Complete Spatial Randomness (CSR) is the null hypothesis employed by many statistical tests for spatial pattern, such as local cluster or boundary analysis. CSR is however not a relevant null hypothesis for highly complex and organized systems such as those encountered in the environmental and health sciences in which underlying spatial pattern is present. This paper presents a geostatistical approach to filter the noise caused by spatially varying population size and to generate spatially correlated neutral models that account for regional background obtained by geostatistical smoothing of observed mortality rates. These neutral models were used in conjunction with the local Moran statistics to identify spatial clusters and outliers in the geographical distribution of male and female lung cancer in Nassau, Queens, and Suffolk counties, New York, USA.We developed a typology of neutral models that progressively relaxes the assumptions of null hypotheses, allowing for the presence of spatial autocorrelation, non-uniform risk, and incorporation of spatially heterogeneous population sizes. Incorporation of spatial autocorrelation led to fewer significant ZIP codes than found in previous studies, confirming earlier claims that CSR can lead to over-identification of the number of significant spatial clusters or outliers. Accounting for population size through geostatistical filtering increased the size of clusters while removing most of the spatial outliers. Integration of regional background into the neutral models yielded substantially different spatial clusters and outliers, leading to the identification of ZIP codes where SMR values significantly depart from their regional background.above and beyond background variation. The implementation of this approach in spatial statistical software will facilitate the detection of spatial disparities in mortality rates, establishing the rationale for targeted cancer control interventions, including consideration of health services needs, and resource allocation for screening and diagnostic testing. It will allow researchers to systematically evaluate how sensitive their results are to assumptions implicit under alternative null hypotheses.The approach presented in this paper enables researchers to assess geographic relationships using appropriate null hypotheses that account for the background variation extant in real-world systems. In particular, this new methodology allows one to identify geographic pattern  The value of this statistic is then compared to the distribution of that statistic's value under a null spatial model. This provides a probabilistic assessment of how unlikely an observed spatial pattern is under the null hypothesis [Cancer mortality maps are important tools in health research, allowing the identification of spatial patterns, clusters and disease 'hot spots' that often stimulate research to elucidate causative relationships ,2. In mopothesis . Waller pothesis  formaliztest statistic quantifies a relevant aspect of spatial pattern 1. The alternative hypothesis describes the spatial pattern that the test is designed to detect. This may be a specific alternative, such as clustering near a focus, or it may be the omnibus "not the null hypothesis".2. The null hypothesis describes the spatial pattern expected when the alternative hypothesis is false .3. The null spatial model is a mechanism for generating the reference distribution. This may be based on distribution theory, or it may use randomization (e.g. Monte Carlo) techniques.4. The reference distribution is the distribution of the test statistic when the null hypothesis is true.5. The e.g. Moran's I and Geary's c); its local counterpart (e.g. LISA); geographic boundary statistics (e.g. boundary count and mean length), and a host of techniques for identifying hot spots, cold spots and foci. While CSR is useful in some situations, it often is not a relevant null hypothesis for highly complex and organized systems such as those encountered in the environmental and health sciences [always present. Hence in many situations rejecting CSR has little scientific value because CSR does not describe any plausible state of the system. As emphasized by Ord and Getis [CSR is the null hypothesis employed by most, if not all, statistical tests for spatial pattern, and is the workhorse of almost all spatial statistical software. Examples of statistics used in these tests include spatial autocorrelation . The problem then is to identify spatial patterns above and beyond that incorporated into the neutral model, enabling, for example, the detection of cancer clusters beyond background or regional variation in the risk of developing cancer. Neutral models can be generated using simulation techniques developed in the field of geostatistics [The term "atistics  which pratistics .The objective of this paper is to present geostatistical approaches to generating neutral models that account for the spatial dependence of cancer rates, their regional background and spatially heterogeneous population sizes. These models are then used for the detection of local clusters and anomalies in cancer rates. The new methodology is applied to the analysis of the geographical distribution of lung cancer in three counties of Long Island, New York, which have been investigated under the CSR hypothesis in a previous issue of this journal ,11The use of neutral models in cluster analysis will be illustrated using the lung cancer data analysed in ,11. This. Data have been released on the following four cancers: breast , colorectal , lung , and prostate. These data represent newly diagnosed cancer cases in the period 1993–7 assigned to the patient's residence at diagnosis, and they are calculated as the number of cancers for each 100,000 people in the population.The New York State Department of Health (NYSDOH) published the cancer incidence data online as part of their Cancer Surveillance Improvement Initiative, To protect patient privacy, the NYSDOH data provided case counts referenced to ZIP codes rather than individual residences. While ZIP codes are somewhat arbitrary spatial units of analysis with respect to potential health and environmental factors, they provide a convenient way to group the population and preserve confidentiality. The methods presented here do not depend on the spatial unit of aggregation and the reader may use census geography if that is their preference. As in the earlier analysis ,11, the Because cancer incidence is related to age, NYSDOH calculated the expected cancer incidence for each ZIP code using the ZIP code's age structure and the average incidence by age class for New York State (direct adjustment). We thus are using an external standard (the state average) rather than an internal standard (the average for Long Island), to calculate the expected incidence. A standardized morbidity ratio (SMR) has been calculated by dividing the observed value by the age-adjusted expected incidence. An SMR value of 1.0 indicates that the observed incidence is the same as expected, lower than 1.0 indicates that fewer than expected cases of cancer occurred, and greater than 1.0 indicates that more than expected occurred.. The local Moran test evaluates local clustering or spatial autocorrelation by computing the contribution of each location to the Moran's I statistics for the whole study area. Its null hypothesis is that there is no association between SMR values in neighboring ZIP codes. The working  hypothesis is that spatial clustering exists. For each ZIP code, referenced geographically by its centroid with the vector of spatial coordinates u = (x, y), the LISA  statistic is computed as:Jacquez and Greiling  identifiu) is the SMR for the ZIP code being tested, which is referred to as the "kernel" hereafter. z(uj) are the values for the J(u) neighboring ZIP codes that are here defined as units sharing a common border or vertex with the kernel u (1-st order queen adjacencies). All values are standardized using the mean m and standard deviation s of the SMR data . Since the standardized values have zero mean, a negative value for the LISA statistics indicates a spatial outlier where the kernel value is much lower or much higher than the surrounding values . Cluster of low or high values will lead to positive values of the LISA statistics (e.g. both kernel and neighborhood averages are jointly above zero or below zero).where z, then computing the corresponding simulated neighborhood averages. This operation is repeated many times (e.g. L = 999 draws) and these simulated values are multiplied by the kernel value to produce a set of L simulated values of the LISA statistics at location u:In addition to the sign of the LISA statistics, its magnitude informs on the extent to which kernel and neighborhood values differ. To test whether this difference is significant or not, a Monte Carlo simulation is conducted, which traditionally consists of (l)(uj) = F-1[p(l)(uj)], F[.] is the sample cumulative distribution function (cdf), and p(l)(uj) is a random number uniformly distributed within 0 and 1. This set represents a numerical approximation of the probability distribution of the LISA statistics at u, under the assumption of spatial independence. The observed LISA statistics, LISA(u), can then be compared to the probability distribution, allowing the computation of the probability of not rejecting the null hypothesis . Following Jacquez and Greiling [α = 0.01101 to account for the fact that the multiple tests (i.e. 214 in this study) are not independent since near ZIP codes share similar neighbors. This significance level was obtained using the Bonferroni adjustment which amounts at dividing the significance level α = 0.05 by the average number of neighbors in each test. Thus, every ZIP code where the p-value is lower than 0.01101 will be classified as a significant spatial outlier  or cluster .with zGreiling , we usedThe use of CSR as the null hypothesis means that the distribution of cancer rates is assumed to be spatially random (no autocorrelation) with uniform risk over the study area. In most cases, however, mortality rates are spatially correlated while the risk of developing cancer varies regionally as a result of changes in environmental exposure or other demographic, social, and economic factors. Another weakness of the above test is that it does not consider whether ratio data are based on many or a few cases, thereby ignoring the instability of rates computed from small population sizes. The basic idea of the proposed approach is to generate neutral models that are more realistic in the sense that they incorporate presence of spatial autocorrelation, non-uniform risk, and account for spatially heterogeneous population sizes.Table p-quantiles zp and yp of the z-cdf F(z) (cumulative histogram) and the standard Gaussian cdf G(y). In practice, the normal score transform proceeds in three steps:The simulation techniques used in this paper assume a multiGaussian distribution for the variable under study, which requires a prior normal score transform of SMR data to ensure that at least their univariate distribution (histogram) is normal. Normal score transform is a graphical transform that allows one to normalize any distribution, regardless of its shape. It can be seen as a correspondence table between equal uα) (i.e. SMR data) are first ranked in ascending order. Since the normal score transform must be monotonic, ties in z-values must be broken, which has been done randomly as implemented in GSLIB software [1. The N original data z(software .uα) with rank k is then computed as  = k/N - 0.5/N.2. The sample cumulative frequency of the datum z = φ(z(uα)) = G-1[F(z(uα))] = G-1[]Model II aims to reproduce the pattern of spatial correlation displayed by the data that is here quantified using the normal score semivariogram ,14 whichh| corresponds to the Euclidian distance between two ZIP codes. Note the following discussion can be readily generalized to other distance measures that could be more appropriate to capture contiguity of entities of complex shape: our methodology is general and does not depend on a particular formulation of the distance measures. Following previous simulation studies [, where n(uα) is the size of the population at risk in the ZIP code with centroid uα. Following an earlier analysis of the data [Here | studies  and in othe data , the popSpatial neutral models are generated using Sequential Gaussian Simulation (SGS) which proceeds as follows (see  p. 380 f1. Fit a permissible function  to the euα only once.2. Define a random path (i.e. using a random number generator) visiting each ZIP code location uα determine the mean and variance of the Gaussian probability distribution of y-values as:3. At each location (l)(ui) are normal scores simulated at locations previously visited along the random path and located within a search radius from uα, mY is the stationary mean of the variable Y , and C(ui-uα) is the covariance function of the normal score variable Y for the separation vector hα i= ui-uα. λi are kriging weights obtained by solving the following system of linear equations (simple kriging, SK):where yuα is , where p(l) is a random number between 0 and 1.4. Draw a simulated value from the conditional cumulative distribution function (ccdf) of probability and add it to the data set. In other words, the simulated value at 5. Proceed to the next location along the random path, and repeat the two previous steps.N locations (i.e. N = 214 here) are simulated.6. Loop until all (l)(uα); α = 1,..., N} so that the target histogram  is reproduced: z(l)(uα) = F-1[p(l)(uα)] with p(l)(uα) = G[y(l)(uα)]7. Transform the simulated normal scores {yThe procedure is repeated using a different random path and set of random numbers to generate another realization. Note that these realizations account for only the histogram and semivariogram model of the SMR data , but they are non-conditional to the SMR data themselves .