Sulfolink column (Pierce Biotechnology, Rockford, Illinois, United States) according to the manufacturer's instructions. Antibody was used at a dilution of 1:200 for immunohistochemistry and Western blotting as described . ImmunohDJ-1 cDNA was PCR amplified from human liver cDNA  and cloned into the expression vectors pET-28a (Novagen, Madison, Wisconsin, United States) or pcDNA3.1 . Flag-DJ-1 and all described mutants were generated by PCR-mediated mutagenesis using standard techniques.E. coli BL21 cells induced with 1 mM IPTG for 4 h at 37 °C. Bacterial pellets were resuspended in 50 mM sodium phosphate (pH 6.8) and 300 mM sodium chloride, and lysed by sonication. Lysates were cleared by centrifugation at 20,000 × g for 20 min, and the supernatant was incubated with NTA-Ni-conjugated agarose resin for 1 h at 4 °C. The resin was subsequently washed five times with 20 resin volumes of lysis buffer containing 20 mM imidazole, and protein was eluted in five fractions of two resin volumes of lysis buffer containing 250 mM imidazole. Recombinant protein elutions were confirmed to be of > 99% purity by SDS-PAGE and colloidal Coomassie staining.His-tagged recombinant human WT or L166P DJ-1 was produced in CS aggregation was performed in 40 mM HEPES (pH 7.8), 20 mM potassium hydroxinde, 50 mM potassium chloride, and 10 mM ammonium sulfate, and monitored in a thermostat-controlled fluorescence spectrophotometer with excitation and emission wavelengths at 500 nm and slit widths at 2.5 nm. Insulin aggregation was performed as described . CS, insαSyn protofibril and fibril formation assays were performed essentially as described . Briefly, protofibrils were formed by incubation of 200 μM WT synuclein with 100 μM DJ-1 or control chaperone protein in PBS for 2 h at 55 °C. Samples were mixed with SDS loading buffer and analyzed by SDS-PAGE and Western blotting using αSyn LB509 antibody (Zymed). Quantitation of high molecular weight αSyn was performed using NIH Image J. Integrated pixel intensity of high molecular weight synuclein for each sample was normalized to monomeric synuclein intensity. For fibril formation, αSyn and chaperone proteins (as described above) were incubated with shaking for 1 wk at 37 °C. Fibril formation was assessed by Congo red .Figure S1(A) DJ-1 catalase activity was quantified as compared to catalase I (5 μg/ml). DJ-1 does not display catalase activity even at concentrations as high as 5 mg/ml.(B) Addition of DJ-1 at 5 mg/ml does not alter catalase activity of the catalase I-positive control, indicating that there are no inhibitory elements present in the DJ-1 preparation.(C) Purity of bacterially produced DJ-1 utilized in the in vitro assays was assessed to be > 99% by SDS-PAGE and colloidal Coomassie staining.(D) GST thermal aggregation (0.4 μM, black circles) is suppressed by WT DJ-1 (2 μM, red squares) and by positive control Hsp27 (2 μM, green stars), but not by L166P mutant DJ-1 (2 μM, blue triangles) or by RNase A (2 μM, purple diamonds).2 · dmol−1. The mutant protein displays significantly reduced secondary structure. CD spectra of DJ-1 (40 μM in 10 mM PBS [pH 7.4]) were recorded on an Aviv 62A sCD spectrometer at 4 °C in a 0.02-cm path length cuvette, and α-helix and β-sheet content were estimated as described (2 · dmol−1) at 222 nm as a function of temperature. Thermal melts were performed in 4 °C increments with an equilibration time of 1 min and an integration time of 30 sec, using a 0.1-cm path length cuvette.(E) Far-ultraviolet CD spectra of WT DJ-1 (blue triangles) and the L166P mutant (red squares); mean residue ellipticity (Θ) equals °C · cmescribed . Based o222 is equal to °C · cm2 · dmol−1 at 222 nm.(F) Thermal denaturation curves for WT and mutant L166P DJ-1; mean residue ellipticity (Θ)(G) Redox regulation is unaffected by the C106A mutation. Redox regulation of C106A DJ-1 was assayed via DTT inactivation (0.5 mM) in the CS aggregation suppression assay.(H) Protofibril preparations (as in (1.2 MB PDF).Click here for additional data file.Figure S22 (Fe) or media alone (0) as described in (A) Undifferentiated ES cells were transfected with Flag-αSyn and treated with 2 mM FeCl5 cells in a 24-well format were transiently transfected with eukaryotic expression constructs encoding WT human DJ-1 or empty vector. After 36 h, cells were starved for 1 h with DMEM lacking cysteine and methionine and supplemented with 8% dialyzed FBS. Cells were pulsed for 2 h with 10 μCi[35S]-L-Met/L-Cys  per well, washed twice, and chased at the indicated intervals with complete medium. Flag-αSyn was immunoprecipitated with Flag antibody-conjugated agarose beads (Sigma), subjected to SDS-PAGE, and visualized by autoradiography.(B) Overexpression of WT DJ-1 does not significantly alter the half-life of soluble Flag-αSyn. CAD murine neuroblastoma cells were stably transfected with Flag-tagged human α-synuclein using standard techniques. 2 × 10(C) Flag-αSyn from (B) was quantitated using NIH Image J.(815 KB PDF).Click here for additional data file.Figure S3p < 0.(A) Overexpression of WT DJ-1 or L166P DJ-1 in the context of αSyn aggregation does not alter cell number. Cells from p < 0.(B) Overexpression of WT DJ-1 or L166P mutant DJ-1 in the context of Q333P mutant NFL aggregation does not alter cell number. GFP positive transfected cells from p < 0.(C) Overexpression of WT DJ-1, but not L166P mutant DJ-1, rescues cells from Q333P mutant NFL toxicity. HeLa cells were transfected with Q333P mutant NFL along with WT human DJ-1, L166P mutant DJ-1, or vector control. After 72 h, cells were assayed by MTT reduction assay (which detects reduction of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide by metabolic enzymes) . Data arp < 0.(D) C53A mutant DJ-1 is unable to rescue cells from Q333P mutant NFL toxicity. Undifferentiated ES cells were transfected with Q333P mutant NFL along with WT human DJ-1, C53A mutant DJ-1, or vector control. After 72 h, cells were assayed by MTT reduction assay . Data ar(E) Coexpression of DJ-1 with NFL does not alter NFL expression levels. CAD cells were transfected with Q333P mutant NFL and vector, WT DJ-1, C53A mutant DJ-1, or L166P mutant DJ-1. Cells were differentiated for 72 h and lysed to produce Triton X-100-soluble and -insoluble fractions. Lysates were exposed to Western blotting with an antibody against transfected human NFL. NFL is present only in the insoluble fraction, and expression of WT or mutant DJ-1 does not alter NFL expression levels.(685 KB PDF).Click here for additional data file.Figure S42 (G–L) for 18 h prior to fixation with PFA. Cells were immunostained with rabbit anti-DJ-1 as described, followed by donkey anti-rabbit Cy5 (A, D, G, and J). Nuclei (B, E, H, and K) were visualized by incubation with the nuclear stain ToPro3 prior to imaging.CAD cells were transfected with WT DJ-1 and differentiated by serum withdrawal for 72 h. Cells were treated with medium alone (A–F) or medium with 2 mM FeCl(1.6 MB PDF).Click here for additional data file.Table S1(45 KB DOC).Click here for additional data file.