(l)(uα); α = 1,..., N} have been generated, Equation (2) is applied to each member of this set to compute the simulated values of the LISA statistics at each location u. The simulated LISA values form the empirical distribution of the LISA statistics, allowing the calculation of the p-value for the test of hypothesis.Once the L sets of N simulated SMR values, {zabove and beyond this non-uniform risk. For example, one might want to detect clusters of melanoma beyond those that are explained by the north-south gradient in solar radiation. Non-uniform risk can easily be accounted for in the simulation procedure by replacing the stationary mean mY in Equation 4 by locally varying means mY(uα), that is using the following estimate for the mean and variance of the Gaussian ccdf:The simulation of neutral model II is conducted using a stationary mean for SMR values, which is unrealistic for situations where environmental exposure or other factors make the risk non-uniform. In this instance the researcher wishes to detect spatial pattern R(ui-uα) is the covariance function of the residual normal score variable [Y(uα) - mY(uα)] for the separation vector hα i= ui-uα, and the kriging weights are obtained by solving the following system of linear equations :where CY(uα), which define the reference background risk the user wants to consider for the null hypothesis. In this paper a smooth model of background risk values was obtained by using the following kriging estimator of the local means of observed SMR data:The first step in the generation of model III is the computation of the local means mThe kriging weights are calculated in two-steps. First, the following "kriging of the local mean" system  is solveThen, to incorporate data reliability due to spatially varying population size directly into the geostatistical filter the kriging weights are rescaled, following , as:This rescaling is applied separately to the negative and positive kriging weights, keeping constant the overall contribution of these two sets of weights; that is the sum of positive (negative) kriging weights is the same before and after rescaling, which ensures that the unbiasedness constraint in system (11) is still satisfied. Note that although the population size is incorporated in the estimation of the local means, it is not accounted for directly into the test of hypothesis, which will be achieved using Models IV through VI introduced below.Once the local means of the normal score transformed SMR data have been estimated, they are subtracted from the SMR values and the semivariogram of residuals is estimated and modelled. Then, the simulation is performed using SGS and SKlm. Last, the L realizations are used to derive the empirical probability distribution of the LISA statistics and the p-value of the test is computed.The neutral models introduced so far ignore the fact that cancer rates estimated over small areas, such as United States ZIP code areas or census tracts, tend to be less reliable ,17, hencThe kriging weights are calculated in two-steps. First, the following system is solved:g0 = b0 × (1-δ(ui-uα)) where b0 is the nugget variance in the weighted semivariogram model of SMR data, and δ(ui-uα) = 0 if ui = uα and 1 otherwise. Then, to incorporate data reliability (i.e. population size) directly into the geostatistical filter the kriging weights are rescaled according to Equation 12. The ability of the proposed approach to reconstruct the underlying disease risk from observed mortality rates has been tested in extensive simulation studies [with  studies .Figures One hundred realizations of neutral Models I through III were generated using Sequential Gaussian simulation and the information displayed in Figures Population in Long Island ZIP codes can vary substantially, ranging from 445 to 105,723, with a mean of 23,298, see Figure Using factorial kriging and the SMR semivariogram models displayed in Figures Figures Under the CSR model (Model I), results similar to the ones reported in  were fouα of 0.01101. The reason for that increase in p-values is illustrated for the ZIP code #11364 (Oakland Gardens) which was the only unit classified as high-low outlier under neutral model I. Figure Accounting for spatial autocorrelation (i.e. Model II) leads to a substantial reduction in the size of significant clusters compared to the CSR assumption. In fact only one ZIP code is a significant low-low cluster under Model II: Saint Albans (11412) which was the center of the Southern low-low cluster detected under CSR. The scattergram in Figure The map of significant ZIP codes at the bottom of Figure For female lung cancer, the same numbers of ZIP codes (6) were classified as significant outliers or clusters under neutral model III. The two low-low clusters are Springfield Gardens (11413) and Saint Albans, which was the only significant unit under neutral model II. These ZIP codes are both located in the western part of Long Island with low background SMR values, and in the same area the following three low-high outliers are found: Bellerose (11426), Little Neck (11362), and New Hyde Park (11040) surrounding the high-high cluster Glen Oaks (11004). The last low-high outlier is found in Shelter Island Heights (11965) in the eastern part of Long Island, though its SMR (72% of the New York average) has a wide confidence interval resulting from the small number of observed cases there (1,080 habitants). The two high-low outliers are found in central Long Island characterized by a low SMR background level: Ridge (11961) and Bayport (11705) with SMR values 20 to 40% higher than the New York state average. Three more clusters of high SMR (1.15 to 1.20) are found in the North western part of Long Island, next to the large group of low SMR recorded in Flushing and Jamaica: Bayville (11709), Mill Neck (11765), and Glen Cove (11542).For all three types of model, accounting for population size through geostatistical filtering leads to a larger number of ZIP codes classified as clusters and fewer outliers, see Figure Across all six types of neutral models, only one out of 214 ZIP codes is consistently classified into the same category: the low-low cluster at Saint Albans (11412) which has a SMR = 0.82 and a population of 37,452. The stability of this cluster under alternative specifications of the statistical null hypothesis can be used by cancer surveillance and control efforts to quantify the degree of confidence associated with this cancer cluster.Results of the cluster analysis for male lung cancer are displayed in Figures As for female lung cancer, accounting for spatial autocorrelation (i.e. Model II) leads to fewer significant ZIP codes compared to the common CSR assumption. Only two units are now significant: high-high cluster at Shirley (11967) and a low-high outlier at Rockaway Park (11694). Changes are also substantial when looking at results obtained under neutral model III. We found two high-high clusters: Shirley (11967) and Queens Village (11429), one low-low cluster: Corona (11368), and one low-high outlier: Elmont (11003).Accounting for population size in the cluster analysis (Model IV) enhances the size and compactness of the two major clusters of low and high SMR values, while the classification of three sparsely sampled ZIP codes (Cutchogue, Moriches and Rockaway Park with 3,444, 2,652 and 19,278 habitants respectively) changed from spatial outliers to clusters. A new cluster of high SMR values (SMR = 1.29) is also found in Lindenhurst (11757). Using spatially correlated neutral models the geostatistical filtering (Model V) reveals a new cluster of low SMR values in Port Washington (11050) and Great Neck (11024) with lung cancer SMR values that are 70% of the New York State average. Comparison of Models III and VI indicates that besides increasing the size of clusters identified under Model II geostatistical filtering leads to the identification of a new low-low cluster: Cold Spring Harbor (11724, with a SMR 63% below the New York state average) and one low-high outlier: Springfield Gardens (11413, SMR = 0.80).Across all six types of neutral models, only one out of 214 ZIP codes is consistently classified into the same category: the high-high cluster at Shirley (11967) which has a SMR = 1.157 and a population of 24,942.The use of randomization in test of hypothesis relies on the assumption that the space of solution is sampled fairly exhaustively and uniformly . It is Cancer mortality maps are used by public health officials to identify areas of excess and to guide surveillance and control activities. Maps of incidence as well as mortality are used as input to disease clustering procedures whose purpose is to identify local areas of excess and deficit. While some controversy revolves around the utility of these techniques, it is indisputable that the finding of a confirmed cancer cluster is often of considerable concern. The accurate quantification of local excesses and deficits, as well as regional trends and differences in cancer incidence and mortality, is therefore a problem of considerable practical importance.e.g. hotspots, coldspots, clusters, etc) above and beyond background variation. Most, if not all, environmental contaminants and diseases with potential environmental causes occur at a background level in the absence of a pollution- or disease-generating process. Nonetheless, this background pattern is typically ignored in spatial analyses that employ null hypotheses of complete spatial randomness. Because some spatial dependency is expected at background levels, CSR often is an inappropriate null hypothesis.Arguably one of the biggest problems facing spatial epidemiology and exposure assessment is that of identifying geographic pattern  of the null hypothesis. To the authors' collective knowledge, CSR is rarely if ever encountered in real-world biological systems. It is an apt descriptor of the "snow" that used to appear on late-night television when the programming day was over. It thus seems that neutral model types I and IV will seldom be appropriate. They perhaps will prove most useful for evaluating the extent of bias in past studies that employed CSR.The approach presented in this paper enables researchers to assess geographic relationships using appropriate null hypotheses that account for the spatial correlation and background variation modeled from the observed rates and any ancillary information (i.e. exposure model). An immediate consequence of using more realistic  neutral models are larger p-values, leading to a substantial reduction in the number of ZIP codes declared significant outliers or clusters across Long Island. This result confirms earlier findings that CSR often leads to an over-identification of the number of significant spatial clusters or outliers. These false positives have potentially serious consequences in that it can lead to public alarm and demands for investigation by already stretched state health departments. The drop in the number of significant units is however accentuated by the use of an adjusted significance level (Bonferroni adjustment) to account for the correlation between the tests conducted at neighboring ZIP codes. Further research should investigate the redundancy between the use of spatially correlated neutral models and adjusted significance level, which might lead to an "under-identification" of the number of significant spatial clusters or outliers.above and beyond background variation displayed in prior time intervals for cervix cancer mortality rates.When the constraint of local conditioning of neutral models is superimposed to the reproduction of spatial autocorrelation (i.e. model III), the approach allows one to detect local departures from the conditioning background specified by the user. In this paper, this background was identified to the regional variability of SMR data which was estimated geostatistically. Future research will investigate the use of exposure models for local conditioning of neutral models, leading to the detection of clustered or isolated geographical units that depart significantly from the cancer rates expected from exposure data. A similar approach has recently been implemented whereby the regional background observed in the past has been incorporated into the geostatistical simulation of neutral models . This neAnother issue, which often impacts the results of cluster analysis, is the lack of reliability of rates inferred from small populations. If ignored, large differences in population size decrease the ability of Moran's I to detect true clustering/departures from spatial randomness. A geostatistical smoother, which accounts for the spatial pattern of SMR data (i.e. anisotropic variability, range of autocorrelation), has been applied to eliminate the random variability that appeared as a nugget effect on the experimental semivariograms. The smoothing of local fluctuations, in particular the ones recorded in sparsely populated ZIP codes, resulted in the detection of larger and more compact clusters of low or high SMR values as well as the disappearance of some unreliable spatial outliers. Geostatistical filters are very flexible and could be used to filter short-range variability in addition to the noise created by small population sizes. In this case, the focus of the analysis would be on the regional background of the data, allowing the detection of regional clusters.The neutral models and methods in this paper make possible, for the first time ever, evaluation of the sensitivity of the results of cluster or boundary analyses to specification of the null hypothesis. Within a study, this will provide detailed quantification of the reliability of the results, and will identify those areas that are stable  or whose classification is highly sensitive to specification of the null hypothesis. This end result will be a spatially explicit analysis of potential false positives and false negatives.Authors PG and GMJ collaborated intensely on all aspects of the manuscript, from research design to data preparation. PG carried out most of the geostatistical and local cluster analysis and drafted the manuscript. Both authors read and approved the final manuscript.


Chlamydia pneumoniae is suspected to play a role in formation and progression of atherosclerosis. Many studies investigated cell death initiation versus inhibition by Chlamydia pneumoniae in established cell lines but nothing is known in primary human aortic smooth muscle cells, a cell type among others known to be involved in the formation of the atherosclerotic plaque. Type of cell death was analyzed by various methods in primary aortic smooth muscle cells after infection with Chlamydia pneumoniae to investigate a possible pathogenic link in atherosclerosis.The intracellular bacterium Chlamydia pneumoniae. Phosphatidylserine exposure was detected by flow cytometry in Chlamydia pneumoniae infected cells. Ultrastructure of Chlamydia pneumoniae infected human aortic smooth muscle cells showed extensive membrane- and organelle damage, chromatin condensation but no nuclear fragmentation. DNA fragmentation as well as cell membrane permeability was analyzed by TUNEL and NHS-biotin staining and occurred exclusively in cells carrying Chlamydia pneumoniae spots but not in smooth muscle cells with inclusions. These morphological features of cell death were not accompanied by an activation of caspase-3 as revealed by analysis of enzyme activity but involved mitochondrial membrane depolarization as shown by TMRE uptake and release of cytochrome c from mitochondria.Chlamydiae were found to be localized up to 72 h post infection in aortic smooth muscle cells either as single bacteria or inside of large inclusions. Quantification of host cell death by lactate dehydrogenase release assay revealed strictly dose and time dependent lysis for all tested isolates of Chlamydia pneumoniae induce a spot like infection in human aortic smooth muscle cells, which results in a chimeric cell death with both apoptotic and necrotic characteristics. This aponecrotic cell death may assist chronic inflammation in atherosclerotic blood vessels.This study provides evidence that  Chlamydia pneumoniae (Cpn) usually causes acute respiratory tract infections [Infection with fections . Chronicfections . Cpn hasfections  because fections ,5. The ofections -8 and hafections ,10. On tfections ,12. In gChlamydiae exhibit a unique developmental cycle with two morphological distinct infectious and reproductive forms: the elementary- and the reticulate body. The life cycle proceeds for 48 – 72 h and ends with pathogen release that may damage the host cells [Chlamydia can be postulated. On one hand it has been shown that Chlamydia psittaci and Chlamydia trachomatis can induce apoptosis in vitro [Chlamydia trachomatis were protected from apoptotic cell death induced by various stimuli [Chlamydia strain it is of relevance to determine cell death in Chlamydia infected primary cells [st cells . Infectist cells . A balanin vitro ,15. On t stimuli ,17. Mostry cells . As regaHuman aortic smooth muscle cells (HASMC) play an important role in the development of atherosclerotic lesions . TherefoApoptosis and necrosis represent two extreme morphologically defined forms of cell death -23. ReceIn order to examine the morphological characteristics of Cpn infection HASMC as well as the tumor cell line HEp-2 were infected with Cpn-K6 and Cpn-VR1310, respectively, and stained after 24 h, 48 h and 72 h with anti-Cpn-MOMP antibody. According to the staining protocol intra- and extracellular localization of Cpn could be distinguished Figure  as well To compare the lytic capacity of different Cpn isolates and to identify a possible time course of host cell death during the chlamydial infectious cycle, LDH release of HASMC was analyzed at 24, 48 and 72 h post infection.Mycoplasma spec. contamination as assessed by PCR. LDH release of mock treated HASMC or of cells inoculated with heat inactivated bacteria was comparable to the spontaneous LDH release of uninfected cells (data not shown).HASMC were inoculated with two different Cpn isolates: Cpn-VR1310 and Cpn-K6 at serial 2-fold dilutions ranging from 1 to 32 inclusion forming unit (IFU)/cell  was carried out. As a control apoptosis was induced by treatment with the well described kinase inhibitor staurosporin.Untreated HASMC had an elongated nucleus rich in euchromatin with one or two well-defined nucleoli Figure . The cytApoptotic HASMC treated with staurosporin showed a distinct condensation of chromatin Figure  and nuclIn general the overall ultrastructural morphology of HASMC infected with spot-like Cpn infection suggests a hybrid form of cell death: it shares chromatin condensation with apoptosis and damage of organelles and membranes with necrosis.The combination of chromatin staining by DAPI with labeling of DNA strand breaks via TUNEL staining and assessment of membrane integrity by NHS-biotin staining allows a distinction between necrosis as well as early and late phases of apoptosis using confocal laser scanning microscopy . Cells dHASMC were infected with Cpn for chosen periods of time or were inoculated with mock isolates or treated with staurosporin. Cells infected with Cpn-VR1310 Figure  showed aMock inoculated cells displayed round or oval shaped nuclei with finely dispersed chromatin visualized by DAPI staining and were always TUNEL negative. NHS-biotin was bound exclusively to the cell surface indicating intact membranes Figure .In Cpn infected cultures a variable labeling pattern of HASMC was observed. Cells with Cpn inclusions always displayed normal chromatin staining and no TUNEL or NHS-biotin labeling Figure . HASMC wIn staurosporin treated cells the rounded nuclei were fragmented or exhibited chromatin that was condensed marginally, whereas NHS-biotin labeling was predominantly negative Figure . Just a HASMC were infected for 48 h with Cpn-K6 or -VR1310 in 2-fold dilutions ranging from 8 to 386 IFU/cell or from 1 to 32 IFU/cell, respectively. As controls cultures were inoculated with mock isolates or with heat inactivated bacteria (data not shown) and analyzed for annexin-V binding and propidium iodide uptake. Uninfected as well as Na-azide treated HASMC served as negative and staurosporin-treated cells as positive controls for apoptosis Figure . Upon CpThe amount of annexin-V single positive HASMC infected with Cpn increased linearly in a dose- Figure  and time5A, BTaken together, the dose- and time dependence between infection and phosphatidylserine externalization were consistent with induction of early apoptotic events.After induction of apoptosis an enzymatic cascade is activated leading to the disassembly of the cell . Key enzA major role in the induction and regulation of cell death is played by mitochondria. Pro-apoptotic factors result at mitochondria in the dissipation of the mitochondrial membrane potential and release of cytochrome c. After infection of HASMC with Cpn-K6 or Cpn-VR1310 cells were loaded with TMRE (tetramethylrhodamineethylester) , a dye, The number of TMRE negative cells strictly increased with the chlamydial dose whereas blocking of chlamydial protein synthesis by addition of chloramphenicol completely abrogated this effect Figure . MoreoveChlamydia pneumoniae induce caspase-independent apoptosis-like cell death in infected HASMC by involving mitochondrial membrane dissipation resulting in the release of cytochrome c.Taken together, the data suggest Chlamydia and is associated with the suppression of bacterial replication and productive infection. The necessity for a reproductive infection was also shown for Chlamydia psittaci which induced caspase 3 independent apoptosis in macrophages and epithelial cells only after reproductive infection along with bacterial protein synthesis [In our in vitro study cell death was only induced in HASMC by infection with viable Cpn but not after incubation with heat inactivated or chloramphenicol treated bacteria. Additionally, different Cpn isolates showed various lytic activities towards host cells that correlated to different reproduction rates in tumor cells as measured by real time PCR . A reproynthesis . Cell deC. trachomatis which died through a combination of necrosis and apoptosis [Cpn induced cell death in HASMC did not fulfill all criteria for apoptosis or classical necrosis. Condensation of chromatin, positive TUNEL staining, externalization of phosphatidylserine, inhibition of TMRE labeling and loss of cytochrome c immunoreactivity in mitochondria were clear markers for apoptosis. But early damage of cell membrane and organelles as found by TEM and NHS-biotin labeling indicated necrotic type of cell death in Cpn infected cells. According to previous classifications Cpn mediated cell death belongs to apoptosis-like cell death  or can bpoptosis .Chlamydia infected cells. This effect could be inhibited by blocking bacterial protein synthesis using chloramphenicol. Moreover, cytochrome c was released from mitochondria of infected HASMC as indicated by loss of immunorecactivity. The important role of mitochondria in Chlamydia induced cell death has also been shown in tumor cell lines infected with Chlamydia psittaci [Chlamydia psittaci was blocked in cells over expressing Bax inhibitor and anti-apoptotic Bcl-2 [-/- mouse