Tools are presented to identify Gene Ontology terms that are over- or under-represented in a dataset, to cluster genes by function and to find genes with similar annotations. We have developed methods and tools based on the Gene Ontology (GO) resource allowing the identification of statistically over- or under-represented terms in a gene dataset; the clustering of functionally related genes within a set; and the retrieval of genes sharing annotations with a query gene. GO annotations can also be constrained to a slim hierarchy or a given level of the ontology. The source codes are available upon request, and distributed under the GPL license. Since complete genome sequences have become available, the amount of annotated genes has increased dramatically. These advances have allowed the systematic comparison of the gene content of different organisms, leading to the conclusion that organisms share the majority of their genes with only relatively few species-specific genes. On this basis, one can develop strategies to infer gene annotations from model species to less experimentally tractable organisms. However, such functional inferences require the definition of species-independent annotation policies.Drosophila [Arabidopsis [In this regard, the Gene Ontology consortium  has beenosophila , mouse [osophila , nematodosophila  and Arabbidopsis . More rebidopsis .In parallel, the recent development of new high-throughput methods has generated an enormous amount of functional data and has motivated the development of dedicated analysis tools. For instance, one might wonder whether genes detected as being coexpressed in a DNA chip experiment are related in terms of molecular or cellular function. In practical terms, we address here the following generic questions. First, are there statistically over- or under-represented GO terms associated with a given gene set, compared to the distribution of these terms among the annotations of the complete genome? Second, among a particular gene set, are there closely functionally related gene subsets? And third, are there genes having GO similarities with a given probe gene?To formulate such questions properly in a well defined mathematical framework, we have developed a set of methods and tools, collectively called GOToolBox, to process the GO annotations for any model organism for which they are available Figure .All the programs are written in PERL and use the CGI and DBI modules. All the ontology data and the gene-GO terms associations are taken from the GO consortium website. These data are structured in a PostGreSQL relational database, which is updated monthly. Statistics are calculated using the R statistical programming environment. The web implementation of the GOToolBox is accessible at .In this section, we describe the five main functionalities of the GOToolBox suite. Two of them (GO-Proxy and GO-Family) are not encompassed by any other GO-processing tool currently available .The first step in analyzing gene datasets consists in retrieving, for each individual gene of the dataset, all the corresponding GO terms and their parent terms using the Dataset creation program. The genomic frequency of each GO term associated with genes present in the dataset is then calculated. The resulting information is structured and stored in a data file, available for download on the GOToolBox server for one week. This file contains also the counts of terms within a reference gene dataset (genome or user-defined), and can then be used as an input for the GO-Stats and GO-Proxy programs described below.An optional tool, GO-Diet, allows either the reduction of the term dataset to a slim GO hierarchy (either one proposed by the GO consortium or a user-defined one) or the restriction of the considered terms to a chosen depth of the ontology. It is also possible to filter terms based on the way these have been assigned to the gene products (evidence code). This tool is useful to decrease the number of GO terms associated with a gene dataset, thereby facilitating the analysis of the results of programs described below, particularly when the input gene list and/or the number of associated GO terms is large. Note that the GO-Diet program can generate a gene-term association file in the TLF format, allowing the use of GO terms as gene labels with the TreeDyn tree drawing program . The GO-k of annotated genes for a given term among a dataset of size n, knowing that the reference dataset contains m such annotated genes out of N genes, is then calculated. This test follows the hypergeometric distribution described in Equation 1:Frequencies of terms within the dataset are calculated and compared with reference frequencies (for example with genomic frequencies or with the frequencies of these terms in the complete list of genes spotted on an array). This procedure allows the delineation of enrichments or depletions of specific terms in the dataset. The probability of obtaining by chance a number X represents the number of genes within a given gene subset, annotated with a given GO term. Implemented in the GO-Stats tool, this formula permits the automatic ranking of all annotation terms, as well as the evaluation of the significance of their occurrences within the dataset. An illustration of such an approach is given in 'Mining biological data'. A typical GO-Stats output is presented in Figure where the random variable The goal of the GO-Proxy tool is to group together functionally related genes on the basis of their GO terms. The rationale sustaining our method is that the more genes have common GO terms, and the less they have specific associated terms, the more likely they are to be functionally related. For any two genes of the gene set, the program calculates an annotation-based distance between genes, taking into account all GO terms that are common to the pair and terms which are specific to each gene. Indeed, any two genes can have 0, 1 or several shared GO terms (common terms) and a variable number of terms specific for each gene (specific terms). This distance is based on the Czekanowski-Dice formula (Equation 2):x and y denote two genes, Terms(x) and Terms(y) are the lists of their associated GO terms, # stands for 'number of ' and Δ for the symmetrical difference between the two sets. This distance formula emphasizes the importance of the shared GO terms by giving more weight to similarities than to differences. Consequently, for two genes that do not share any GO terms, the distance value is 1, the highest possible value, whereas for two genes sharing exactly the same set of GO terms, the distance value is 0, the lowest possible value. All possible binary pairs of genes from the dataset are considered, resulting in a distance matrix.In this formula, Next this matrix is processed with a clustering algorithm, such as the WPGMA algorithm, and a functional classification tree is drawn, in which the leaves correspond to input genes. On the basis of this tree, classes can be defined, for instance by using partition rules, and the statistical relevance of the terms associated with each class is calculated using the method described for GO-Stats. The Czekanowski-Dice distance and the corresponding clustering have already proved their effectiveness in delineating protein functional classes derived from the analysis of protein-protein interaction graphs .A last tool, GO-Family, aims at finding genes having shared GO terms with a user-entered gene, on the basis of a functional similarity calculation. It searches the genomes