embryonic fibroblasts were shown to be resistant to Chlamydia muridarum-induced apoptosis and fewer bacteria were recovered after two infection cycles. This suggests that Bax-dependent apoptosis may be used to initiate a new round of infection [Chlamydia trachomatis infected mouse fibroblasts showed a higher level of apoptosis than Chlamydia infected HeLa cells as measured by annexin V / propidium iodide double labeling [Chlamydia infection than tumor cells.If cell death induction by Cpn in HASMC is caspase-3 independent the question arises about alternative pathways that are involved. Mitochondria are important regulators of cell death and loss of mitochondrial permeability transition is an important indication for apoptosis . Here wepsittaci . Pro-apoic Bcl-2 . Bax-/- nfection . In a selabeling . This nolabeling  but thatChlamydia-induced cell death. AIF which is upregulated and translocated from the mitochondria to the cytosol may play an important role in induction of caspase-3 independent cell death [Beside Bax other factors like apoptosis-inducing factor (AIF) may be involved in caspase-independent cell death  but theyll death -36. Injell death . Future Chlamydia trachomatis prevent cells in vitro from undergoing apoptosis [Chlamydia trachomatis, has been shown to interact with death domains and co-localizes with Fas. Recombinant CADD has been shown to induce caspase dependent apoptosis in several tumor cell lines [Chlamydia could interact with the host cell death machinery and inhibit or promote cell death.There exist numerous studies showing that Cpn and poptosis ,17,38-44poptosis  and relepoptosis -48 that poptosis ,49. CADDll lines . TherefoCurrently, it is unclear how the observed morphology of Cpn in HASMC and the induced aponecrotic cell death affects the progression of atherosclerosis. Cpn infection of HASMC was accompanied by phosphatidylserine externalization which might result in the ingestion of dying cells by macrophages. Macrophages will then either stop further progress of Cpn mediated damage or in turn become activated and thereby contribute to inflammation in the atherosclerotic vessel ,52. EarlCpn infection of cultured HASMC results in cell death that can be termed aponecrosis due to the sharing of apoptotic and necrotic features. This form of cell death occurs just in cells bearing single or aggregated bacteria but not in cells with inclusions. Aponecrotic HASMC may in vivo assist chronic infection in the vascular wall supporting the progression of atherosclerosis.Cpn-VR1310 and Cpn isolate Kajaani 6 (Cpn-K6) were kindly provided by G. Christiansen (Institute of Microbiology and Immunology, University Aarhus, Denmark) and M. Puolakkainen (Haartman Institute, University of Helsinki, Finland), respectively.Mycoplasma spec. contamination by PCR [HEp-2 cells obtained from ATCC (Manassas, USA) were cultivated in MEM-Eagle's (Gibco BRL, Invitrogen, Basel, Switzerland) and HASMC purchased from Clonetics (Verviers, Belgium) were maintained in DMEM supplemented with 10% FCS, 2 mM glutamine, all from Gibco BRL, Invitrogen, and 10 μg/ml neomycin . Cells and chlamydial organisms were tested for n by PCR  and 4',6Chlamydia-LPS antibody from Biorad (Redmont, USA). Donkey anti-mouse polyclonal antibody was obtained from Jackson Immuno Research  and staurosporin from Sigma Chemicals.Anti-Cpn-MOMP monoclonal antibody was from DAKO (Zug, Switzerland); anti-cytochrome c antibody raised in sheep was from Sigma, FITC-labeled monoclonal anti-Chlamydia-LPS antibody, inclusions were counted under a fluorescence microscope and the titer was calculated.Cpn was cultured according to an established protocol . Briefly4 cells/cm2 one day prior to infection. Infection with chlamydial organisms was performed in DMEM using titers between 8 and 386 IFU/cell which were added to HASMC, centrifuged onto the cells for 1 h at 1000 × g. Subsequently, the supernatant was replaced by DMEM containing 10% FCS, 2 mM glutamine and 10 μg/ml neomycin. In some experiments chloramphenicol 0.1 mg/ml was added analysis or cells were treated with 1 μM staurosporin for 14 h. Cells were grown at 37°C in the presence of 5% CO2 until they were analyzed.HASMC were seeded into 6-well plates (Nunc, Roskilde, Denmark) at a density of 2.4 × 10Live HASMC and HEp-2 cells were incubated with anti-Cpn-MOMP antibody (1:50) in PBS + 2% BSA for 1 h at room temperature (RT). After washing and fixation with 2% paraformaldehyde + 3% sucrose in PBS at RT, cells were incubated for 1 h with donkey anti-mouse-FITC antibody, diluted 1:100 in PBS + 2% BSA. Bacteria located extracellularly are stained exclusively green by this step. Subsequently, cells were lysed with 0.1% Triton-X 100 for 1 min at RT, rinsed with PBS + 2% BSA for 20 min, and incubated with anti-Cpn-MOMP antibody (1:50) followed by Texas Red-labeled donkey anti-mouse antibody (1:200) in 2% BSA in PBS. The second staining step identifies both intracellular and extracellular bacteria.Counting of cells with inclusions or spots as well as the number of inclusions and spots were performed on volume data obtained using a laser scanning microscope (SP2, Leica, Heidelberg, Germany) followed by image analysis using the software package Imaris (Bitplane, Zurich, Switzerland).4 cells/cm2, infected with Cpn between 2 and 32 IFU/cell and cultivated for 24 h, 48 h and 72 h. For cell death analysis 100 μl of cell-free supernatant was harvested, mixed with dye solution, incubated for 20 min and absorption was measured at 490 nm. Percent Chlamydia-specific lysis was calculated according to the formula: /(maximum release – spontaneous release) × 100. Spontaneous release corresponded to untreated HASMC and infected cells lysed additionally with Triton-X 100 showed the maximal release.LDH release of HASMC was assessed using a cytotoxicity detection kit (Roche) following the manufacturer's instruction. In brief, HASMC were seeded in a 96-well plate (NUNC) at a density of 2.4 × 104 in 50 mM sodium cacodylate buffer, pH 7.3 dehydrated in an ethanol series and embedded into epon . Ultrathin sections of 50 nm were contrasted with uranyl acetate and lead citrate and studied with a CM 100 transmission electron microscope (Phillips, Leiden, Netherlands).Cells were fixed in 50 mM sodium cacodylate buffer pH 7.3 containing 2% glutaraldehyde and 0.8% paraformaldehyde and postfixed with 1% OsOCells were trypsinized, washed in PBS and incubated with 0.1 mg/ml NHS-biotin (Pierce, Rockford, USA) in PBS for 30 min on ice, fixed with 3% paraformaldehyde + 2% sucrose in PBS and cytospinned onto glass slides. Samples were permeabilized with 0.1% Triton-X 100 in PBS for 1 min at RT and stained for DNA strand breaks using terminal transferase (Roche), incorporating dUTP-FITC (Roche) overnight at 37°C as previously described . SubsequPhosphatidylserine exposure and propidium iodide incorporation was assessed using the annexin-V-FLUOS staining kit (Roche). In brief, supernatants of HASMC cultures were collected and mixed with trypsinized cells. Samples were washed in PBS followed by 20 min staining with annexin-V / propidium iodide staining solution and analyzed by flow cytometry (Becton Dickinson, Basel, Switzerland).6 HASMC consisting of floating and adherent cells were lysed at 4°C in cell lysis buffer (20 mM PIPES, pH 7.2, 100 mM NaCl, 1 mM EDTA, 10 mM DTT, 0.1% CHAPS, 10% sucrose), subjected to three freeze-thaw cycles and centrifuged at 14000 × g for 10 min. Protein content of supernatants was measured using the micro BCA protein assay reagent kit (Pierce). Cell lysates containing 40 μg protein were incubated with DEVD-p-nitroanilide (0.8 mM) in lysis buffer and absorption was measured every 5 min in a spectrophotometer at 405 nm for 6 h at 37°C. Caspase-3 activity was calculated as the initial ascending slope of absorption depicted as arbitrary units.0.2 × 102, collected by trypsinization, combined with detached cells at time of trypsinization and analysed by flow cytometry (Becton Dickinson).Cells were stained with TMRE 100 nM and propidium iodide 2 μg/ml in DMEM with 10% FCS for 30 min at 37°C and 5% COCells were incubated in the presence of 50 nM mitotracker red for 30 min, subsequently fixed with 3% paraformaldehyde + 2% sucrose and were cytospinned onto glass slides. Samples were permeabilized with 0.1% Triton-X 100 in PBS for 1 min at RT. Subsequently stained with sheep anti cytochrome c (1 : 2000) in 3 % BSA in PBS for 1 h at 37°C followed by detetion with donkey anti sheep biotin antibody (1:200) in 0.5 % BSA in PBS for 1 h at RT. Cells were labeled with streptavidin Cy-5 (1 : 200) for 1 h at RT and analysed under a confocal laser scanning microscope.CD performed all infections and analyzed morphological markers for cell death.CFM and MKJS performed FACS experiments.DG initiated the study and performed the first infections of vascular cells.MW analyzed viability experiments and provided input for exeriments.PG provided critical intellectual input to the study and organized financial support.UZ established assays for the detection of caspase 3, morphological cell death markers and was leading the study.


The three-dimensional structure of the DBD shows that only the second and third Myb domains are directly involved in sequence-specific DNA-binding, while the R1 repeat does not contact DNA and only marginally affects DNA-binding properties. No structural information is available on the N-terminal 30 residues. Since deletion of the N-terminal region including R1 plays an important role in oncogenic activation of c-Myb, we asked whether this region confers properties beyond DNA-binding to the neighbouring c-Myb DBD.The transcription factor c-Myb is expressed in hematopoietic progenitor cells and other rapidly proliferating tissues, regulating genes important for proliferation, differentiation and survival. The DNA-binding domain (DBD) of c-Myb contains three tandemly arranged imperfect repeats, designated Myb domain R1-repeat than for the minimal DNA-binding domain.Analysis of a putative RNA-binding function of c-Myb DBD revealed that poly(G) preferentially inhibited c-Myb DNA-binding. A strong sequence-selectivity was observed when different RNA polymers were compared. Most interesting, the poly(G) sensitivity was significantly larger for a protein containing the N-terminus and the R2 and R3, but not R1, are necessary for DNA-binding, R1 seems to have a distinct role in enhancing the RNA-sensitivity of c-Myb.Preferential inhibition of c-Myb DNA binding by poly(G) RNA suggests that c-Myb is able to interact with RNA in a sequence-selective manner. While R The belocytes ,9.1, R2 and R3), located in the N-terminus of the protein. Determination of the structure of the c-Myb DBD has revealed that each repeat folds into three well-defined helices forming a helix-turn-helix-related structural motif, where the last helix in R2 and R3 makes specific DNA contacts (recognition helices) [myb gene, v-myb, found in the AMV and E26 viruses, has deletions in both ends, leading to a v-Myb protein lacking the N-terminus, most of the first Myb repeat (R1) and a large part of the C-terminal negative regulatory domain (reviewed in [The Myb family of proteins is defined by the presence of a well-conserved DNA-binding domain (DBD) composed of Myb repeats . Each rehelices) -15. The iewed in ).2R3 [1, is not fully understood. It has been shown to be dispensable for specific DNA-binding [1 stabilizes the protein-DNA complex [1 repeat does not contact DNA directly [v-myb-like truncation of the N-terminus of c-Myb (until the end of R1) has been shown to be sufficient for oncogenic transformation of chicken bone marrow cells [1 repeat could serve as a special regulatory module, either acting as a target for molecular interactions or being subject to post-translational modifications.The c-Myb DBD binds specifically to the sequence PyAAC(T/G)G, termed the Myb recognition element (MRE) -19. The 2R3 ,21. The -binding ,22, but -binding . Some gr complex ,25 and i complex . Accordidirectly . Howeverow cells . The R1 1 repeat confers properties beyond DNA-binding to c-Myb DBD. We sought evidence for a putative RNA-binding function, analogous with several other transcription factors harbouring dual DNA-RNA binding properties. The motivation for investigating RNA-interaction was the design of the c-Myb DBD, built of repeating modules, a design resembling the structural logic of zinc finger proteins. Some well-studied zinc fingers have been found to use subsets of the repeats as RNA- or DNA-binding units. The classical example is the Xenopus zinc finger protein TFIIIA, which acts as a DNA-binding activator of 5S ribosomal RNA genes [Xenopus oocytes [WT1) [In the present work we address whether the N-terminal region including the RNA genes ,29. TFII oocytes ,31. The  oocytes ,33. Anotes [WT1) -36. One es [WT1) ,35.Drosophila acts both as a transcription factor, activating zygotic segmentation genes during blastoderm formation, and as a regulator of mRNA translation by binding to the mRNA of another homeodomain transcription factor, caudal [The capability of specific interaction with both DNA and RNA is not restricted to the zinc finger family of proteins. The homeodomain protein bicoid of , caudal ,38. Inte, caudal ,13,15,21, caudal ). Murine1 might be implicated in RNA-binding rather than DNA-binding. As a test of our proposed RNA-binding function of c-Myb, we investigated the effect of different homoribopolymers on c-Myb DNA-binding. The homoribopolymer polyguanylic acid (poly(G)) strongly inhibited the sequence-specific DNA-binding of c-Myb, while poly(A), poly(C) and poly(U) did not, indicative of an RNA-binding activity. The same phenomenon, although weaker, was observed for A- and B-Myb. An order-of-addition experiment indicated that poly(G) bound directly to c-Myb in competition with DNA. Interestingly, the DBD construct containing the N-terminus and R1 was significantly more sensitive to poly(G) than the minimal DBD. Thus, the N-terminus including R1 seems to be important for the RNA-sensitivity of c-Myb DBD.Based on these occurrences of dual nucleic acid interactions, we asked whether a similar design was found in c-Myb. In particular, we raised the question whether the N-terminal region including R1 repeat is involved in RNA-binding we first examined the effects of homoribopolymers on c-Myb DNA-binding. The sequence-specific DNA-binding was analyzed by the electrophoretic mobility shift assay (EMSA) using two purified recombinant human c-Myb protein domains, NR1R2R3 (amino acid 1–192) and R2R3 (amino acid 89–192), in mixture. The assay was performed in the presence of increasing amounts of the homoribopolymers poly(A), poly(C), poly(G) and poly(U)  was added. We also tested two artificial RNAs, poly(I*C) and poly(I). The former is a duplex RNA, and the latter is a variant of poly(G) less prone to forming unusual structures, but retaining some of the pairing properties of guanosines. The duplex did not affect DNA-binding, while poly(I) caused some inhibition at high concentrations  Fig. . The DNAons Fig. . To conf1R2R3 was significantly more sensitive to poly(G) competition than R2R3, indicating that the N-terminus including the R1 repeat was important for the inhibitory effect.To better compare the sensitivity of the two proteins, we titrated the poly(G) inhibition Fig. . The DNA1R2R3 and R2R3 significantly, the first being fully inhibited while the latter seemed to be almost unaffected. In contrast, pre-incubation with DNA allowed the two forms to bind with similar efficiency. This supports the notion that the N-terminal region including the R1 repeat plays an important role in conferring RNA-sensitivity to the protein.Different plausible mechanisms for the observed poly(G) inhibition of c-Myb sequence-specific DNA-binding may be operating. Poly(G) might bind to c-Myb in direct competition with DNA. Or, the binding of poly(G) to c-Myb might be allosteric, inducing structural changes in c-Myb that reduces its DNA-affinity. A third explanation might be that poly(G) in a subtle way interacts with the DNA-probe and blocks its specific interaction with c-Myb. To clarify the mechanism of inhibition, we studied the importance of the order of addition of probe and homoribopolymers to the binding reaction Fig. . Three sThe homoribopolymer experiments reported above were performed with 15 minutes of incubation at 25°C. To exclude that the poly(G) effect was just a consequence of slower complex formation, we repeated the experiment with longer incubation times up to 90 minutes (results not shown). No change in the inhibition pattern was observed. This argues against the possibility that poly(G) only reduces the rate of c-Myb/DNA complex formation and indicates that the complex with RNA is highly stable.1R2R3 forms of the vertebrate Myb relatives A- and B-Myb together with c-Myb were analyzed by EMSA in the presence of different concentrations of poly(G) and poly(A). As shown in Fig. We then asked whether the poly(G) inhibition was specific for c-Myb or if other Myb proteins exhibited the same properties. Purified NR1, R2 and R3, among which R2 and R3 together are responsible for sequence-specific DNA-binding. The function of the first repeat, R1, has remained elusive in particular because R1 does not directly contact DNA. Its involvement in the process of oncogenic activation of Myb suggests a specific biological role of R1. In this report we have focused on the N-terminal region including the R1 repeat of c-Myb, trying to find a function beyond DNA-binding. A homoribopolymer inhibition assay revealed a strong preferential inhibition of sequence-specific DNA-binding by poly(G), suggesting that c-Myb can interact with RNA in a sequence-selective fashion. The most interesting observation was the finding that the RNA-interference function of c-Myb was highly dependent on the N-terminal region including R1. Upon exposure to RNA before DNA, the protein domain containing this region was severely inhibited by low concentrations of poly(G) while the protein domain lacking this region was almost unaffected. What the precise biological role of this novel RNA-interaction is, remains to be elucidated.The DBD of c-Myb contains three tandemly arranged pseudo-repeats, RCentral to our examination of the RNA-binding hypothesis is a homoribopolymer inhibition assay. Differential binding to homoribopolymers has been exploited as evidence for RNA-binding activity of several proteins, like the Ets-related transcription factor PU.1 , the neu1 -dependent RNA-interaction where G-rich RNA interacts more avidly than other RNAs. Why G-rich species are so much more potent inhibitors is not obvious. G-rich RNA molecules have special folding capacities that could be recognized by c-Myb. This is illustrated by the fact that poly(G) has been reported to fold into several unique structures, including single, double and four-stranded helices [In addition to the data presented above, the SELEX  technology was applied to search for more sequence-specific RNA patterns recognized by c-Myb. The SELEX procedure did produce specific patterns, confirming the proper behaviour of the experiment, but the selected RNAs did not seem to mimic the homoribopolymer effect in terms of inhibitory efficiency and content of G-bases (results not shown). This does not argue against RNA-binding per se, but indicates that c-Myb may not interact in a strictly sequence-specific fashion with RNA. Rather, we believe that the inhibitory RNA-effect seen in the homoribopolymer experiments reflects an R helices -49. To iThe poly(G) inhibition was surprisingly strong. Due to the undefined length of the homoribopolymers, it was not evident how to precisely determine their molar concentrations. A fictitious poly(G)-length of 23 nucleotides, which is the length of the probe, gives an RNA-concentration of about 6 nM when 1 ng is added to the binding reactions. Consequently, a six-fold estimated molar excess of poly(G) compared to the probe concentration (1 nM) was sufficient to completely abolish sequence-specific DNA-binding . Interestingly, specific nucleotide triphosphates did in fact inhibit DNA-binding of c-Myb with differences resembling the pattern observed with ribopolymers in the present work. GTP produced the most prominent effect among the nucleotides, while CTP or UTP had little or no inhibitory effect. However, since the inhibition was observed first in the mM concentration range, significantly higher than the amount of poly(G) producing the same level of inhibition, the GTP phenomenon seems to be only a weak reflection of the strong inhibition we see with poly(G). Still, it is intriguing that we observe the same specificity suggesting some type of specific interactions between guanosines and the DNA-binding domain of c-Myb.1 is not total, in the sense that without this region all RNA-interference disappears. Rather, the presence of this region seems to enhance the sensitivity to RNA-mediated inhibition several fold, making it possible to find conditions where a Myb DBD containing this region becomes fully inhibited while a minimal DBD remains more or less unaffected .It could be argued that we have shown mainly experiments of the RNA-interference type, not directly demonstrating RNA-binding. We have, however, several lines of evidence indicating that RNA-interference occurs through direct binding of c-Myb DBD to RNA. c-Myb NRThe identification of an increasing number of proteins capable of both DNA- and RNA-binding challenges the established picture of DNA-bound regulators with functions confined to promoter-activation, and suggests a broader function for some transcription factors . The pre1 repeat, was shown to contribute substantially to this RNA-sensitivity. This finding suggests a more specific function of the enigmatic first Myb repeat than having a stabilizing effect on DNA-binding only.We have obtained evidence that c-Myb DNA-binding is preferentially inhibited by poly(G) RNA, indicative of a sequence-selective RNA binding function. The N-terminus of c-Myb, including the RAll homoribopolymers were purchased from Sigma Aldrich. Purities were higher than 98% for all polymers. The length distributions of the homoribopolymers were determined by agarose gel electrophoresis and UV-shadowing. When compared to a dsDNA ladder, poly(A), poly(C), poly(G)and poly(U) migrated with a distribution corresponding to the following ranges: 200–1600 bp, 250–1200 bp, 100–700 bp and 250–700 bp, respectively (results not shown). RNase T1 was purchased from Ambion, Inc.E. coli (strain BL21 (DE3) LysS) using the T7 system [1R2R3) and 89–192 (R2R3), human A-Myb 1–187 (NR1R2R3) and B-Myb 1–183 (NR1R2R3). Expression and purification were performed as previously described [The following DBD subdomains were expressed in 7 system : Human cescribed .Sequence-specific DNA-binding was examined by electrophoretic mobility shift assay (EMSA) as previously described . The binmim-1 promoter: 5'-GCATTATAACGGTTTTTTAGCGC-3'. Double-stranded DNA oligonucleotides were 32P- labelled by T4 polynucleotide kinase according to the specifications of the manufacturer (Ready-To-Go T4 Polynucleotide kinase, Amersham Biosciences). Radiolabelled probe was purified on G-25 MicroSpin columns (Amersham Biosciences) or by polyacrylamide gel electrophoresis with subsequent gel extraction.The sequence of the MRE-containing DNA probe was from the EMSA gels were transferred to 3 MM paper and dried for 90 minutes at 80°C in a vacuum gel drier. Radiolabelled bands were detected with Molecular Imaging Screen BI (Bio-Rad) and analyzed with a Bio-Rad GS-250 PhosphorImager. Quantitation of band intensities was performed with the Molecular Analyst 2.0.1 software (Bio-Rad).ON performed the majority of the experiments and participated in the writing of the manuscript. TØA was responsible for the experiments shown in Fig.