either of one or several supported species (five at the moment). Given an input gene name, the program retrieves the associated GO terms and compares them with those of all other genes by calculating a functional similarity percentage. The program then returns the list of similar genes, sorted by score. By similar genes, we mean either genes having more than one common associated term, or genes which have different associated terms but one or more common parent terms.S between the input gene A and another gene G, one can identify terms that are common to the two genes (Tc), and terms that are specific to A (Ta) and G (Tg). Three different similarity measures have been implemented and proposed to the user:When measuring the similarity percentage Si = (Tc/(Ta+Tc)) × 100     (3)Sp = (Tc/(Ta+Tg+Tc)) × 100     (4)Scd = (1 - ((Ta+Tg)/(Ta+Tg+2Tc))) × 100     (5)Si), similarity percentage relative to the pair of genes (Sp) and Czekanowski-Dice proximity percentage (Scd). The results are then ranked by decreasing similarity values. A typical GO-Family output is presented in Figure respectively called similarity percentage relative to the input gene  [GO can also be used to split gene sets into coherent functional subclasses on the basis of shared annotation terms. As an illustration, we have analyzed a gene set encompassing putative targets of the Engrailed transcription factor in (X-ChIP) . These eIn the first step, the set of putative target genes has been fed to the dataset-creation program and slimmed down by cutting the annotations to the fourth level of the Gene Ontology, using GO-Diet. This eliminates the poorly informative terms. In a second step, the resulting dataset has been processed with GO-Proxy, leading to 11 classes as shown in Table In this study, we have described the GOToolBox suite, which performs five main tasks: gene dataset creation, selection and fitting of ontology level (GO-Diet), statistical analysis of terms associated with gene sets (GO-Stats), GO-based gene clustering (GO-Proxy), and gene retrieval based on GO annotation similarity (GO-Family). Recently, several web-based GO-processing tools have been developed to display, query or process GO annotations. In this section, we are interested in comparing GOToolBox to several GO-processing programs. As shown in Table First, it should be highlighted that, to the best of our knowledge, no other program performing all five functions proposed in GOToolBox exists at present. Furthermore, the GO-Proxy and GO-Family tasks are unique to GOToolBox. These two functionalities are potentially very useful to the biologist. Indeed, on the one hand, the GO-Proxy implementation of a gene-to-gene distance calculation based on several GO terms allows the determination of classes consisting of functionally related genes. This feature should prove useful in all cases where the user wishes to identify functional subgroups within a list of genes of interest. On the other hand, the ability to search for genes similar to a user-defined gene on the basis of related GO terms (GO-Family) is also unique among all GO processing tools. When used to find functionally similar genes within a given species, the GO-Family program is often able to find paralogs as well as other genes with related functions, independently of sequence similarities. Similarly, when used to find functionally similar genes in other species, the program can successfully identify genes with related functions, including orthologs. In addition, the GO-Family program could be very valuable in the context of genome annotation: it could be used by database annotators to verify the coherence of the annotations of genes with known related functions, which if correctly annotated, would indeed be expected to be detected by the program.Because of the presence of these two programs in our suite, we are inclined to think that GOToolBox represents a major improvement over other GO-based Web tools.Numerous programs have been developed to provide statistical evaluation of the occurrence of GO terms Table . We compx genes annotated for a given GO category have been implemented in various dedicated programs: hypergeometric distribution, binomial distribution, Fisher exact test and Chi-square test.As shown in Table P-value calculations obtained with both the hypergeometric and the binomial distributions. In particular, the Chi-square test seems to be the less efficient, because it only gives valid results for large gene datasets, and it does not distinguish between over and under-represented terms [The two latter are non-parametric tests and are therefore less powerful than ed terms .x genes annotated for a given GO category when randomly picking k times one gene among N genes, leaving the possibility that one gene can be picked many times, which is not the correct situation in our case. It is important to note that when N is large, the hypergeometric distribution tends to give the same results as the binomial distribution. On average, the hypergeometric distribution seems to be both the most adapted model and the most powerful statistical test.The binomial distribution permits us to calculate the probability of obtaining P-value calculation, we have implemented these methods in the GO-Stats module of GOToolBox, excepted the Chi-square test for reasons explained above. The implementation of these tests in GO-Stats permits us to compare the methods without having to deal with problems due to program-specific input formats, data update, and supported/unsupported organism species, as is often the case when using different programs. In addition, this gives great flexibility to the user, allowing he or she to use different statistical methods. We verified that (as might be expected) different programs using the same statistical methods give the same results. This was essentially true, with slight variations probably due to the use of different versions of GO by some programs (data not shown). Therefore, the comparison between programs mainly relies on the number of possible statistical tests that are available. As shown in Table To compare the results obtained with the different methods for GOToolBox is the only one in which a multiple testing correction is implemented to adjust P-values and provide a correction for the occurrence of false positives. We choose the Bonferroni correction since it appears to be the most stringent in assessing the significance of enrichment/depletionHowever, among these three programs, In addition to the statistical tests used by the different programs, the presence of functional features offering flexibility to the end-user can also be considered as a criterion for program comparison. Features such as the GO terms filtering utilities and output types proposed by different programs are worth comparing  of the ontology hierarchy. As shown in Table P-values, (with the exception of GoMiner [As far as the output types are concerned, several programs propose a tabulated output file with terms ranked according to their  GoMiner  and GOTM GoMiner , therefo GoMiner , GOTM anIn conclusion, the GOToolBox is a multipurpose, flexible and evolvable software suite that compares favorably to all existing GO-based web-analysis programs. Its two unique features, GO-Proxy and GO-Family, enable new kinds of analyses to be carried out, based on the functional annotations of gene datasets These new functionalities are likely to be very useful to many biologists wanting to extract novel and meaningful biological information from gene datasets.