The present article corrects common textbook accounts of Mendel's experiments by re-establishing what he wrote and how he accounted for his observations. It notes the long-established tests for the validity of any explanations that purport to explain observations obtained by experiment. Application of these tests to Mendel's paper shows that the arguments he used to explain his observations were internally consistent but were, on one crucial issue, implausible. The same tests are applied to the currently accepted explanation for Mendel's observations.The currently favoured explanation for Mendel's observations is untenable. It misrepresents Mendel, fails to distinguish between the parameters and the variables of any system of interacting components, its arguments are inconsistent, it repeats the implausibility in Mendel's paper, fails to give a rational explanation for his observed 3:1 trait ratio and cannot explain why this ratio is not always observed in experimental practice. A rational explanation for Mendel's observations is initiated. Readers are challenged to complete the process before a further article appears. We all talk, more or less knowingly, about Mendelian genetics. But four questions need to be asked and answered.1. Do we understand Mendel's work?To judge from nearly all modern accounts of genetics, we do not. Mendel's paper of 1866 has been persistently misrepresented ever since it was rescued from obscurity in 1900.2. Do we teach our students a rational description of the inheritance of traits?The answer is again no. Why? Because our current depiction of the inheritance of traits or characteristics is based on false statements, inconsistent arguments and an implausible assertion.3. Does the current description of Mendelian genetics account for his observations of dominant and recessive traits?No, for the reasons given in answering question 2.4. Do we account rationally for Mendel's observation of a 3(dominant):1(recessive) trait ratio in some but not all of his experiments?The answer is again no. The reasons will become clear in this article and its successor.A survey of the relevant literature for the period from 1900 to 2003 shows that the various misrepresentations of Mendel's first paper [There are two long-established tests of the validity of any hypothesis or proposed explanation for the results observed by experiment. The first test asks: Are all the arguments employed consistent, one with all the others? The second test asks: Are all the proposed mechanisms plausible? Could they be confirmed by experiment, i.e. by a "real"experiment or by a logical "thought experiment". Both tests must be passed if the proposed explanations for the observations are to be accepted.If judgement is being passed on work carried out in the distant past, allowance must be made for the availability or lack of availability of tests of plausibility at that time. On the other hand, we should not hesitate to criticise a current explanation that fails tests of plausibility that are now available but were not available in the past.These two tests of validity (consistency and plausibility) will be applied to Mendel's explanation for his observations and to the currently favoured explanation for his observations.Versuche paper [We must first re-establish what experiments Mendel performed and what he wrote in his published accounts of these experiments in order to correct the various false textbook descriptions of Mendel's work. For this purpose it is necessary to study authentic reprints of his two papers ,2. The fhe paper . There mVersuche über Pflanzen-hybriden (Investigations on plant hybrids).Many earlier biologists had noted the appearance of hybrid plants but their findings did not show how hybrids arose, whether there was any regularity in their occurrence, or how their properties were related to those of their parents. Mendel showed that there was a general rule for the appearance of hybrid plants and that an exact relationship existed between the traits displayed by hybrids and those displayed by their parents. Hence the title of his first paper: Mendel recognised five preconditions for success in his experiments on the origin of hybrids:Pisum sativum (the edible pea plant) for most of his work because many established varieties were readily available; and because the flowers enclose the reproductive organs, so minimising accidental cross-fertilisation by insect-or air-borne pollen.(i) He needed suitable plants for his experiments. He chose Pisum sativum, like all leguminosae, is androgynous. The flowers contain both male (pollen or sperm) and female  cells and are therefore normally self-fertilising. This provided experimental advantages, as we shall see.(ii) (iii) It was necessary to have stocks of true breeding plants for his cross-fertilisation experiments. He therefore spent much time establishing that 22 varieties of edible pea plants were in fact true breeding. He discarded those plants that were not true breeding before starting his experiments on hybridisation.(iv) He had to ensure that any cross-fertilisations were strictly under his control. To achieve this control, he removed all the immature pollen-bearing stamens from a true-breeding pea plant that displayed a particular trait, e.g. green seeds, then transferred pollen to these emasculated flowers from another true breeding pea plant that displayed an alternative form of the same trait (e.g. yellow seeds).(v) Success depended on meticulous enumeration of the occurrence of hybrids, and of alternative traits, in the populations of plants that arose from his cross-and self-fertilisation experiments; and on repetition of each cross-fertilisation and self-fertilisation experiment in order to obtain reliable, average, results. Table The following account uses the notation proposed by Bateson  for succP = the original male and female parental generations;F1 = the first filial progeny population arising from crosses between plants of the P generation;F2 = the second filial generation that arises from sexual reproduction by members of the F1 generation – and so on.The advantage of Bateson's notation is that it does not depend on any preconceived ideas about the mechanisms of inheritance of traits during sexual reproduction. It can therefore be used to describe the stages in Mendel's experiments without misrepresenting any of his observations, arguments or conclusions.A) form to (a) form plants was, in every case, close to 3:1. Mendel also carried out experiments in which he cross-fertilised plants displaying concurrently two or three trait differences, and then recorded the occurrence of each trait in the F1 and F2 generations. These results are not shown here but they were consistent with the findings exemplified in Table Table A) and (a) displayed by the parental (P) plants.(i) All plants in the F1 population displayed only one of any two differing trait forms  – the dominating trait (A). He defined the alternative trait form, which did not appear in any of the F1 plants, as das recessivem Merkmal (a) – the recessive trait (a).(ii) He defined the trait form that was displayed in the F1 plants as a) were 'constant form' (true-breeding) plants; i.e., when they were allowed to self-fertilise, all their F3 progeny displayed the same parental trait (a).Mendel now faced the problem of explaining how the 3(dominant):1(recessive) trait ratio arose in the F2 population of plants (Table A) were allowed to self-fertilise some proved to be 'constant form' plants because, when they were allowed to self-fertilise, they produced F3 progeny that again displayed this same parental trait (A). But other plants initially identified by the symbol (A) in the F2 population were not 'constant form' plants. Some of their F3 progeny did display the original parental trait (A). Other plants in the same F3 population displayed the alternative parental trait (a). Yet other plants in this F3 population were again not 'constant form' plants. They were like the F1 plants (their "grandparents") and like the F2 parents from which they were immediately derived. When they were allowed to self-fertilise, some of their progeny displayed the (A) form, some the (a) form of trait and some were again like the F1 plants. The experimental procedures Mendel used to make these distinctions are readily understood by reading a reprint of the original paper or a reliable translation.On the other hand, when F2 plants initially identified by the symbol (A) and those now designated (Aa), Mendel was able to state the average distribution of trait forms among the plants of the F2 population as (one dominant: two hybrid: one recessive) or, in his notation, (A + 2Aa + a); i.e. the 3:1 trait ratio factored into the proportions 1:2:1.Given this ability to distinguish, by experiment, between those plants initially designated ((A) that was displayed by the F1 plants, 1/4 displayed the 'constant form' parental trait (a) that did not appear in any of the F1 plants (Table Aa) that displayed only the dominant trait (A) but were not 'constant form' plants.Mendel was now able to add a further generalisation: When F1 plants were allowed to self-fertilise, 1/4 of the F2 population displayed the 'constant form' parental trait traits. Examples have already been given of the use of letters (A) and (a) when only one trait difference between parental plants was tested (Table B) and (b), (C) and (c) when he described experiments in which two or three trait differences were displayed concurrently.Mendel used upper case and lower case italicised letters throughout his paper to denote, by definition, dominant and recessive ed Table . Mendel For reasons given in Section 2.5 these single letters also designated what Mendel called 'constant forms' of traits. Plants displaying these traits were 'true-breeders'; they were the parental plants he used in cross-fertilisations Table .A, a, B, b, C, c) did not represent the structure or composition of the traits. All the traits shown in Table A) and each recessive trait by (a) in Table There is one further crucial feature of Mendel's single letter notation for 'constant form' traits. These letters (A, a, B, b, C, c) represented classes of traits – a dominant class represented by an upper case letter, and a recessive class of trait represented by the corresponding lower case letter (Table The letters (er Table . It is nAa) to represent hybrid plants? This will become clear in section 2.7.Why then did Mendel use a combination of letters  by the following postulates and arguments; they were based on his further experiments (section 2.5):-Aa) in welcher beide Merkmale vereinigt sind – in which both  traits (A and a) were united; trait (a) was not displayed by these hybrids, so that these hybrids displayed what he had defined as the dominating trait (A) only.(1) All the F1 plants were hybrids (A) and (a) in the F1 hybrids (Aa) segregated into traits (A) and (a) during formation of the male pollen (sperm) cells and also during formation of the female germinal cells (ova). Thus, each pollen cell and each germinal cell carried only one trait – either (A) or (a) but not both.(2) The traits ((3) Fertilisation of one germinal cell by one pollen cell was a random event.A) fertilised a germinal cell bearing the same trait (A), all their progeny displayed the trait (A). Likewise, when a pollen cell bearing a trait (a) fertilised a germinal cell bearing the same trait (a), all their progeny displayed the trait (a). But when a pollen cell bearing trait (A) fertilised a germinal cell bearing the alternative trait (a), the resulting plant was the hybrid (Aa); if the pollen cell displaying a trait (a) fertilised a germinal cell displaying the alternative trait (A), the outcome was again a hybrid (Aa). In either event, the hybrid (Aa) displayed only the dominant trait (A).(4) When a pollen cell bearing trait (Aa), given that traits (A) and (a) in the hybrid (Aa) first segregated into individual pollen cells (sperm) and individual germinal cells (ova) before recombining, in random fashion, during formation of the F2 population. The arrows in Figure (5) Mendel illustrated these postulates and explanations in a diagram Figure  showing Note two crucial points:-die Merkmale) or the characters (die Charaktere) in his plants and their seeds, not the mechanisms underpinning these occurrences. These mechanisms could not have been investigated in 1866.(i) Mendel observed and recorded the occurrence of traits ((ii) All Mendel's explanations were based solely on observations of the changes in the occurrence of alternative traits in successive populations that arose from cross-or self-fertilisations and back-crosses.combination, segregation and recombination of traits during gametogenesis was the most important feature of Mendel's work. Moreover, Bateson realised [[Mendel was a well-trained scientist , an astuealised [; in a foealised [-12, was "das gen;  die gene" as the determinant of a trait; he was also the first to make a clear distinction between the genotype (der Anlagetypus) and the phenotype (der Erscheinungstypus) on the basis of his experiments with self-fertilising bean plants. In Johannsen's experiments the weights of individual beans were the characteristics or traits. He had, in effect, repeated Mendel's experiments but by measuring a trait  he was able to introduce three new concepts (gene, genotype and phenotype) that were the most significant, after Mendel's concepts of combination, segregation and recombination of traits during gametogenesis, in understanding the origin of genetic phenomena (the origin of changing traits).Another leading contender for the title "the first geneticist" was the Danish biologist, Johannsen ,14, an eth century. This failure is, surprisingly, still evident in current depictions of elementary Mendelian genetics (Section 3).Failures to recognise the significance of Johannsen's work ,14 preveAa) are consistent with his arguments for the segregation of the component traits of the hybrid into separate gametes, and their random recombination in generating the F2 population (A + 2Aa + a). Mendel's arguments pass the test of consistency.It is clear that Mendel's experimental procedures (sections 2.2, 2.5) were sound; his notation was simple, unambiguous and consistently applied (section 2.6). His arguments (section 2.7) for a combination of traits in forming the F1 hybrids (Aa) was formed by combining the two differing traits (A) and (a) of their parents. He did not explain how a F1 hybrid (Aa) displayed only trait (A) and how it did not display trait (a), even when some F2 plants, like one of the two original parental (P) plants, did display trait (a). What explanation could we now give for this selective display of only one of two traits that are said to be combined in a hybrid?It is equally clear (but hitherto not noticed) that Mendel's explanations failed the test of plausibility. Mendel postulated that a F1 hybrid (A) was displayed by the hybrids (Aa) because (A) was a dominant trait and (a) was a recessive trait. Such statements do not even qualify as a circular argument. They are illogical. Such statements fail to distinguish between an arbitrary definition and a plausible explanation. Mendel's definition of a dominant trait should be seen as an arbitrary device that accounts for his observation (by experiment) that his hybrids (Aa) in the F2 populations (A + 2Aa + a) displayed trait (A) but not trait (a).It may be (and has been) argued by some that trait (Aa) for a hybrid was crucial in establishing his consistent arguments; it was also the basis for Bateson's recognition that the essential features of Mendel's work were the concepts of combination, segregation and recombination of alternative traits (i.e., components of the phenotype). If we now wish to replace Mendel's implausible formulation (Aa) for a hybrid by a plausible formulation, we face the prospect of abandoning the rest of Mendel's arguments. That is not to say that we abandon admiration for Mendel's work. For its time, it was unsurpassed and should be recognised as one of the important steps in the development of experimental procedures in what became known as genetics. We should take care not to misrepresent Mendel's experiments and his arguments. It will become clear that misrepresentations of Mendel's paper have served only to sustain untenable concepts in current biology.A word of caution is necessary. Mendel's formulation  that combine, segregate and recombine. May we then anticipate that modern explanations of Mendel's observations will pass the tests of consistency and plausibility?The currently favoured explanation for Mendelian heredity in general, and in particular for the occurrence of Mendel's 3(dominant):1(recessive) trait ratio, is shown in Figure The assertions and descriptions generally attached to Figure (i) Mendel explained his experimental results by assuming that particles or factors  determined or specified the observed traits.A) is a dominant allele;(a) is a recessive allele.(ii) ((Aa) segregate into separate gametes. Each gamete then contains only one dominant allele (A) or only one recessive allele (a).(iii) The alleles in the male and female heterozygous somatic cells (iv) Fertilisation is a completely random event. Given a large number of fertilisation events, the possible recombinations of alleles are those displayed in the four squares.AA + 2Aa + aa).(v) Therefore the average distribution of the alleles at one diploid locus in the resulting progeny population of individual plants will be (It is then argued that:AA) will give rise to a dominant trait (AA).(vi) The dominant allele pair (aa) will give rise to a recessive trait (aa).(vii) The recessive allele pair (Aa), the recessive allele is ineffective, or is suppressed by the dominant allele (A), so that only the dominant allele (A) is expressed in the heterozygote. Expression from one (A) is as effective as that from two dominant alleles (AA). Thus the heterozygote (Aa) expresses a dominant trait.(viii) In the heterozygote (AA + 2Aa + aa) is expressed  as the trait series (AA + 2Aa + aa).(ix) Therefore the allele series ((x) This trait series gives rise to Mendel's 3(dominant):1(recessive) trait ratio (by the arguments in vi, vii, viii).There are seven faults in the descriptions and arguments attached to Figure (i) Mendel is misrepresented; he did not assume that particles or factors specified the observed traits. It is historically inaccurate and scientifically misleading to suppose that he made any such assumption.A) and (a) are Mendel's notation for dominant and recessive traits  should refer to traits alone.(ii) The letters  and the same nomenclature (dominant and recessive) for both.(iii) Figure UU + 2Uu + uu); this states that a given locus, in three genetically related diploid cells, comprises a pair of two normal alleles (UU), or one normal and one mutant allele (Uu), or a pair of two mutant alleles (uu). Mutations change the allele constitution or composition at a locus. The modern (non-Mendelian) notation (AA + 2Aa + aa) in Section 3.1 (items vi, vii) then states explicitly that a dominant trait (AA) comprises two aliquots (A + A) of some material substance or of two doses of dominance (A + A); likewise that a recessive trait (aa) is composed of two entitities (a + a) or two doses of recessivity. This is simply not true. It was not true in Mendel's time and it is not true today. Furthermore, it is not what Mendel's notation meant. It was pointed out (Sections 2.5, 2.6) that Mendel's notation (A) and (a) distinguished classes of traits, specifically 'constant form' classes of traits (Table AA) for (A) and (aa) for (a) in a trait series is illogical and indicates a regrettable failure to read Mendel's paper with the care that should be given to one of the classic papers in biology.(iv) Because we must not confuse alleles with traits, we could reasonably write an allele series as (AA) generate a dominant trait (AA); and that two recessive alleles (aa) generate a recessive trait (aa). In other words, it is asserted that there is a direct, positive, linearly proportional (or additive) relationship between the allele constitution at a gene locus and the constitution of the trait expressed from that locus. If we are to be consistent, the same arguments should apply to the heterozygote (Aa).(v) If the arguments attached to the homozygotes in Figure A) in a heterozygote (Aa) is as effective as two dominant alleles (AA) in the homozygote. The arguments in item (viii) are therefore inconsistent with arguments in items (vi) and (vii). Item (viii) also transfers Mendel's implausible assertion that a hybrid (Aa) displays only trait (A) to the equally implausible assertion that one allele (A) in a heterozygote (Aa) is as good as two such alleles in a homozygote (AA). The argument in item (viii) that allele (a) is ineffective is an extreme case; it is therefore not generally applicable. The alternative argument, that allele (a) in a heterozygote is suppressed by the dominant allele (A), lacks any experimental support or rational theoretical justification. Items vi, vii and viii attached to Figure On the contrary, the arguments in section 3.1 (item viii) state that one dominant allele ((vi) Figure (vii) Figure parameter and a variable in the same system. Parameters are those components of any system that are directly accessible to the experimentalist; they can be changed and maintained by the experimentalist at the new value, at least for the duration of an experiment. Variables are those components of the same system that are not directly accessible to the experimentalist; they can be changed and maintained at a new value only by making a finite change in at least one parameter of the system or of its immediate environment. The magnitudes of individual variables, in any system, respond to changes in the magnitude of one or more parameters of the system or of the immediate environment.It is necessary to restate fault (iii) in section 3.2 in more widely applicable terms. It is illegitimate to use the same notation and nomenclature for a In the case under discussion, the alleles are parameters (and part of the genotype); the traits are variables (and part of the phenotype). If the parameters and variables of any system of interacting components are represented by the same notation and the same nomenclature, confusion will inevitably result – as illustrated by Figure Figure because the alleles are dominant or recessive. This habit is unscientific. It also occurs in discussion of mutations of non-catalytic proteins.The primary error in Figure When haemoglobin A (HbA) is mutated to the sickle cell haemoglobin (HbS), the three possible trait forms are correctly depicted as follows:normal/normal protein, condition;(A/A) – the homologous, normal/mutant protein (sickle cell), condition;(A/S) – the heterologous, mutant/mutant protein, condition.(S/S) – the homologous, Contrast these depictions with those sometimes found:dominant condition;(A/A) – the sickle cell condition;(A/S) – the recessive condition.(S/S) – the dominant and recessive from traits (variables) to alleles (parameters) and the contention that, if alleles are themselves dominant or recessive, their expressed traits must always be dominant or recessive. If changes in the composition of non-catalytic proteins do explain the occurrence of Mendel's dominant and recessive traits, we require a demonstration that does not depend on these illogical notions.These latter statements depend solely on the illegitimate transfer of Mendel's terms The sickle cell trait (A/S) in humans is significantly different from the normal trait (A/A).where malaria is endemic. They do not die from malaria as frequently as those in the population with the (A/A) condition. The sickle cell condition (A/S) is debilitating but, provided it is not too debilitating, the frequency in the local population of those carrying the (A/S) protein pair is greater than it would be in malaria-free areas.Those carrying the sickle cell (A/S) condition enjoy an advantage in areas dominant and recessive noted above. The appropriate term is "heterozygous superiority". The "superiority" indicates the better chance of surviving in regions where malaria is endemic.This higher frequency of the sickle-cell (A/S) condition in areas where malaria is endemic is often said to be an example of "over-dominance". The term "over-dominance" is inappropriate. It presumably arose from the illegitimate transfer of the terms A and a), and the same nomenclature (dominant and recessive), to describe an allele series and a trait series can be traced to Sutton [AA:2Aa:aa which is the same as that given for any character in the Mendelian case." Mendel's expression (A + 2Aa + a) gave the proportions of characters in his F2 population as A:2Aa:a. Sutton gave no justification for rewriting these proportions in the form AA:2Aa:aa. By writing the expression for chromosome pairs as AA:2Aa:aa and the expression for the proportions of F2 characters as AA:2Aa:aa, Sutton established a direct, one-for-one, relationship between pairs of chromosomes and the traits arising from them. This false relationship also persists in the currently favoured depiction of Mendelian genetics  was later transferred to pairs of alleles (what