Epithelial ovarian tumours exhibit a range of malignant potential, presenting distinct clinical phenotypes. Improved knowledge of gene expression changes and functional pathways associated with these clinical phenotypes may lead to new treatment targets, markers for early detection and a better understanding of disease progression.Gene expression profiling (Affymetrix, U95Av2) was carried out on 18 ovarian tumours including benign adenomas, borderline adenocarcinomas of low malignant potential and malignant adenocarcinomas. Clustering the expression profiles of samples from patients not treated with chemotherapy prior to surgery effectively classified 92% of samples into their proper histopathological group. Some cancer samples from patients treated with chemotherapy prior to surgery clustered with the benign adenomas. Chemotherapy patients whose tumours exhibited benign-like expression patterns remained disease free for the duration of this study as indicated by continued normal serum CA-125 levels. Statistical analysis identified 163 differentially expressed genes: 61 genes under-expressed in cancer and 102 genes over-expressed in cancer. Profiling the functional categories of co-ordinately expressed genes within this list revealed significant correlation between increased malignant potential and loss of both IGF binding proteins and cell adhesion molecules. Interestingly, in several instances co-ordinately expressed genes sharing biological function also shared chromosomal location.Our findings indicate that gene expression profiling can reliably distinguish between benign and malignant ovarian tumours. Expression profiles of samples from patients pre-treated with chemotherapy may be useful in predicting disease free survival and the likelihood of recurrence. Loss of expression of IGF binding proteins as well as specific cell adhesion molecules may be a significant mechanism of disease progression in ovarian cancer. Expression levels in borderline tumours were intermediate between benign adenomas and malignant adenocarcinomas for a significant portion of the differentially expressed genes, suggesting that borderline tumours are a transitional state between benign and malignant tumours. Finally, genes displaying coordinated changes in gene expression were often genetically linked, suggesting that changes in expression for these genes are the consequence of regional duplications, deletions or epigenetic events. Epithelial ovarian cancer is the fifth leading cause of death for women in the United States . AlthougMicroarray technology has revolutionised the study of gene function by providing "snapshots" of global gene expression patterns from different normal and diseased tissues over multiple stages of development. Nowhere has the impact of this technology been more pronounced than in the field of cancer biology where gene expression profiling has been successfully used to objectively classify tumours and, in some instances, identify novel tumour sub-types . MicroarPrevious microarray studies of ovarian cancers have focused on the characterisation of differences between normal ovarian epithelial cells (and cell lines) and various types and stages of ovarian tumours -10. In tWe report here the results of applying clustering and statistical analyses to the microarray expression profiles of 18 ovarian tumours. Our findings indicate that gene expression profiling distinguished properly classify 92% of tumours in this study as benign or malignant. Samples taken from ovarian cancer patients who had been treated with chemotherapy prior to surgery were found not to cluster as a distinct group but rather with either the benign or malignant (not pre-treated) tumours. Chemotherapy patients whose tumours clustered with the benign group remained disease free for the duration of the study as evidenced by continued normal serum CA-125 levels. Profiling the functional categories of co-ordinately expressed genes revealed significant correlation between increased malignant potential and loss of IGF binding proteins, and cell adhesion molecules. In several instances co-ordinately expressed genes sharing functional categories also correlated with chromosomal location.To determine if gene expression profiling can distinguish between histologically determined tumour types, we analysed the profiles of 13 ovarian tumours Table . One cluSince many of the genes in our data set display no differential expression across the 13 tumours, their contribution to the SOM is negligible and can be considered noise. Removing genes whose expression pattern displayed insignificant variation (low standard deviation) across all samples, we reduced the data set to1000 probe sets. After removing probe sets representing the same gene, the reduced data set contained expression values representing 700 genes. The SOM and UHC of the reduced data set yielded identical clusters to those obtained using the entire data set Figure  and 1b.To determine the genes most highly correlated with each cluster identified by the SOM analysis, we performed a marker analysis Figure  on the rFive of the cancer patients in our study were treated with chemotherapy prior to surgery. We added the microarray profiles of these patient samples to our analysis in order to determine if they would cluster into a new distinct group or into one or more of the existing groups. The SOM Figure  and UHC In an initial effort to test the possible clinical significance of the differential clustering of samples obtained from patients pre-treated with chemotherapy, we examined the post-operative history of these patients. One commonly used indicator of recurrence is the level of Cancer Antigen-125 (CA-125) in the blood . AlthougTo identify genes whose differential expression correlate with malignant potential, we performed a statistical analysis comparing the expression profiles of the three tumour types examined in this study . Malignant adenocarcinoma sample c_79 was excluded from this analysis since both the SOM and UHC classification methods identified this sample as an outlier of the malignant adenocarcinoma group (see above). The F statistic was used to test equality of group means . Genes wHierarchical clustering was performed to visualise gene expression across tumour types for each of these 163 genes. All 12 tumours were correctly assigned as shown by the dendogram above the gene expression colour plot Figure . SeveralTo independently test the validity of the differential expression patterns determined by microarray, we measured the expression patterns of 3 representative genes in 6 tissue samples using quantitative real time RT-PCR . Genes wTwo expression subgroups were evident in the list of 163 differentially expressed genes Figure : genes wGene expression profiling via microarray technology has previously been shown to be an effective tool for the objective classification of established tumour types ,19 and iOne particularly promising result emerging from our study is that expression profiling may be useful in predicting recurrence in patients treated with chemotherapy prior to surgery. We find that the microarray patterns of ovarian adenocarcinomas obtained from patients treated with chemotherapy prior to surgery clustered either with the benign tumours or with the malignant adenocarcinomas. Serum CA-125 levels indicate that patients whose samples clustered with the benign tumours have remained disease free for more than 3 years after surgery while those patients whose samples clustered with the malignant tumours recurred within 2 years of the initial treatment. Clearly, the testing of additional patient samples will be needed before definitive conclusions can be drawn. However, the preliminary results are consistent with the hypothesis that gene expression profiles of samples removed on the day of surgery may predict recurrence and would therefore be an indicator of the long-term effectiveness of chemotherapy administered to patients prior to surgery.Our microarray data are, in general, most consistent with the hypothesis that borderline ovarian tumours represent an intermediate stage between the benign and malignant tumours. Borderline tumours of the ovary display many but not all characteristics of malignancy including nuclear atypia and increased mitotic count, usually in the absence of stromal invasion -24. WhetGenes physically linked to one another shared changes in gene expression between tumour types. For those cases where linked genes displayed a significant reduction in expression in malignant vs. benign tumours Table , at leasWe also observed a co-ordinated increase in gene expression of physically linked genes in the malignant samples in several cases Table . These cIn a few instances genes that were physically linked displayed opposing changes in gene expression between benign and malignant tumours Table . It is pOur finding that a number of the genes displaying a significant difference in expression among malignant and non-malignant tumours indicates that some caution must be taken in the functional interpretation of microarray results. For example, significant changes in the expression of only one gene in a physically linked group may be of functional significance although correlated changes in gene expression may result from a regional effect.Two common features of malignant cancer cells are increased cell proliferation and loss of cell adhesion . ConsistThe IGF system is a complex network of molecules involved in the normal growth and development of many cell types . DisreguOver-expression of certain members of the IGF system increased sensitivity to IGF1 signaling in breast cancer cells  leading Loss of cell adhesion molecules (CAM) is one mechanism proposed to induce the tissue invasion and metastatic capabilities acquired by cells during tumourigenesis. Intra-aCell-cell adhesion is often mediated through the cadherins, a family of transmembrane glycoproteins that require calcium to perform their adhesive functions. Well-documented changes in cadherin subtype expression correlate with the progression of breast and prostate cancer . RecentlSince cadherins are calcium-dependent cell adhesion molecules and increOur findings indicate that gene expression profiling can reliably distinguish between benign and malignant ovarian tumours. Expression profiles of samples from patients pre-treated with chemotherapy may be useful in predicting disease free survival and the likelihood of recurrence. Genes displaying co-ordinated changes in gene expression were often genetically linked suggesting that changes in expression for these genes are the consequence of regional duplications, deletions or epigenetic changes. Loss of expression of IGF binding proteins as well as specific cell adhesion molecules may be a significant mechanism of disease progression in ovarian cancer. A significant portion of the differentially expressed genes exhibited expression levels in borderline samples intermediate between benign adenomas and malignant adenocarcinomas, suggesting the borderline tumours are a transitional state between benign and malignant tumours.A set of 18 primary ovarian tumours was obtained from the Ovarian Cancer Institute. This set of tumours was comprised of 4 benign cystadenofibromas, 4 carcinomas of low malignant potential (borderline carcinomas), 5 adenocarcinomas, and 5 adenocarcinomas from patients who received chemotherapy prior to surgery. This study was approved by the Institutional Review Board of the University of Georgia and of Northside Hospital (Atlanta), from which the samples were obtained Tissue was collected at the time of initial surgery and preserved in RNA Later (Ambion) within one minute of collection. For RNA isolation, each tissue (50 ± 25 mg) was homogenized on ice in 1.5 ml Trizol (Molecular Research Corporation) with a polytron homogenizer for about 30 seconds. RNA was isolated from the crude homogenate according to the manufacturer's protocols (Trizol, Molecular Research Corporation) with the following specifics. Linear polyacrylamide (5 μl) was added prior to homogenization to aid in RNA precipitation. Total RNA was further purified over an RNEasy (Qiagen) column using the manufacturer's cleanup protocol.Biotinylated target cRNA was generated according to the Affymetrix Technical Manual. In brief, 5–10 μg total RNA was converted to double stranded cDNA using Supercript II (Invitrogen). The cDNA was cleaned by phenol/chloroform extraction and ethanol precipitation. In vitro transcription of the cDNA with the High Yield RNA Transcript Labeling Kit (Enzo) yielded 50–100 μg of biotin labeled cRNA target. The cRNA was fragmented in a metal catalyzed acid hydrolysis to a length of 20–200 bp (by electrophoresis) and the fragmented cRNA was hybridized to the Affymetrix array (U95Av2) for 16 hours at 45C. Hybridized arrays were washed, stained and scanned according to the Affymetrix technical manual.Signal values were generated in two ways. Affymetrix signal values were generated from the .CEL file using the Affymetrix software MAS 5.0. The overall intensity of each array was scaled to an average intensity of 500. These normalized signal values were exported to Excel (Microsoft) for further analysis labeled the Affy-data set. Robust multi-array analysis (RMA) signal values were generated from the .CEL file using the espresso wrapper in the Affy library of the Bioconductor package in the R-statistical environment. The parameters of PM correction, background correction, normalization, and summary method were set to PM only, RMA, quantile, and median polish, respectively. The normalized signal values were exported to Excel for further analysis and will be referred to in this paper as the RMA-data set. For each data set, Affy-data set and RMA-data set, Pearson correlation coefficients were calculated (Microsoft, Excel) for a_97 vs. all other arrays. Higher correlation and lower standard deviation from the mean within groups was seen with the RMA data set, suggesting higher quality data.) was used to cluster the dataset on both samples and genes. Except where described below, default parameters were used. The SOM feature of GeneCluster was employed, and various values were explored for the "Cluster Range" and "Iterations" parameters. The 'Cluster Range' parameter sets the geometry of the clusterings that will be performed on the data. For instance, if 2–3 is entered, two cluster sets are produced. One has two clusters and the other three clusters. When entering a number, any set of factors of that number will create a clustering. If 9 is entered, a linear set of 9 clusters and a 3 × 3 matrix of clusters are produced. Marker analysis was also performed in GeneCluster, again using the default parameters.Raw data output from the Affymetrics MicroArray reader is transformed into expression level values using the RMA method ,49of the-ΔΔCt method[Total RNA (2 μg) from ovarian tissue was converted to cDNA using Superscript III (Invitrogen) primed with random hexamers under conditions described by the supplier. cDNA from this reaction was used directly in the Quantitative RT-PCR analysis. TaqMan probes and gene specific primers for three genes (RPL-29, UBE2C, OVGP1, and CDH2) were obtained from Applied Biosystems' Assay on Demand. The mRNA levels of the three genes were measured in 6 ovarian tumours and one normal ovary on the ABI Prism 7700 Sequence Detection System. PCR was performed using the TaqMan Universal PCR MasterMix (Applied Biosystems), according to the manufacturer's protocols with standard PCR cycling steps. Using RPL29 as a housekeeping gene and the normal human ovary RNA as a reference sample, the expression levels of UBE2C, OVGP1 and CDH2 were calculated according to the 2Ct method. The Ct th statistically significant gene (see results section), we continued the analysis on the top 300 statistically significant genes. Genes whose groups means were identified as significantly different (p ≤ 0.001, 299 genes) in the unadjusted ANOVA were further analysed using Fisher's Least Significant Difference multiple comparison method. The differences between group means for all pairwise combinations of groups were calculated and compared to the least significant difference. Genes were declared differentially expressed if the pairwise difference between group means was greater than the least significant difference. Probe sets duplicated between pairwise comparisons and probes sets with a fold change value below 2.0 were removed, leaving 163 unique genes differentially expressed.The RMA normalised data set was analysed for probe sets likely to be absent in all samples. Probe sets whose maximum RMA normalised value across all samples was less than 5.2 were removed from further analysis. Analysing the remaining 10,520 probe sets, we applied an analysis of variance (ANOVA) to test the hypothesis that the mean expression values for all groups (adenoma, borderline and cancer) are equal. For each gene, the within group and between group variation was calculated and used to generate the F statistic and subsequent p-values . Adjuste[[Genes found to be differentially expressed in the statistical analysis were divided into two lists: genes overexpressed in cancer, and genes underexpressed in cancer. Each list was analyzed for over-represented functional categories based on molecular function, biological process, cellular component and chromosomal location using two different freeware programs: EASE and OntoE[. Given aSW performed microarray, basic statistical analysis and hierarchical clustering. SP performed SOM clustering and marker analysis. SD contributed to the statistical analysis. BB collected tissue and serum. EK directed SW and SP. JM contributed to the linkage and CA-125 analyses and directed SW.The file complete_list.xls contains the gene name and chromosomal location for the 163 genes determined to be differentially expressed in this study.Click here for file


Fat mass, which is a major component of body weight, is directly related to bone mineral density and reduced fracture risk. It is not known whether abdominal fat is associated with hip fracture. The present study was designed to examine the association between abdominal fat and hip fracture in women and men aged 60+ years.This was a nested case-control study with one fracture case being matched with two controls of the same age. In women 63 cases were matched with 126 controls, and in men 26 cases were matched with 52 controls. Hip fracture was confirmed by X-ray and personal interview. Other measurements included weight, height, body mass index (BMI), abdominal fat, and femoral neck bone density (FNBMD). Conditional logistic regression model was used to analyse data.The odds ratio of hip fracture risk associated with each 10% lower abdominal fat was 1.5 (95% CI, 1.1 to 2.1) in women and 1.2 (95% CI, 0.7 to 2.0) in men. However after adjusting for FNBMD or body weight, the abdominal fat-fracture association was no longer statistically significant. Similarly, body weight and BMI was each significantly associated with hip fracture risk (in women), but after taking with account the effect of FNBMD, the association become statistically non-significant.Lower abdominal fat was associated with an increased risk of hip fracture in elderly women, but the association was not independent of FNBMD or weight. The contribution of abdominal fat to hip fracture risk is likely to be modest. Hip fracture is a public health concern, because it is associated with increased mortality, morbidity, reduced quality of life, and incurs significant economic and social costs . Bone miCentral abdominal fat, which can be derived from dual-energy X-ray absorptiometry (DXA) scans, is highly correlated with, and has been suggested to be a surrogate measure of body fat . TherefoThe present study was designed as a nested case-control study within the larger Dubbo Osteoporosis Epidemiology Study (DOES), which has been on going since 1989 ,18. Brie+ have participated in the study. Within this population, 89 (63 women and 26 men) hip fracture cases, which had had abdominal fat measured were identified from radiologists' reports from the two centres providing X-ray services as previously described [By mid 2003, 2560 subjects aged 60escribed . Fractur+ years and men 81+ years. In total, data from 267 subjects were included in the analysis.For every fracture case, two non-fracture controls of the same age were randomly selected from the database. Age matching tolerance of ± 5 years was applied for women 85Subjects were interviewed by a nurse co-ordinator who administered a structured questionnaire to collect data including age, life-style factors such as past and present tobacco intake (assessed as pack-years) and alcohol consumption, physical activity. Anthropometric variables (height, weight) were measured and a dietary assessment was performed based on a frequency questionnaire for calcium intake as described elsewhere .2) was measured by DXA using a LUNAR DPX-L densitometer (GE-LUNAR, Madison). The radiation dose with this method is <0.1 μGy. The coefficient of reliability of BMD in our institution in normal subjects is 0.96 and 0.98 at the proximal femur and lumbar spine, respectively [Femoral neck bone mineral density  were tested by paired t-test or Wilcoxon signed ranks test with significance level of 5%, depending on the distribution of data. The association between abdominal fat and hip fracture risk was assessed by the conditional logistic regression via the PROC PHREG  of the SAbdominal fat in both women and men was normally distributed with no significant skewness. In the entire sample, there was no significant difference in percent of abdominal fat between women and men (23.9 ± 9.5 % vs. 24.0 ± 10.1 %, P = 0.993). Abdominal fat significantly decreased with age (r = -0.21, P = 0.003) with 1.4% (SE = 0.47) per 5 year in women. In men the rate of decrease was 0.5% (SE = 0.87) with each 5 year of age; however the decrease was not statistically significant (r = -0.07, P = 0.561). The correlation between abdominal fat and weight (r = 0.7, p < 0.001 for both genders) was higher than that between abdominal fat and FNBMD (r = 0.4, p < 0.001 in women and r = 0.2, p = 0.041 in men), .After matching for age and gender, compared to the controls, women with hip fracture had significantly lower weight (-0.7SD, p < 0.001), BMI (-0.6SD, P = 0.001), abdominal fat (-0.5SD, P = 0.014) and FNBMD (-0.9SD, P < 0.001). In contrast, in men there was no significant difference between those with hip fracture and those without fracture with respect to weight, BMI and abdominal fat; however FNBMD in men with a hip fracture was 1.1SD lower than those without a fracture (Table The risk of hip fracture was estimated to increase by 1.5-fold (95%CI: 1.1 to 2.1) in women and 1.2-fold (95% CI: 0.7 to 2.0) in men for each 10% lower abdominal fat. However after adjusting for BMD or body weight, the abdominal fat-fracture association was no longer statistically significant. (Table Similarly, body weight and BMI was each significantly associated with hip fracture risk (in women), but after taking with account the effect of FNBMD, the association become statistically non-significant. In both women and men, the association between BMD and fracture risk was consistently significant either in unadjusted or in adjusted analysis Table .It has been known for some time that body weight and whole body fat mass are significant predictors of hip fracture risk in women ,15, howeInterestingly, in men abdominal fat or body weight was not significantly associated with hip fracture risk either before or after adjusting for BMD. Moreover, the magnitude of difference between fracture versus non-fracture cases in body weight or abdominal fat in men was generally more modest compared to that in women. For example, men with hip fracture had 0.2SD lower abdominal fat and 0.4SD lower in weight than non-fracture men. These differences were not significant. In contrast, in women the corresponding differences were significant and were 0.5SD lower for abdominal fat and 0.7SD for weight. This may suggest that the BMD-hip fracture association in men is not mediated via fat mass, despite a similar correlation between fat mass and BMD. However the lack of significance of the association between abdominal fat and hip fracture in men in the present study may be due to the small sample size.In recent years, there has been considerable interest in the relationships between osteoporosis, diabetes and cardiovascular disease -30. A coFrom a public health point of view, the present study's finding suggests that abdominal fat does not add to the discriminatory value of hip fracture risk that is already provided by BMD. Indeed, in this study, none of the body size measurements  was a significant predictor of hip fracture risk after adjusting for BMD. This suggests that these measures have limited value in the prediction of hip fracture in a population or an individual.A number of issues should be kept in mind before extrapolating the present finding. First, the participants in this study were Caucasian aged 60 years and above, so it may not be generalizable to younger populations and to different races. Second, neither total body fat, nor waist and hip circumferences (WHC) were measured and these may have had stronger predictive value. However, these measurements may underestimate abdominal adiposity in those with both large waist and hip circumferences, while DXA determination of regional body fat distribution may indeed be more valid than WHC .These data have demostrated that in the elderly, abdominal fat was significantly associated with hip fracture risk in women but the association was not independent of BMD, whereas in men abdominal fat was not a significant predictor of hip fracture risk. Measurement of DXA abdominal fat does not contribute to hip fracture prediction over and above that provided by BMD.All abbreviations are defined in the text.The author(s) declare that they have no competing interestNDN obtained and analysed the data, and drafted the manuscript. CP had an active role in data analysis and interpretation of results. JRC had an active role in the conduct of the Dubbo Osteoporosis Epidemiology Study and helped with the interpretation of results. JAE established the Dubbo Osteoporosis Epidemiology Study. TVN had an active role in the conception if this project, involved in study design, analysis data and interpretation of results. All authors contributed to the last version of the manuscript.The pre-publication history for this paper can be accessed here:


In addition to known protein-coding genes, large amounts of apparently non-coding sequence are conserved between the human and mouse genomes. It seems reasonable to assume that these conserved regions are more likely to contain functional elements than less-conserved portions of the genome.Here we used a motif-oriented machine learning method based on the Relevance Vector Machine algorithm to extract the strongest signal from a set of non-coding conserved sequences.We successfully fitted models to reflect the non-coding sequences, and showed that the results were quite consistent for repeated training runs. Using the learned models to scan genomic sequence, we found that they often made predictions close to the start of annotated genes. We compared this method with other published promoter-prediction systems, and showed that the set of promoters which are detected by this method is substantially similar to that detected by existing methods.The results presented here indicate that the promoter signal is the strongest single motif-based signal in the non-coding functional fraction of the genome. They also lend support to the belief that there exists a substantial subset of promoter regions which share several common features including, but not restricted to, a relative abundance of CpG dinucleotides. This subset is detectable by a variety of distinct computational methods. Since the publication of draft sequences for the human  and mousHere, we apply the Eponine Windowed Sequence (EWS) sequence analysis method method which uses a Relevance Vector Machine (RVM)  to extraWe considered a set of alignments made by the blastz program  between In total, the tight blastz set contained 787173 blocks of sequence with high-scoring alignments between the two genomes. We considered only those blocks assigned to human chromosome 6, a 170 Mb chromosome which has recently undergone manual annotation of gene structures and other features [ab initio gene predictions from the Genscan program [Since we were interested in non-coding features of the genome, we ignored all regions where an alignment overlaps an annotated gene structure. This removed 20.8% of aligned bases. It is possible that some genes, and especially pseudogenes, have been missed by the annotation process, so we also removed portions covered by  program . This el program  and trf  program  (5.9%) wA negative training set of equal size was prepared by picking 200-base windows at random from the non-coding, non-repetitive portions of chromosome 6, using the same criteria to define repeats and coding sequence. While it is probable that this set also included some functional sequences, we would expect them to be represented at a substantially lower level than in the conserved set.These two sets of sequence were presented to the Eponine Windowed Sequence machine learning system, as described in the methods section. Randomly chosen 5-base words were used as seed motifs, and three independent training runs were performed, each for 2000 cycles. The set of motifs used in model 1 is shown in table e.g. figure While the exact set of motifs used in the model varied somewhat from run to run, testing pairs of models on non-overlapping windows from a 1 Mb region of human chromosome 22 and plotting the scores showed that the model outputs were highly correlated  but can never grow longer than the seed word size. When evaluated on the pseudochromosome, the resulting models always showed a preference for regions around gene starts, regardless of word length, as shown in figure This suggests that a large fraction  of the information learned by these models can be encoded in dinucleotide frequencies. It is well known that many transcription start sites are close to regions of relatively high CpG dinucleotide composition (CpG islands) . To inveWe have shown here that, when presented with a set of non-coding sequences which are strongly conserved between human and mouse, a simple motif-oriented machine learning system consistently builds models which are able to detect a substantial fraction of human promoter regions with good accuracy. This strongly suggests that this promoter signal represents the most widely used motif-based signal in functional non-coding sequence. While the model learned here can clearly be applied for the purpose of genome-wide promoter annotation, in practise existing methods offer better coverage and (in the case of the EponineTSS predictor) predictions for the precise location of the transcription start site.It is interesting that the promoter model learned by this technique detected substantially the same set of promoters as found by the EponineTSS and PromoterInspector methods. It has previously been remarked that these two methods detect similar sets , but thiOne distinct group of promoters, which previous results show may correspond to this easily detected family, is the set of promoters associated with CpG islands . HoweverHuman genome sequence release NCBI33 and mouse genome release NCBIM30 were extracted from Ensembl databases , which aHuman-mouse genome alignments were generated by the blastz alignment program. These were subsequently re-scored and filtered to give a 'tight' set of high-confidence alignments, as described in . We downA 16.3 Mb pseudochromosome sequence was produced based on version 2.3 of the curated annotation for human chromosome 22. This includes all the experimentally-validated gene structures and their upstream regions, while omitting regions containing genes that are predicted but not fully verified. In the case of a pair of divergent genes where one has been verified and the second has not, their shared upstream region was cut at the midpoint. More information about pseudochromosome construction is given in [The Eponine Windowed Sequence (EWS) model is designed by analogy to the Eponine Anchored Sequence model first described in , but ratThe EWS model uses the Relevance Vector Machine  algorithThe "sensors" of the EWS model are DNA position-weight matrices , which mW(s) is the probability that sequence s was emitted by weight matrix W, |S| is the sequence length, |W| is the weight matrix length, and  denotes a subsequence from i to j.where An initial set of basis functions is proposed by taking all possible DNA motifs of a specified length  and generating weight matrices which preferentially recognise these motifs. As the relevance vector machine trainer removes non-informative basis functions from the working set, they are replaced by applying one of the following sampling strategies to a basis function picked randomly from the working set:• Generate a new weight matrix in which each column is a sample from a Dirichlet distribution with its mode equal to the weights in the corresponding column of the parent weight matrix.• Generate a new weight matrix one column shorter than the parent by removing either the first of the last column.By using these sampling rules, the trainer is able to explore motif space. The process of generating candidate motifs using these rules then selecting the most informative using the RVM can be seen as a form of genetic algorithm.TD and TH conceived and designed this study, and analysed results. TD implemented the Eponine machine learning system and drafted the manuscript. All authors read and approved the final manuscript.