Sutton described as subunits of the chromosomes).The illegitimate use in Figure o Sutton . Sutton th Century who copied his error, were struggling to understand the hereditary origin of traits.It would be easy to blame Sutton for our present confusions. We should remember that Sutton, and those in the early years of the 20We may more reasonably ask: Why, one hundred years later, are these obvious errors still one of the features of Figure Traits (variables) may be dominant or recessive, as defined by Mendel. Alleles (parameters) are, always have been, and can only be normal or abnormal (mutant). Harris (pages 143–157 in reference ), for exdominirende ("dominating") from traits to genes; the second and the third quoted texts ignored the fact that the term "das gen"  was first used and its role as the determinant of traits postulated by Johannsen, 43 years after Mendel's paper was published (Section 2.8); Mendel did not mention the word gene (Section 3.2). Of the 13 texts examined, only one gave a definition of dominance and recessivity that would have been recognised by Mendel. Even so, this author contradicted his correct definition of dominance and recessivity as properties of components of the phenotype by giving an explanation of elementary Mendelian genetics that employed Figure A review of 13 textbooks of genetics showed that in 12 instances, dominance and recessivity were defined specifically as properties of genes or alleles. These texts, published between 1982 and 2002, were intended for student use; their definitions of dominance and recessivity ignore Mendel's definition of dominance and recessivity as properties of the traits (sections 2.4, 2.5, 2.6, 2.7); they take no account of the need to distinguish between the parameters and variables of a system of interacting components (section 3.2). In one of these 12 texts, it was further claimed that: "Mendel proposed the existence of what he called particulate unit factors for each trait". In another, that: "Mendel realised that some genes (dominant genes) expressed themselves when present in only one copy". In a third that: "Mendel imagined that during the formation of pollen and egg cells, the two copies of each gene in parents segregate". Of these three quoted texts: The first misrepresented Mendel; he did not "propose the existence of particulate unit factors for each trait". The second misrepresented Mendel by transferring his term The correct nomenclature for alleles used by Harris (pages 143–157 in reference ) is, unfIf we continue to propose Figure Barker , writingalleles by symbols that differ from those (A, a, B, b, C, c) used by Mendel to represent traits. We must replace symbols (A and a) for alleles in Figure U) to represent a normal allele, not a "dominant allele"; and (u) to represent a mutant or abnormal allele, not a "recessive allele". The F2 allele series in Figure UU + 2Uu + uu.A fresh approach to the origins of dominant and recessive traits is needed. As a first step, we need to represent normal and mutant trait series in Figure (A + 2Aa + a) because, as explained earlier, Mendel was concerned (as we are, first and foremost) only with understanding the origin of two classes of trait – the dominant class (A) and recessive class (a). We will later be concerned with the quantitative composition of traits.Similarly, the Aa) for a hybrid that, allegedly, displayed the trait (A). An implausibility, like an inconsistency, must be eliminated if we are to arrive at an internally consistent and plausible account of Mendel's observations. The implausible notation (Aa) can be eliminated by replacing it by the single symbol (H) for a hybrid.We have, however, already identified an implausibility in Mendel's notation (parameters of the system (in this particular case, the components of the genotype) and the variables of the system (in this particular case, the components of the phenotype).We have now adopted a stance that, in sharp contrast to Figure A + 2Aa + a). Replacing Mendel's notation (Aa) for a hybrid by the single symbol (H) does not alter Mendel's experimental observation of the proportions of trait forms in the F2 populations (section 2.5). It does mean that we can avoid Mendel's implausible postulate that, although recessive trait plants did display trait (a), his hybrids (Aa) did not. We have, of course, to discover an experimentally verifiable mechanism that would explain why hybrids (H) display a trait that is sometimes indistinguishable and sometimes distinguishable from trait (A).Mendel found, by experiment, that the proportions of different plant forms in his F2 populations were 1(dominant trait):2(hybrids):1(recessive trait) or, in his notation,  in the F2 population is expressed as the trait classes (A + 2H + a) in that population, where all that we have done is to replace Mendel's implausible (Aa) by a plausible (H). Note also that we have now also eliminated the illegitimate use of paired symbols for Mendel's dominant (A) and recessive (a) traits.Our remaining task is to explain rationally how this series of U) in a heterozygote (Uu) could be as effective as two such alleles (UU) in a homozygote.Most of the clues that facilitate this task are present in this article. One clue is missing, but it can be inferred by asking how one allegedly dominant allele  why Mendel's 3:1 trait ratio, though not uncommon, does not always occur.A further article will provide the answers, but in the interval readers may like to rise to the challenge of explaining: (1) how


To evaluate current treatment patterns for coronary artery revascularization in Canada and explore the potential impact of drug eluting stents (DES) on these treatment patterns.Eleven cardiologists at multiple Canadian academic centers completed a questionnaire on coronary artery revascularization rates and treatment patterns.Participating physicians indicated slightly higher rates of PTCA, CABG, and stent implantation than reported in CCN publications. Participants estimated that 24% of all patients currently receiving bare metal stents (BMS) would receive DES in the first year following DES approval in Canada, although there was a large range of estimates around this value (5% to 65%). By the fifth year following DES approval, it was estimated that 85% of BMS patients would instead receive DES. Among diabetic patients, estimates ranged from 43% in the first year following approval to 91% in the fifth year. For all patients currently receiving CABG, mean use of DES instead was estimated at 12% in the first year to 42% at five years; rates among diabetic patients currently undergoing CABG were 17% in the first year to 49% in the fifth year.These results suggest a continued increase in revascularization procedures in Canada. Based on the panel's responses, it is likely that a trend away from CABG towards PTCA will continue in Canada, and will be augmented by the availability of DES as a treatment option. The availability of DES as a treatment option in Canada may change the threshold at which revascularization procedures are considered. Coronary artery disease (CAD) is a common condition in western society . TreatmeTreatment choices and treatment patterns for CAD have changed over the past several years, and are likely to evolve further in the next few years. Drug eluting stents (DES) are a newly available treatment modality. DES are stents that incorporate bioactive coatings (polymer or non-polymer) permitting the release of associated molecules to attenuate the processes of restenosis. Preliminary clinical data suggests that use of DES can substantially reduce the rates of restenosis seen following implantation of bare metal stents but at much lower incidences of severe procedure-related complications as compared to CABG surgery ,4. DES mIt is likely that the availability of DES will change treatment patterns for patients with CAD . To bettThe objective of this study was to obtain opinions and estimates from Canadian cardiologists regarding the annual rates of PTCA, CABG, and stenting in Canada, treatment patterns associated with these procedures, and the potential future impact of DES on coronary revascularization treatment patterns. The results from this questionnaire were used in the development of health economic models used to evaluate these stents for use among Canadian patients.The questionnaire asked participating physicians to assess the projected annual procedure rates for CABG, PTCA and stent implantations published by the Cardiac Care Network of Ontario (CCN) Target-Setting Working Group . In addiCardiologists at multiple academic centers distributed across Canada were identified for this study. These physicians were contacted, provided a brief introduction to the study, and offered a study honorarium of $400 dollars to participate. The questionnaire was distributed to physicians willing to participate. Completed questionnaires were collected and data was entered into Excel. Summary statistics including mean, median, maximum, and minimum values were computed for the various estimates provided by the participating physicians. The summary statistics were returned to those doctors who completed questionnaires. Study participants were then asked to comment on the summary results and indicate any questions or comments on the results. At the time of the study, only one drug-eluting stent (CYPHER™, Cordis Corporation) had been approved for use by Health Canada (Nov. 2002).A total of 18 Canadian physicians were contacted and invited to participate. Eleven physicians agreed to participate and complete the questionnaire, a response rate of 61%. All eleven physicians were male, specialized in cardiology, and had experience in this specialty ranging from 6 to 30 years. The average length of time specializing in cardiology was 12.8 years. The average age of participating physicians was 44.0 years. In each of the result tables, the number of panel members responding to each item is reported.The CCN Target-Setting Working Group (TSWG) projected annual rates of CABG surgery and PTCA of 110 and 160 per 100,000 Canadians, respectively [Table Tables Tables Projected use of DES among diabetic patients who currently receive bare metal stents is presented in Table Table Similar to DES adoption among bare metal stent patients, the likelihood of DES use among CABG patients increases with each year after approval. At year five, 42% of all CABG patients are likely to receive DES compared to 85% of bare metal stent patients. In all years except year three, the proportion of single vessel disease CABG patients instead receiving DES is less than the proportion for multi-vessel disease CABG patients.Table Rates for single and multi-vessel disease patients with diabetes are similar; however, as noted above, missing data makes comparison of these subpopulations to the overall diabetic population difficult. During the fifth year following approval, an estimated 48.6% of diabetic patients who would have received CABG surgery are instead projected receive DES.should receive DES rather than these other procedures. In requesting this additional information, the questionnaire specified that respondents could indicate that the proportion of patients who should receive DES is the same as or different from the proportion that are likely to receive DES  for coronary artery revascularization to increase over the next five years. DES will both be used instead of bare metal stents and an alternative to CABG surgery. This increase in DES use will increase initial procedure-related costs compared to bare metal stents, but is likely to decrease subsequent costs due to the decreased need for repeat revascularizations. Medical care decision makers and planners need to prepare for this increased use, in terms of both facility and budget allocation as well as staffing availability and training.This study was performed under a research contract from Boston Scientific Corporation. MH has received research funding from Boston Scientific. ML, MAC, and MV are employees of Boston Scientific Corporation. Boston Scientific is providing the article-process charge for this manuscript. Boston Scientific holds a number of patents on the TAXUS Express2 Paclitaxel-Eluting Coronary Stent System. Publication of this article may result in increased consulting work for Exponent, Inc.MH directed the overall study and participated in all aspects. ML and MV participated in study design and questionnaire development. MAC participated in data analysis. All authors participated in preparation of the manuscript and read and approved the final manuscript.The pre-publication history for this paper can be accessed here: