High-density oligonucleotide microarray technology enables the discovery of genes that are transcriptionally modulated in different biological samples due to physiology, disease or intervention. Methods for the identification of these so-called "differentially expressed genes" (DEG) would largely benefit from a deeper knowledge of the intrinsic measurement variability. Though it is clear that variance of repeated measures is highly dependent on the average expression level of a given gene, there is still a lack of consensus on how signal reproducibility is linked to signal intensity. The aim of this study was to empirically model the variance versus mean dependence in microarray data to improve the performance of existing methods for identifying DEG.In the present work we used data generated by our lab as well as publicly available data sets to show that dispersion of repeated measures depends on location of the measures themselves following a power law. This enables us to construct a power law global error model (PLGEM) that is applicable to various Affymetrix GeneChip data sets. A new DEG identification method is therefore proposed, consisting of a statistic designed to make explicit use of model-derived measurement spread estimates and a resampling-based hypothesis testing algorithm.The new method provides a control of the false positive rate, a good sensitivity vs. specificity trade-off and consistent results with varying number of replicates and even using single samples. DNA microarrays have become common tools for monitoring genome-wide expression in biological samples harvested under different physiological, pathological or pharmacological conditions. One of the most challenging problems in microarray data analysis is probably the identification of differentially expressed genes (DEG) when comparing distinct experimental conditions. In spite of its biological relevance, there is still no commonly accepted way to answer this question.An ideal DEG identification method should limit both false positives, i.e. genes wrongly called significant type 1 errors), and false negatives, i.e. genes wrongly called not significant (type 2 errors). To this end, understanding how gene expression values measured in replicated experiments are spread around the true expression level of each gene, would help to distinguish biologically relevant gene expression changes from fluctuations due to different sources of variability that are unrelated to the biological phenomenon under investigation. Measurement error estimates can be obtained in two ways: either by empirically inferring noise from highly replicated data or by deducing noise from a theoretical error model [ errors, et al. [et al. [The most widely used methods for identifying DEG range from purely empirical filtering techniques (e.g. selecting genes that show a fold change higher than a fixed threshold) to more sophisticated statistical tests such as the signal-to-noise ratio described by Golub et al.  or the S [et al. . While eet al. [et al. [et al. [et al. [In the past years various authors have proposed competing error models for microarray data from which discordant implications for the variance versus mean dependence can be deduced. Chen et al.  first pr [et al.  and Li a [et al.  introduc [et al.  proposed [et al.  proposed [et al.  empiricaet al. [Independently from the choice of the error model, another point that remains to be faced is on how to estimate residual error. A discussed by Wright et al.  the possThe aim of this study was to use highly replicated microarray data to empirically determine the true variance versus mean dependence that exists in this type of data. This knowledge enabled the proposal of PLGEM as a simple but powerful error model. We fitted the proposed model on various data sets without pre-filtering the data, deriving an improved test statistics and identifying DEG even in data sets with very little number of replicates.The relationship between measurement variability and average expression values was investigated by means of scatter plots where different measures of spread were displayed against different measures of location. For each gene absolute or relative standard deviation was plotted against the mean expression value in either linear or log-log plots using data from the 16iDC data set :where α and β can be estimated from linear regression coefficients in 1 in a straightforward way:Model parameters α = ec     eqn. 3β = k     eqn. 4et al. [et al. [Instead of performing a simple linear fit through the whole set of points, we preferred to implement a method that could provide improved model robustness by partitioning the data to gain local estimates of spread as in Mutch et al. . Most im [et al.  proposed) value and subdivide the overall expression range into a given number p of partitions containing an equal number of ranked genes.• Rank genes according to their ln values as the x-coordinate and q-th quantile of ln(s) values as the y-coordinate.• Choose a "modeling quantile" q and determine for all the genes contained in each partition a single "modeling point" with median of ln. The reason for not considering p > 500 was that above this number we empirically revealed a poorer modeling quality in terms of correlation coefficients (data not shown), most likely due to the decrease of the number of data points contained in each partition.Thus, for all possible combinations of p and q a slope kth-percentile to obtain a central tendency of standard deviation to be used for improving test statistics (see next section). Another application of this method could be to fit PLGEM at the 5th- and at the 95th-percentile of standard deviation, to consequently find the limits of the corresponding 90% empirical confidence interval of standard deviation.As a straightforward application of this modeling method, PLGEM could be fitted at the 50In order to verify the feasibility of the former application, fitting of PLGEM on real-life data as well as distribution properties of the random variable E were investigated by analyzing the residuals of the model, i.e. differences between observed and expected values:εg computed from the 16iDC data set and its dependency on the rank of mean expression values. Figure α and β was determined. In all of the three independent data sets measurement variability could be accurately modeled through equation 2, with a power coefficient β that was always between 0 and 1 and a random variable E that appeared to be normally distributed with zero-mean and constant standard deviation over the whole range of expression values. Of course, these findings were eventually expected only for q = 0.5, and their occurrence demonstrated a goodness of fit of PLGEM on a series of unrelated real-life data sets.Figure et al. [In order to identify DEG, we implemented the following general algorithm derived from the framework of statistical hypothesis testing, in which we test against the null hypothesis of non-differential expression. First of all, we chose to implement as the test statistic the signal-to-noise ratio (STN) already used by Golub et al. , becauseet al. : and  represent, respectively, the mean of the replicated expression measures of gene g in condition 1 and 2, whereas  and  are the corresponding standard deviations. Instead, we propose to use model-derived standard deviation estimates predicted by PLGEM in equation 2 for the corresponding signal mean, rather than data-derived standard deviation values calculated independently from the few data points that are usually available for every single gene.where in the original version a priori knowledge on the truly differentially expressed transcripts. ROC plots investigate the relationship between false positive rates (FPR) and false negative rates (FNR) at different significance levels; in this way the performance of the PLGEM-derived STN statistic (PLGEM-STN) has been compared with the original STN statistic (CLASSIC-STN) and the statistic implemented in the commonly accepted Significance Analysis of Microarrays (SAM) DEG identification method (SAM-STAT). To this purpose Exp01 of the Latin Square was taken as the baseline to which the remaining 13 experiments were compared. For each comparison absolute values of each statistic were ranked in decreasing order and first n genes selected (where n ranged from 5 to 200). Figure n (data not shown). In addition, the ROC curve of PLGEM-STN always had the shortest distance from origin, indicating that it resulted in the best trade-off between sensitivity and specificity. Interestingly, improved sensitivity was observed especially when the nominal fold change was particularly low (see Exp02 vs. Exp01 and Exp14 vs. Exp01).The improvement of the test statistic in ranking DEG was evaluated as done by Broberg  through Apart from discriminating between significant and not significant gene expression changes, an optimal test-statistic should additionally provide an accurate quantification of the actual degree of differential expression. Figure Though ranking of genes based on the absolute value of their test-statistic has been proven to be an effective method for selecting DEG, an even more useful way would be to compare the observed statistic with its null distribution , in order to control the FPR.A classic approach to empirically obtain the null distribution of a test-statistic is running a series of random permutations of the chip indexes of the full data set and re-computing the test-statistics at each permutation. Permutated test-statistics can then be pooled and significance thresholds  are found as specific quantiles of the null distribution.Nevertheless, we can foresee that the classic permutation strategy may not be optimal for estimating the actual FPR when the test-statistic makes use of a global error model such as PLGEM. We can in fact hypothesize that measurement spread of DEG may not be accurately described by means of a global error model that was designed to describe signal variability in absence of differential expression. To test this hypothesis we compared the correlation between the expected significance level and the observed FPR using PLGEM-STN and the classic permutation strategy either including or excluding DEG during the permutation step. To this end, data sets containing different percentages of DEG were obtained by merging the 62 known DEG of the Latin Square data set with differently sized random samples of not DEG extracted from the same data set. As predicted, the presence of DEG during the permutation step caused the significance level to be less correlated with the observed FPR and this correlation worsened with increasing percentages of DEG Table . This la1 replicates of condition A with n2 replicates of condition B:Since in real-life data sets true DEG are unknown in advance, we propose the following resampling-based method to obtain the null distribution of not DEG when comparing n1 indexes corresponding to the replicates of only one experimental condition. If available, chose the condition with the highest number of replicates;• Artificial condition A* is obtained by randomly sampling with replacement n2 values from the same set to obtain indexes of artificial condition B*;• Similarly sample n• Compute resampled test-statistics between A* and B* at each cycle.The previous resampling should be repeated a sufficiently large number of times – as large as possible compared to the total number of possible combinations and compatibly with available computational resources – and the resampled test-statistics finally pooled. In our opinion resampling the expression values from only one experimental condition, rather than permutating indexes of both conditions, makes more sense with this particular statistic, because in this way we avoid merging true and false null hypothesis. Note that when more than one condition  are to be compared to a common baseline, the distribution of resampled test-statistics needs to be determined only once, obviously providing a computational advantage. As a test of substantial equivalence between this resampling method and the classic permutation strategy (excluding DEG), we compared the distribution of the permutated and of the resampled PLGEM-STN test-statistics in Q-Q plots. The distribution of the PLGEM-STN resampled from Exp01 of the Latin Square data set was almost identical with the distributions of permutated PLGEM-STN obtained with the classic strategy from each comparison with the remaining 13 experimental conditions (data not shown). Figure In accordance with the previous observations, the ROC curve of the resampling method applied to the PLGEM-STN statistic was not significantly different from the ROC curve of the classic permutation strategy (excluding DEG) applied to the same statistic on the Latin Square data set (data not shown). Conversely, ROC curves of the classic permutation strategy (including DEG) applied to the CLASSIC-STN statistic and of the SAM method gave poorer performance similarly to the results in Figure Another appealing feature of an optimal DEG identification method is that it should provide consistent results when different replicates of a same data set or different numbers thereof are analyzed. We therefore compared the performance of our resampling approach applied to the PLGEM-STN statistic (method 1) with SAM (method 2) and with the classic permutation strategy applied to the CLASSIC-STN statistic (method 3). The number of available replicates for each experimental condition in the Latin Square data set was unfortunately too small to investigate this particular task. We therefore took advantage of the 16iDC+LPS data set, where the first sixteen columns can be considered as the baseline condition for the remaining four experimental replicates. We then constructed a series of reduced data sets in which the baseline columns were kept constant while all possible combinations of 1, 2 or 3 replicates of LPS-stimulated DC were systematically deleted from the 16iDC+LPS data set, reaching a total of fifteen distinct data sets including the original one. Since methods 2 and 3 are not applicable on the four reduced data sets containing single samples for the LPS experimental condition, only the eleven data sets with at least two replicates were used for comparison purposes. Since the sixteen baseline columns are identical in each reduced data set, PLGEM parameters were determined only once on this common baseline condition. Significance levels used by each method in all eleven data sets were empirically selected in order to achieve a similar number of significant genes (ca. 500 probe sets) in the full data set, i.e. the one containing all available replicates. Thus, for each method eleven lists of identified DEG were obtained and the consistency between these lists was evaluated by counting the number of times each probe set was selected, giving a probes set count between 1 and 11. In Figure We finally evaluated the possibility of applying our method also to data sets where one of the experimental conditions was investigated only with a single sample without replication. To this end, we used the remaining four reduced data sets that could not be used in the previous comparison. In this case, the same PLGEM parameters derived from the sixteen baseline columns were applied to each of the single LPS-treated DC sample to obtain an estimate of standard deviation associated to each gene expression value, treated here as if it was a mean value from a larger group of values. Interestingly, when results obtained through this procedure were compared to the previously described results a comparable number of DEG was identified and only one probe set was newly detected in comparison to the previously identified ones (data not shown), arguing for a good consistency of results.In the present work we described a new global error model for microarray gene expression data that describes measurement variability with the same degree of accuracy over the whole dynamic range of values and that can be fitted at any desired quantile of spread. PLGEM has proven to correctly model signal standard deviation, in spite of the presence of different sources of variability, e.g. biological variability as well as the use of different target preparation protocols or of different chips. Moreover, PLGEM has shown to be able to deal with the great variability that exists at low expression levels while at the same time considering the significant relative reproducibility of highly expressed genes. Previously proposed error models assumed that measurement spread depended on signal location following different mathematical relationships, but none of them was based on a power law thus far. Analysis of the residuals showed a good fit of PLGEM to a number of high-density oligonucleotide microarray data sets, with model parameters being very similar to each other even when dealing with RNA samples coming from completely different biological sources and analyzed on different array layouts. This suggests that PLGEM could represent a general Affymetrix GeneChip measurement noise model. Even though scaled MAS5 Signals gave satisfactory modeling results, a further improvement could be achieved by using other emerging gene expression indices ,14 or moβ was found to be quite stable and comprised between 0 and 1 in all analyzed data sets. It is noteworthy that for β ∈ (0:1) absolute variability increases with growing expression values, while relative variability decreases  values increases, thus leading to an underestimation (or overestimation) of the standard deviation and a consequent overestimation (or underestimation) of the test statistic, finally leading to false positives .Interestingly, when the performance of our method was compared on a data set of DC stimulated for 24 hours with LPS, SAM showed a decreased sensitivity in identifying down-regulated genes when the number of LPS replicates was low (data not shown). Under these experimental conditions DC undergo a process known as maturation, which is a specialized form of cellular differentiation, for which both up- and down-regulation of gene expression is expected ,18. We sThe gene selection method proposed in the present work does not provide a direct control on the FDR, but the significance level has been proven to be a direct estimate of the FPR. Thus, if a significance level of 0.001 is used and 12488 probe sets are displayed on the MG-U74Av2 chip, 12–13 genes are expected to be selected by chance in cases where all genes are in fact not differentially expressed. Therefore, a researcher can test how many genes would be selected over a range of different significance levels and chose the one that results in the most acceptable compromise between number of selected genes and estimated FPR.The proposed DEG identification method provides a direct control of the FPR and an indirect control of the FDR. Moreover, as tested on the Latin Square data set, our method improved the specificity vs. sensitivity trade-off in comparison to other commonly applied DEG selection techniques. It finally showed an increased robustness when different replicates or numbers thereof are analyzed, giving consistent results even in data sets containing single samples. In conclusion, the global error model presented here may facilitate the analysis of microarray gene expression data by discriminating information from noise, and thus possibly helping the formulation of new hypothesis concerning gene functions.RNA was harvested from ten biological samples of unstimulated immature mouse dendritic cells (DC), each extracted from an independent batch of cells. One operator prepared the biotin-labeled cRNA for hybridization from three of the ten RNA samples, a second operator prepared the remaining seven. While operator 1 applied the total RNA protocol to all of its three samples, operator 2 applied the purified mRNA protocol to five of its seven samples and the total RNA protocol to the remaining two. Two of the three cRNA samples prepared by operator 1 and four of the seven cRNA samples prepared by operator 2 have been hybridized twice; therefore, a total of 16 MG-U74Av2 GeneChips (Affymetrix, Santa Clara, CA) have been employed.Eight RNA samples were harvested from human fibroblast cell lines each deriving from a distinct Leigh syndrome patient ,20 and iFour individual and two pooled RNA samples from human muscle biopsies of sixteen healthy young male donors were hybridized on six HG-U95Av2 GeneChips (Affymetrix). This data set was downloaded from , experimThis data set consists of 3 technical replicates of 14 separate hybridizations (named Exp01–14) of 42 spiked transcripts in a complex human background at concentrations ranging from 0.125 pM to 512 pM. Thirty of the spikes are isolated from a human cell line, four spikes are bacterial controls, and eight spikes are artificially engineered sequences believed to be unique in the human genome. Further details on the design of the Latin Square data set can be found at . ConsideThis data set consists of the same samples of the 16iDC data set, but includes additional four samples as a second experimental condition. To this end dendritic cells were stimulated to mature with lipopolysaccharide (LPS) for 24 hours. Two independent biological samples were harvested and individually processed by the same two operators that prepared the samples for the 16iDC data set: one applied the total RNA protocol, the other one applied the purified mRNA protocol. Each cRNA sample was hybridized twice, thus using a total of four Affymetrix MG-U74Av2 chips.All chips mentioned in the present study were hybridized and scanned following Affymetrix recommendations and MicroArray Suite 5.0 (MAS5) was used as the image acquisition and analysis software. All data sets used passed quality control tests and probe set signals were scaled so that the 4%-trimmed mean of all expression values of each chip was equal to a predefined reference intensity  following manufacturer's recommendations:TGT = 100 for MG-U74Av2 and HG-U133A chips and TGT = 500 for HG-U95Av2 chips.All procedures for fitting PLGEM, for calculating observed PLGEM-based signal-to-noise ratios (STN), for obtaining expected PLGEM-STN through the resampling-based approach and for comparing observed with expected STN values have been implemented as R functions  and willNP conceived the study and drafted the manuscript. MP wrote the software, participated in the design of the study and in the editing of the manuscript. CV performed the microarray experiments, participated in the design of the study and the editing of the manuscript. MC participated in the microarray experiments, AS participated in the design of the algorithms, FG and PRC coordinated the study. All authors read and approved the final manuscript.Performance of modeling method using different combinations of parameters p and q. The modeling method described in this study was tested on the 16iDC data set using different combinations of partitions (5, 10, 20, 50, 100, 200 and 500), and quantiles (0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95, 0.98 and 0.99). For all 77 analyzed combinations of p and q regression lines were fitted to the data as described in the text. Goodness of fit was evaluated from the resulting slope (panel A), intercept (panel B) and adjusted r2 (panel C).Click here for file


Drosophila larvae. Following puncture wounding, larvae begin to bleed but within an hour a plug forms in the wound gap. Over the next couple of hours the outer part of the plug melanizes to form a scab, and epidermal cells surrounding the plug orient toward it and then fuse to form a syncytium. Subsequently, more-peripheral cells orient toward and fuse with the central syncytium. During this time, the Jun N-terminal kinase (JNK) pathway is activated in a gradient emanating out from the wound, and the epidermal cells spread along or through the wound plug to reestablish a continuous epithelium and its basal lamina and apical cuticle lining. Inactivation of the JNK pathway inhibits epidermal spreading and reepithelialization but does not affect scab formation or other wound healing responses. Conversely, mutations that block scab formation, and a scabless wounding procedure, provide evidence that the scab stabilizes the wound site but is not required to initiate other wound responses. However, in the absence of a scab, the JNK pathway is hyperinduced, reepithelialization initiates but is not always completed, and a chronic wound ensues. The results demonstrate that the cellular responses of wound healing are under separate genetic control, and that the responses are coordinated by multiple signals emanating from the wound site, including a negative feedback signal between scab formation and the JNK pathway. Cell biological and molecular parallels to vertebrate wound healing lead us to speculate that wound healing is an ancient response that has diversified during evolution.To establish a genetic system to study postembryonic wound healing, we characterized epidermal wound healing in  A powerful new system for studying wound healing in the fruitfly is helping to unearth the genetic and cellular requirements of the healing process The capacity to heal wounds is essential for organisms to endure and thrive despite an occasionally hostile environment. Organisms throughout the animal kingdom can heal wounds, but mammalian wound healing has been studied most intensively because of its medical relevance. Wound healing must occur to restore health after trauma or surgery, or in conditions such as cancer or peptic ulcers in which internal processes cause tissue damage. Mammalian epithelial tissues display a characteristic set of responses to tissue damage, including the rapid formation of a blood clot at the site of injury, followed by spreading of the damaged epithelium across the wound gap to restore tissue integrity . HoweverCellular studies of mammalian wound healing have shown that it is a complex process that takes weeks to complete and involves not just the damaged epithelial cells and their neighbors, but also fibroblasts and blood vessels in the underlying stroma, and inflammatory cells that are recruited to the wound site . Only thAlthough many different cell types are present at the wound site, and dozens of signaling molecules, receptors, matrix proteins, and proteases are known to be expressed during the healing process , their rRhodnius prolixus has a robust wound healing response  al cells A. To ensal cells B–1G, andal cells N by visual cells A.Unwounded larvae have a semitransparent white cuticular surface with rare or no blemishes see H, and 1MOver the next 24 h, the outer part of the plug is converted into a scab. This part of the plug becomes electron dense J and 2L By 2 or 3 d after wounding, debris is cleared, the scab resolves, and the exterior of the animal resumes a nearly normal appearance see F and 1L.The response of epidermal cells to wounding was examined in transgenic larvae in which epidermal cell nuclei were labeled with green fluorescent protein (GFP) and cell membranes were immunostained for the basolateral membrane marker Fasciclin III or the septate junction protein Coracle . EpidermAs healing progressed, the polarization and fusion of epidermal cells spread outward from the wound. As cells bordering the wound fused, the more-peripheral cells just beyond the syncytium began to polarize toward the wound see D. Some oA key step in wound healing is the closure of the epidermal gap and reestablishment of epithelial integrity. By 2 h after wounding, the epidermis was still discontinuous but the breach was filled by the plug and developing scab see J. Ultras. Basket phosphorylates the Drosophila Jun and Drosophila Fos transcription factors (puckered (puc), which encodes a phosphatase that negatively regulates Basket, and other targets , two genes induced by JNK pathway activation in other contexts  that leaves a much larger gap in the epidermis than does a fine puncture wound and hence provides a more rigorous test of wound reepithelialization . In contlozenge (lz) encodes a transcription factor required for development of the crystal cell lineage  (data not shown), a mutation that alters crystal cell morphology and eliminates serum phenoloxidase activity , these results support a model in which crystal cells promote scab formation by supplying serum factors such as prophenoloxidase that are necessary to form or consolidate the scab.Our results identify two important requirements for maturation of the plug and scab formation. One is crystal cells. The mutations The other critical requirement for scab formation is a breach spanning both the epidermis and cuticle. In both puncture and pinch wounds, the epidermal layer is disrupted, but only puncture wounds formed scabs. The most obvious difference between the two types of wounds is that the cuticle layer remains intact after pinch wounding. This leads us to propose that scab formation is initiated by a signal generated or liberated by cuticle rupture, or by contact between serum and ruptured cuticle or air. One consequence of this would be local activation of prophenoloxidase by serine proteases that are present as inactive zymogens in insect cuticle .Drosophila has an open circulatory system, so any rupture of both epidermis and cuticle will lead to blood loss. r15lz mutants did not form scabs and survived poorly after puncture wounding; the few surviving larvae appeared flaccid, suggesting continued blood loss from the wound. Although the wound plug likely provides a temporary stop to bleeding, scab formation appears necessary to form a stable hemostatic barrier. Second, the scab likely serves an immune function, which may also enhance survival upon puncture wounding. The orthoquinone precursors of melanin are cytotoxic to microorganisms . Once reepithelialization is completed, debris is cleared, and the scab is sloughed or degraded, it is difficult to discern the old wound site by light microscopy. However, healing is not scar-free; the syncytium formed during healing persists and marks the wound site at least until metamorphosis begins. Occasionally, such syncytia are also seen in untreated larvae; these may be scars of naturally occurring wounds suffered earlier in larval life.The results suggest that there are multiple signals induced by wounding that control and coordinate the different events of larval wound healing: a signal that initiates formation of the wound plug and scab, one that orients surrounding epidermal cells and induces them to fuse, one that activates the JNK pathway and epidermal cell migration, and one dependent on scab formation that suppresses the JNK pathway. There may also be signals that attract plasmatocytes to combat infection and tracheal branches to increase wound oxygenation .These signals have distinct properties. One obvious difference is their range of activation around the wound. The signal that triggers plug and scab formation does so only at the epidermal and cuticular breach, whereas the JNK pathway activator influences cells up to seven cell diameters away. Some signals influence only the damaged cells and their neighbors, whereas others like the putative plasmatocyte and tracheal attractants must reach circulating cells and other tissues.Some of the signals are likely to be diffusible molecules released by damaged cells. These could be intracellular components such as uric acid, histones, or heat shock proteins, all of which have been shown to be released by necrotic mammalian cells and are implicated as intercellular signals . They coNot all signals need be freely diffusible. Surface-bound signals could be sequentially propagated from one cell to the next, and some signals might be mechanical rather than chemical. Wounding appears to alter the tensile properties of the epidermis around the wound site see H, which It is not obvious how many distinct signals are generated by wounding, because individual signals might regulate multiple responses. A high priority now is to molecularly identify the signals and the mechanisms by which they control and coordinate the wound healing responses.Drosophila and mammalian epidermis, embryonic wound healing appears similar to that in mammalian embryos, where it is also a rapid process involving actin cable formation but no apparent hemostatic or inflammatory response 06946 A251.1 06946  and the 3)A251.1 ; both arA58-Gal4 driver expresses the yeast Gal4 transcription factor throughout the larval epidermis beginning in L1 ; e22c-Gal4  were used. To express Puckered, 1118; UAS-puc/ msn-lacZ, A58-Gal4w larvae were used. Larvae of the same genotypes but lacking A58-Gal4 served as controls.The Gal4/UAS system  was used22c-Gal4  and 69B-69B-Gal4  express FP-actin  was driv-GFP.nls  expresseAS-bskDN , UAS-puc at the dorsal midline between the hair stripes of abdominal segment A3 or A4. Typically, the needle pierced through the larva but only the entry wound was analyzed. After wounding, larvae were rinsed and returned to fly media in 1-dram vials and cultured at 25 °C. For experiments depicted in Larvae were dissected at 4 °C in EM fixative  and pinned ventral side up on a Sylgard (Dow Corning, Midland, Michigan, United States) surface. A ventral incision along the length of the animal was made with dissecting scissors, and the four corners of the epidermis were stretched with forceps and pinned to the surface. Internal tissues were removed, and the epidermis was fixed an additional 15 min at room temperature and then trimmed to a flat piece of epidermis surrounding the wound. Tissue samples were incubated for 1 h at 4 °C in 1% osmium tetroxide, stained overnight at 4 °C in 0.5% uranyl acetate, dehydrated through a graded series of ethanol concentrations and propylene oxide, and embedded in EMbed 812 (Electron Microscope Sciences, Hatfield, Pennsylvania, United States) with N, N-dimethylbenzylamine, which was polymerized overnight at 55 °C. Transverse sections (75–90 nm) were cut through the wound site with a Leica Ultracut ultramicrotome (Leica, Wetzlar, Germany) and collected on formvar/carbon-coated 75 mesh copper grids and stained for 20 s in supersaturated uranyl acetate:acetone (1:1) followed by 0.2% lead citrate for 3–4 min. Specimens were observed with an 80-kV beam on a JEOL TEM-1230 microscope (JEOL, Peabody, Massachusetts, United States), and images were captured on a Gatan Multiscan 791 digital camera .r15lz mutants, there was no scab to mark the wound site, so r15, FRT18E/Y; msn-lacZ/+lz larvae were used and stained with X-gal  (see below) to locate the wound. Wounded larvae were dissected in phosphate-buffered saline (PBS), fixed in 2% glutaraldehyde for 15 min at room temperature, and stained with X-gal as described below, except the staining solution lacked Triton X-100 and contained 20 mM K4[FeII(CN)6], 20 mM K3[FeIII(CN)6], 2 mM MgCl2, and 0.2% X-gal in PBS. The propylene oxide dehydration steps during TEM sample preparation were omitted to preserve the X-gal reaction product.For TEM analysis of lacZ transgenes were dissected open in PBS, fixed for 15 min at room temperature with cold 2% glutaraldehyde, rinsed with PBS, and then stained at room temperature for 6 h (puc-lacZ) or 2 h (msn-lacZ) in 150 mM NaCl, 10 mM Na2HPO4, 3 mM K4[FeII(CN)6], 3 mM K3[FeIII(CN)6], 1 mM MgCl2, 0.1% Triton X-100, and 0.2% X-gal.For β-galactosidase histochemistry, larvae carrying Drosophila embryos. Secondary antibodies (Jackson Immunoresearch, West Grove, Pennsylvania, United States) were goat anti-mouse IgG-Cy3 (1:1000 dilution) and goat anti-rabbit IgG-FITC (1:300 dilution). Samples were blocked in PHT buffer  for 1 h or more and then incubated overnight at 4 °C with primary antibody diluted in PHT. Samples were washed with fresh PHT at least six times for 1 h at room temperature, incubated overnight at 4 °C with secondary antibody, and washed as before. Samples were mounted in 70% (v/v) glycerol or Vectashield  mounting medium and observed with a Bio-Rad confocal microscope . Cy3 and FITC channels were sequentially excited and captured for each specimen; a Z-series of optical sections was collected and merged to avoid loss of out-of-plane information due to tissue wrinkling.For immunostaining, primary antibodies were anti-Coracle monoclonal antibodies 9C and C61516B  1:500 d (1:50 dihttp://www.ncbi.nlm.nih.gov/) accession numbers for the genes discussed in this paper are bsk (NM_164901), Jra (NM_165739), kay (NM_170427), lz (NM_078544), msn (NM_079940), and puc (NM_079549).The GenBank (


Anetoderma is a benign condition with focal loss of dermal elastic tissue resulting in localized areas of flaccid or herniated saclike skin. Currently, anetoderma is classified as either primary (idiopathic), or secondary anetoderma . Lesions appear on the upper arms, trunk, and thighs.We report a 14-year-old boy, which was noticed to have had multiple, white, non-pruritic areas on the acral sites of upper and lower extremities for two years. In physical examination, the patient had normal mental development. Skin lesions consisted of scattered, white to skin-colored papules, less than 1 cm in diameter, and with central protrusion, with distribution on dorsal part of the index finger, forearms, distal portion of thighs and calves. Lesions were detected neither on the trunk nor the proximal areas of extremities. There are no sensory changes associated with the lesions. Otherwise, his general health was good. He did not have any medication consumption history. Family history was negative. Laboratory examinations were within normal limits. Skin biopsy from one of his lesions was done, that confirmed the diagnosis of anetoderma.In summary, we report a case of anetoderma on unusual sites of the skin. We could not find similar reports of anetoderma developing on distal extremities without involvement of the upper trunk and proximal arms, in the medical literature. The term anetoderma (anetos = slack) refers to a circumscribed area of slack skin associated with a loss of dermal substance on palpation and a loss of elastic tissue on histological examination .In the past, cases of primary anetoderma were divided into the Jadassohn-Pellizari type, in which the lesions are preceded by erythema or urticaria, and the Schweninger-Buzzi type, in which there are no preceding inflammatory lesions. This is now of historical interest only, because in the same patient some lesions may be preceded by inflammation and the others may not, and the prognosis and histology are identical in the two types ,3.Anetoderma is a rare disorder that in the most usual form develop on the trunk, thighs and upper arms, less commonly on the neck and face and rarely elsewhere.The scalp, palms and soles are usually spared. We report a patient with anetoderma whose lesions present on distal extremities consisting of hands and calves.A 14-year-old boy was noticed to have had multiple white, non pruritic area on his distal extremities for two years. The lesions consisted of whitish papules and depressed areas with central protrusion.On clinical examination, the otherwise healthy looking patient's general appearance and mental state had lumbar lordosis, laxity in large joints, tibia vara, high-arched palate, and dental misalignment. Skin lesions consisted of scattered, white to skin-colored papules, less than 1 centimeter in diameter, and with central protrusion, that were distributed on the dorsum of fingers Fig , distal Laboratory examinations consisting of complete blood count, urinalysis, and blood chemistry including erythrocyte sedimentation rate and liver function tests were within normal limits. Antinuclear antibody was negative. The patient had no risk factor for AIDS or syphilis, so we did not request HIV or VDRL test. Hepatitis B surface antigens were not detected. Immunological assays consisting of IgM, IgA, IgG, IgE, C3, and C4 levels were normal. An induration of 0.5 centimeter in diameter was observed after tuberculin testing. Chest x-ray film was normal. The skin biopsy was done. Haematoxylin and eosin stained section showed faintly eosinophilic separated collagen fibers in the upper and mid-dermis Fig . VerhoefAnetoderma, which was first described by Jadassohn in 1892, is characterized by localized areas of loss of substance and elastic tissue with flaccid skin and often leads to a herniation phenomenon  of anetoBorrelia species) in some cases. In the most usual form, crops of round or oval, pink macules 0.5–1 centimeter in diameter develop on trunk, thighs and upper arms, less commonly on the neck and face and rarely elsewhere[This rare disorder occurs mainly in women aged 20–40 years, but is occasionally reported in younger and older patients of both sexes. It is perhaps more frequent in central Europe than elsewhere, which suggests a possible relationship to chronic atrophic acrodermatitis (due to elsewhere. The scaelsewhere. Sometimelsewhere. The leselsewhere. They maelsewhere.Although infrequently reported, anetoderma may occur in families, and patient must be examined for associated systemic abnormalities for thorough assessment of their skin disorders. In familial anetoderma, there were associated ocular, gastrointestinal or orthopedic anomalies in the affected patients or in any other family members, but causes without them have been reported . AlthougPrimary anetoderma can be inherited, but it has also been described in association with prematurity, lupus erythematosus, antiphospholipid syndrome, and with decreased serum levels of alpha-1-antitrypsin ,7,9,10 aThe differential diagnosis of anetoderma includes other focal dermal atrophies and miscellaneous diseases that must be differentiated from the skin herniation phenomenon of anetoderma , are shoAtrophoderma of Pasini and Pierini is a major source of confusion both etymologically and clinically. Patients have larger lesions with a sharp peripheral border dropping into a depression with no outpouching. On biopsy, elastin is normal, while collagen may be thickened, but this finding is difficult to quantify . PerifolElastase-producing strains of staphylococcus epidermidis have been held responsible for perifollicular macular atrophy. Anetoderma has also been reported in 5 patients with false-positive syphilis serology, 3 of who also fulfilled the criteria for the antiphospholipid syndrome . Its patVenencie et al. , suggestGhomrasseni et al. , demonstPenicillamine-induced anetoderma has also been reported ,34 and . PenicilIn summary, we report a case of anetoderma with lesions on unusual sites. We did not find similar reports of acral anetoderma in the medical literature.According to this paper liquid nitrogen cryotherapy has moderate efficacy in the treatment of some of the early anetoderma lesions, without frank atrophy.None declared.The pre-publication history for this paper can be accessed here:


Mycobacterium tuberculosis, especially at the within-host level. The insertion sequence IS6110 is a genetic marker that is widely used to track the transmission of tuberculosis between individuals. This and other markers may also facilitate our understanding of the disease within patients.While the pathogenesis and epidemiology of tuberculosis are well studied, relatively little is known about the evolution of the infectious agent M. tuberculosis within patients. The arguments are based on a comparison between empirical findings from molecular epidemiology, and population genetic models of evolution. Under the hypothesis of neutrality of genotypes, 1) the mutation rate of the marker IS6110 is unusually high, 2) the time it takes for substitutions to occur within patients is too short, and 3) the amount of polymorphism within patients is too low.This article presents three lines of evidence supporting the action of positive selection on Empirical observations are explained by the action of positive selection during infection, or alternatively by very low effective population sizes. I discuss the possible roles of antibiotic treatment, the host immune system and extrapulmonary dissemination in creating opportunities for positive selection. Mycobacterium tuberculosis cells undergo adaptive evolution on the spatial and temporal scales of individual infections? On the one hand, the long generation time and limited sequence diversity of this organism might suggest a slow pace of adaptive evolution. On the other hand, the rapidity and ease with which antibiotic resistance is generated during infection suggests otherwise. The physiology and immunology of tuberculosis pathogenesis have been well studied. The infectious agent M. tuberculosis is known to invade and replicate within alveolar macrophages. There is a spectrum of responses by the immune system, corresponding to the relative involvement of Thl and Th2 immune cells, which respectively stimulate the cytotoxic response (more effective against infected cells), and the humoral/antibody response (more effective against extracellular pathogens) [M. tuberculosis have been carefully studied. Genotypic data are rapidly accumulating in the molecular epidemiology of infectious diseases. These are usually compiled and summarised to make inferences about the state of an epidemic in a given geographic location, or at the global level. For example, epidemiologists seek to identify risk factors for infection, and to locate particular strains that are especially transmissible or pathogenic [M. tuberculosis has also been characterised. For example, it has been argued that the limited variation at the nucleotide is due to a recent population bottleneck [Mycobacterium bovis and M. tuberculosis may well have been a human rather than bovine pathogen [How actively do populations of thogens) ,2. Some thogens) -5. At ththogenic -8. The ettleneck , and thapathogen .M. tuberculosis at the cellular level inside bodies. There has been little integration of the genetic information from markers with the population genetics of the bacterial population within hosts. In this article, I examine data collected for the purposes of molecular epidemiology to present three lines of evidence supporting the action of positive selection on M. tuberculosis. The data come from the marker IS6110, which is currently the standard method of typing tuberculosis isolates. These genotypic data will be considered under assumptions of neutrality, and then under the assumption that positive selection is acting. The case for the action of selection is based on the following three arguments.Less understood is the evolution of 6110 is unusually high; the estimated mutation rate is lower if selection is acting.• Under the assumption of neutrality, the observed mutation (or transposition) rate of the genetic marker IS• The observed times associated with change are too low to be explained by neutrality; positive selection lowers the expected substitution time.• The observed level of polymorphism is too low to be explained by neutrality.s (described in the Appendix). Although the analyses start with strict neutrality (s = 0) in each argument, alleles for which s < 1/N, where N is the effective population size, can be considered nearly neutral, in that the effects of drift outweigh the force of selection [In each of the following sections a comparison is made between the strictly neutral model and a generalised model including selection through a single parameter election . In eachwithin-host substitution process is of interest. Rosenberg et al. [IS6110 marker to be around 0.00184 to 0.0390 events per copy per year, with the maximum likelihood estimate at 0.0287. Under neutrality, therefore, this rate corresponds to a per insertion mutation rate of μi ~ 7.9 × 10-5 events per site per generation, assuming a generation time of 1 day in active infections. This figure comes from a measured doubling time of close to 24 hours, based on clinical isolates grown in human monocyte cultures and in culture media [per nucleotide per generation) are usually in the vicinity of 10-9. In mutator strains, that is, genomes in which the DNA repair machinery is damaged, leading to elevated mutation rates, the mutation rate rises orders of magnitude, up to ~10-7 – 10-6 [6110 under neutrality therefore seems suspiciously high, although this is only "circumstantial evidence", since it is not inherently problematic. Indeed, mutation rates as high as 10-4 per element per generation have been measured for IS10 in vitro [When genetic mutations are selectively neutral, the substitution rate is equal to the mutation rate . In the g et al.  determinre media -15. Rate7 – 10-6 . The mutin vitro . NeverthK = uN μi     (1)u is the probability of fixation of a mutant, μi is the mutation rate and N is the population size [s is  = K/(uN). The diffusion model of drift provides an expression for u as a function of the population size N and selective coefficient s (see the Appendix). Figure  over s for a few different values of N. In each curve the estimated mutation rate decreases as the selective coefficient rises. According to this analysis, lower mutation rates are possible when there is some selection and a large population size, or when selection is strong and the population size is small. Note that the estimated mutation rate remains high if mutations are nearly neutral.where ion size . An esti6110 as a genetic marker by examining genotypes of serial isolates from patients with persistent infection. A small number of changes in the genotypes between serial isolates indicates a stable marker. Differences in genotypes due to exogeneous reinfection by unrelated strains are excluded from consideration. In the data of Niemann et al. [et al. [6110 genotypes from serial samples of M. tuberculosis is 212 days, and the maximum is 683 days. Because the second sample is taken some time after fixation of the mutant, the actual substitution times are unknown, but they were clearly all under 683 days. I will now show that the expected substitution times under strict neutrality are well in excess of this value. Various studies have measured the stability of ISn et al.  and Rose [et al. , the medμNu) = 1/μ since u = 1/N under strict neutrality. The average time for a successful neutral mutant to reach fixation is 4N generations. The mutation rate of interest in this context is the rate per genome per generation, since what is of concern is whether any of the elements in a given genome produce change. For simplicity, assume that the genomic mutation rate scales linearly with copy number.  Considering a typical strain has 10 copies of the IS element, the relevant mutation rate here is μ = μi × 10 = 7.9 × 10-4. Therefore, for N = 10, 103, 105, the expected substitution times are roughly 1300, 5300, 4 × 105 generations, respectively. With the generation time set to one day, the upper bound of observed substitution times was 683 generations, which is well below theoretical expectations.Let us start with the assumption that the expected time for substitution to occur is the average time taken for the successful mutant to appear plus the time taken for that mutant to reach fixation conditional on its eventual fixation. (I will later drop the assumption about waiting for the mutant to appear). The average appearance time is 1/ would be to say that the mutant destined to reach fixation appears exactly at the time of the first sample. We can then ask how long it takes on average for this mutant to reach fixation if it is positively selected. An even more conservative model would be that not only is the successor strain present at the time of the first sample, but is present at a frequency of 30%. Furthermore, let us say the subdominant strain only needs to be at 70% at the time of the second sample to be considered to have replaced the parental strain.a, b) , conditional on fixation, was found by Ewens [A model of the sojourn times of alleles in populations conditional on fixation must now be specified. Again using the diffusion model of drift (see Appendix), the mean time spent by a mutant in the range of frequencies (by Ewens  and by Mby Ewens  to bea, b). Even in the extremely conservative model shown in the right-hand plot, the effective population size must be below 400 in order to explain the observed substitution times under strict neutrality. The data are difficult to account for even in terms of nearly neutral mutations (s < 1/N) and an effective population size of N = 1000. The alternative explanation is that the effective population size is larger, but positive selection is acting to make changes sweep through the population faster.Figure et al. [M. tuberculosis isolates are monomorphic using IS6110 as the marker. They also show that the limits of detection of a second strain are around frequencies of 0.1 to 0.3. More sensitive instruments and refined genotyping procedures are likely to reveal greater polymorphism. The current information can be used, however, to study the population of the organism in hosts by using ranges of detectable polymorphism. In this section, two ranges will be considered in examining predictions from models: first, 0.1 to 0.9, and second, 0.3 to 0.7.Many analyses of pathogen genotypes assume isolated strains to be clonal, that is, to be monomorphic. This assumption has been scrutinised by De Boer et al. , who shoThe polymorphism argument rests on the assumption that the isolates reported in  can be vf(x) be the probability density function of this distribution and F(x) be the cumulative probability function F(x) (see Appendix). The probability that a given population (patient) is between frequencies a and b (where a <b) isWright  found tha, b).This quantity can be alternatively interpreted as the proportion of populations observed to be polymorphic according to the detection limits set by (s = 0), the distribution described by f(x) is a Beta distribution. Figure a, b) = (0.1, 0.9) and (0.3, 0.7), and a mutation rate of μ = 7.9 × 10-4 per cell per generation.First consider the neutral case. When there is no selection  the bacterial populations sampled in [N values of around 100 or lower, which seems grossly at odds with the usually large census population sizes of bacteria. In mouse models of TB infection, for instance, bacterial loads reach around 105 – 107 colony forming units per lung [The three lines of evidence presented in this article suggest positive selection on mpled in  might noper lung ,24. It hper lung ,25.N and s. First, from the plots shown here, it is clear that different combinations of the two parameters can explain the observations. This would make it difficult to locate the best fit. Second, although the model can be used to assess the possibility of neutrality in the current context, it cannot adequately serve as a framework for estimation given the intricacies of host-pathogen interactions. Further, adding more parameters to the model would increase the complexity of the analysis beyond what can be sustained by the resolution of the currently available data.I will also comment on why I have not attempted to statistically fit the model to data to estimate M. tuberculosis within patients are very low due to population structure, background selection, or other factors. If there is indeed detectable adaptive evolution of tuberculosis within patients, what are the sources of selection? Two important candidates are antibiotic treatment and the host immune system. Studies using serial isolates have found no correlation between IS6110 genotype instability and (a) drug resistance/susceptibility of the isolate [change in drug resistance status [M. tuberculosis from the lung lesions of six patients has shown heterogeneity in resistance-associated alleles, but not with respect to IS6110 [Taken together, the results suggest positive selection, although the evidence is not conclusive. A possible alternative is that the effective population sizes of  isolate ,27, (b) e status  or (c) de status . It is so IS6110 .M. tuberculosis within patients, i.e., bottlenecks – which is overcome by survivors with new genotypes. If the observed patterns are to be explained by severe bottlenecks, the surviving cells are not necessarily better adapted to residing in the host than the parental cells that were eliminated by the immune response. It is noteworthy that Yeh et al. [M. tuberculosis are not primarily driven by the immune system. However, the extraordinary ability of M. tuberculosis to manipulate the T cell response [Alternatively, fingerprint changes may reflect (evolutionary) escape from the immune system. The analysis here hints at low effective population sizes – perhaps the immune system induces a heavy decline in population sizes of h et al.  found noresponse  suggestset al. [6110 change and extrapulmonary disease or pulmonary+extrapulmonary disease and extrapulmonary origin of isolates. Dissemination is a major factor in the pathogenesis of tuberculosis. Since the lungs are the preferred environment of the organism, the new environments outside lungs may create opportunities for adaptive evolution. Adaptive evolution leading to specialisation to tissue types is to be expected. A recent article [Streptococcus pyogenes by examining ratios of non-synonymous to synonymous substitution rates (dn/ds).Interestingly, de Boer et al.  found an article , for exa6110 directly responsible for adaptive mutations? On one hand, the apparently strict asexuality of M. tuberculosis implies that all genes good, bad or neutral are tightly linked to each other. It is likely then that IS-induced changes hitchhike to fixation with other mutations that confer advantage to the genome. On the other hand, it has recently been demonstrated that IS6110 carries a promoter that can modify the the expression of neighbouring genes, raising the possibility of a direct role for the element in adaptive evolution [6110 can be not only beneficial, but also neutral or deleterious [Is ISvolution . Note theterious .M. tuberculosis shares with viruses the characteristic of replicating within cells, a major difference is that mutation rates in viruses are much higher, particularly in retroviruses, which depend on reverse transcriptase (a low-fidelity enzyme) to copy their genomes. Hence, the extent of nucleotide variation of M. tuberculosis is not expected to be the same as is commonly observed for example in HIV [dn/ds) has established the action of positive selection on HIV within patients [At the within-patient level, the best studied pathogen is perhaps HIV. While e in HIV . There ie in HIV -35. In apatients ,36.M. tuberculosis, the level of polymorphism at synonymous sites has been noted to be extremely low [dn/ds ratios may provide further insight into the nature of positive selection in M. tuberculosis.In mely low . It woulNe in which there are 2Ne copies of the  gene in question. The diffusion model is used here with minor adjustments to describe bacterial populations. Let the number of bacterial cells in a population be N. Each mutant appears in the population at frequency 1/N. Realistically, population sizes fluctuate and only a subset of cells actively divide. The number N should therefore be considered to be the effective rather than the actual population size, which may be much larger than N.The analyses here rely on the commonly used diffusion model of drift and selection in a population, based on the Wright-Fisher process ,38. It ix of an allele per generation is m(x) = sx(1-x), which is identical to the diploid model with additive fitnesses (no dominance) if each copy of the advantageous allele adds s to the fitness (see [[s and homozygotes have advantage 2s.It can be shown that the deterministic dynamics of selection in a haploid model are well approximated by a logistic model. The mean change in frequency ss (see [, p. 192]v(x), the variance in change of allele frequency per generation, can also be taken from diploid theory. Replacing the diploid model of the random union of gametes with choosing cells randomly from each generation to the next, the effective population size is adjusted according to the distribution of offspring number under a given model of cellular division.The variance component p0 = 1/4, p1 = 1/2, p2 = 1/4, where pi is the probability of producing i offspring. The variance in offspring number here is 1/2 and the variance-effective population size equals N/ = 2N. Thus in this case, the diploid theory can be directly used as far as v(x) is concerned (replacing 2Ne with 2N). Johnson and Gerrish [There are numerous ways to model drift in populations of organisms that reproduce by dividing to produce two daughters . Here, c Gerrish  considerp in a randomly mating diploid population of size Ne (with 2Ne copies of the gene in question) isDiffusion models of genetic drift have shown  that thep = 1/N rather than the usual p = l/(2Ne) the probability of fixation of a mutant bacterial cell with selective advantage s isTherefore, using Ns >> 1, u ~ 4s. This agrees with a result of Gerrish and Lenski [u for alternative models.Note that when 4d Lenski , using ad Lenski  for discμ). As stated above, selection is additive. As shown by Wright [Let the mutation rates from any genotype to any other be equal (y Wright , the steSee also ,38 for f


Drosophila, in which the female carries two X chromosomes, while the male carries only one.Because males and females possess different numbers of the two sex chromosomes , the potential “dose” of each gene differs. Without some compensating mechanism, female mammals would express twice the quantity of an X-linked gene as males. The same holds true in the fruitfly Drosophila takes the opposite tack, doubling the output from the single male X chromosome. It does so through the creation of “compensasomes,” protein–RNA complexes that bind to the X chromosome and boost gene transcription. One model of compensasome activity has posited a two-step mechanism, in which the complexes form only at 35–40 specific “entry sites” along the X, and then spread out to the surrounding regions. In this issue, Delphine Fagegaltier and Bruce Baker test this model and show that its predictions do not match experimental results.In mammals, dosage compensation is achieved by silencing one of the X's in the female. roX1 and roX2. Fagegaltier and Baker reasoned that, according to the entry-site model, if a piece of the X not containing one of the entry sites was transposed to an autosome (non-sex chromosome), it should be unable to recruit MSLs and therefore be unable to form compensasomes. To test this prediction, they used autosomes into which various pieces of the X had been transposed. Contrary to prediction, they found that even the smallest pieces could recruit MSLs, whether or not they contained entry sites. Furthermore, the pattern of MSL binding was exactly the same as if the fragment of the X was still on its native chromosome, suggesting that each of the hundreds of sites at which compensasomes are found function autonomously to recruit them.The compensasome complex includes half a dozen proteins collectively known as MSLs , along with two pieces of RNA, Another prediction of the entry-site model is that compensasomes should spread out from the entry site, along the chromosome. And here again, the model does not hold up—Fagegaltier and Baker found that even when entry sites from the X chromosome are put close to an autosomal region, compensasomes never spread from the X onto these regions. These results suggest that spreading is not an innate function of the compensasome, and further strengthens the case for autonomous recruitment all along the X. In place of the two-step “entry site plus spreading” model, the authors propose a model based on differential affinity for compensasome components. They suggest that the 35–40 “entry sites” are simply high-affinity sites that recruit MSLs first, based on intrinsic differences that allow them to bind and hold MSLs more strongly than other sites. Once these sites are occupied, additional compensasome components can bind to lower-affinity sites. This mechanism can account for observed compensasome activity without the restriction to a limited number of entry sites and the requirement for spreading.roX transgenes. They propose that the additional binding observed specifically around roX transgenes results from a mass action of compensasomes, as roX transgenes would act as assembly sites for compensasomes, just as ribosomal RNA genes do for ribosomes. Once formed, compensasomes may bind locally to other neighboring sites.Fagegaltier and Baker note that while compensasome spreading does not normally occur during dosage compensation on the X chromosome, it has nonetheless been documented for some While the details of dosage compensation and the dosage compensation complexes now clearly differ between mammals and flies, there are broad similarities, including the widespread modification of chromatin structure and the use of RNA components in the compensation machinery. A deeper understanding of the process in flies may help shed light on the details of compensation in other organisms as well.


Systems Biology Markup Language (SBML) is gaining broad usage as a standard for representing dynamical systems as data structures. The open source statistical programming environment R is widely used by biostatisticians involved in microarray analyses. An interface between SBML and R does not exist, though one might be useful to R users interested in SBML, and SBML users interested in R.A model structure that parallels SBML to a limited degree is defined in R. An interface between this structure and SBML is provided through two function definitions: write.SBML which maps this R model structure to SBML level 2, and read.SBML which maps a limited range of SBML level 2 files back to R. A published model of purine metabolism is provided in this SBML-like format and used to test the interface. The model reproduces published time course responses before and after its mapping through SBML.List infrastructure preexisting in R makes it well-suited for manipulating SBML models. Further developments of this SBML-R interface seem to be warranted. Systems biology markup language (SBML) is a standard for representing dynamical systems of biological interest ,2. InterThe software exists completely in R. It is comprised of four functions and is currently being distributed as a developmental package called "SBMLR" through Bioconductor . The sofn statements, the first n-1 trivially convert input vector components into variables with the same names. The nth statement then contains the complete reaction rate law. It can occupy multiple lines, but it must be a single statement, i.e. it cannot depend on substitution variables temporarily defined in preceding statements.To facilitate mappings between SBML and R, an SBML-like list structure is defined in this subsection using the purine metabolism model of Curto et al.  as a speModel definition codes such as that given in Figure t = 0 and plots the responses of inosine monophosphate (IMP) and hypoxanthine (HX) as shown in Figure i loop over the rows (i.e. state variables) and a j loop over the columns (i.e. reactions). If a state is a product of a reaction, the corresponding matrix element becomes a positive integer equal to its stoichiometry [factor converts string names to factors so that summary can count them], and similarly for reactants, though with negative numbers entering the matrix in this case .This code simulates the response to a 10-fold increase in phosphoribosylpyrophosphate (PRPP) at time The function fderiv creates the current species vector by overriding initial states with current states clipped to positive values, and by overriding any time varying boundary conditions defined by rules (SBML rules are not needed for the purine model, but are needed to implement other models ). The fuTwo functions comprise the SBML-R interface: write.SBML converts SBML-like R models (e.g. Curto.r) into SBML models (e.g. Curto.xml), and read.SBML converts SBML models (e.g. Curto.xml) into an SBML-like R model (e.g. CurtoX.r). A key component of these two interface functions is a locally defined recursive function named recurs. This function converts arbitrary R expressions into arbitrary MathML expressions, and vice-versa; it is defined differently, locally, in each of the two functions. In write.SBML, shown in Figure The second of the two SBML-R interface functions, read.SBML, maps a limited range of SBML level 2 files (function definitions and events are not handled) into SBML-like R model files. Portions of read.SBML are given in Figure The function write.SBML was applied to Curto.r to generate Curto.xml and the function read.SBML was then applied to Curto.xml to generate CurtoX.r. Execution of the script given in Figure If the model of Curto et al.  were impAs SBML evolves to handle a broader range of dynamical systems, it will become more and more challenging for simulation packages to handle all possible SBML models. It is envisioned here that the development of this SBML-R interface will be driven by its users, and not by the model representation capabilities of SBML, i.e. it is expected that the users of this interface will be programmers who are capable of modifying it as their needs require.Compared to Matlab, which may be better equipped than R to simulate arbitrarily complex dynamical systems, R has the advantage of list handling infrastructure in parse and xmlTreeParse, and it also has the advantage of indexing by names instead of numbers. A further advantage, though not exploited here, is that R is object-oriented; in future versions of this interface, a print method might be defined for objects of class SBMLR (i.e. models) to generate more readable renderings of models in R. Another advantage of R over Matlab is that it provides access to a much broader collection of microarray analysis tools, e.g. see Bioconductor . This asProject name: SBMLRProject home page: Operating system(s): Windows XPProgramming language: R 2.0Other requirements: R packages: XML and ODESOLVELicense: GNU GPLAny restrictions to use by non-academics: no restrictionsSBML = Systems Biology Markup Language; XML = extensible markup language; MathML = Mathematical Markup Language; ODE = ordinary differential equation.TR is the sole contributor.packages and install from local zip. The XML package installs similarly [packages and install from CRAN.The SBMLR package is available through Bioconductor as a developmental package . It has imilarly . Note thThe implementation of Curto et al.'s model shown in Figure


Antibiotic overuse and misuse for upper respiratory tract infections in children is widespread and fuelled by public attitudes and expectations. This study assessed knowledge, beliefs, and practices regarding antibiotic use for these paediatric infections among children's caregivers' in Trinidad and Tobago in the English speaking Caribbean.In a cross-sectional observational study, by random survey children's adult caregivers gave a telephone interview from November 1998 to January 1999. On a pilot-tested evaluation instrument, respondents provided information about their knowledge and beliefs of antibiotics, and their use of these agents to treat recent episodes (< previous 30 days) of upper respiratory tract infections in children under their care. Caregivers were scored on an antibiotic knowledge test and divided based on their score. Differences between those with high and low scores were compared using the chi-square test.Of the 417 caregivers, 70% were female and between 18–40 years, 77% were educated to high school and beyond and 43% lived in urban areas. Two hundred and forty nine (60%) respondents scored high (≥ 12) on antibiotic knowledge and 149 (34%) had used antibiotics in the preceding year. More caregivers with a high knowledge score had private health insurance (33%), (p < 0.02), high school education (57%) (p < 0.002), and had used antibiotics in the preceding year (p < 0.008) and within the last 30 days (p < 0.05). Caregivers with high scores were less likely to demand antibiotics (p < 0.05) or keep them at home (p < 0.001), but more likely to self-treat with antibiotics (p < 0.001). Caregivers administered antibiotics in 241/288 (84%) self-assessed severe episodes of infection (p < 0.001) and in 59/126 (43%) cough and cold episodes without visiting a health clinic or private physician (p < 0.05).In Trinidad and Tobago, caregivers scoring low on antibiotic knowledge have erroneous beliefs and use antibiotics inappropriately. Children in their care receive antibiotics for upper respiratory tract infections without visiting a health clinic or a physician. Educational interventions in the community on the consequences of inappropriate antibiotic use in children are recommended. Our findings emphasise the need to address information, training, legislation and education at all levels of the drug delivery system towards discouraging self-medication with antibiotics in children. Acute respiratory infections and diarrhoeal diseases are the leading causes of childhood mortality, resulting in 25–33% of all deaths in children in developing countries. Worldwide, antibiotics are the most commonly prescribed and abused drugs for upper respiratory tract infections (URTIs) ,2. In suStreptococcus pneumoniae, Hemophilus influenzae and Moraxella catarrhalis, the most common bacterial pathogens causing acute bacterial rhinosinusitis (ABRS) in children, in both the developed and developing countries, have already demonstrated resistance to the first line antibiotics [Streptococcus pneumoniae and the increasing prevalence of strains resistant to the beta-lactams and co-trimoxazole. Unnecessary antibiotic use in viral respiratory illnesses in humans is a key factor influencing the emergence and spread of resistant pneumococci. Inappropriate antibiotic use may be consequent to misdiagnosis of the illness , patients' expectations, and their demands which induce physicians to prescribe antibiotics [A significant force driving the occurrence and the spread of antibiotic resistance is the inappropriate use of antibiotics in primary and ambulatory care settings. ibiotics -9. The Sibiotics  which isibiotics .In the United States, a higher proportion of infections due to penicillin resistant pnuemococci among young children and whites have been attributed to an overuse of antibiotics ,13. EpidPrevious community-oriented studies suggest an irrational use of antibiotics, particularly in the developing and lesser-developed countries. Factors influencing antibiotic resistance are the higher incidence of infectious diseases in children, the lack of access to health care, costs and poor regulatory controls on the use of prescription drugs such as antibiotics, coupled with low antibiotic knowledge prompting increased self medication with these drugs -17. As tTrinidad and Tobago, a twin island republic in the Caribbean located off the Venezuelan coast with a population of 1.3 million people is the second largest country in the English speaking Caribbean. About 74% of the country's population live in the urban areas and 60% of the population is aged between 15–64 years. The literacy rate in Trinidad and Tobago is 98% and at least 70% of the country's adult population has completed secondary (high) school to the pre-university level. Medical care in Trinidad and Tobago is publicly financed through three (3) regional health authorities (North West, South West and Eastern regions) in Trinidad, and one in Tobago. Secondary and tertiary care are provided at one general hospital in Port-of-Spain, one in San Fernando (1,245 beds), and at two county hospitals in Trinidad (111 beds), and at one hospital in Tobago (96 beds), besides institutions for specialised services. Primary health care is provided at 82 health centers in Trinidad and 19 in Tobago. The ratio of the population to a health center ranges from < 3000 per center in Tobago to > 21,000 per center in the north-west region. There are 33 private hospitals and approximately 45% of the population preferentially uses the private sector services. Private general practitioners are concentrated in the cities and larger towns and the estimated ratio of physician per population is 7.5 per 10, 000 inhabitants ."Antibiotics are drugs that are prescribed for the treatment of diseases caused by germs".This prospective cross sectional observational study was conducted in Trinidad and Tobago from November 1998 to January 1999 in randomly selected subjects interviewed over the telephone. The study design and methods have been described previously [The pilot-tested questionnaire consisted of 42 items in three parts, designed to investigate knowledge, beliefs and practices of antibiotics . The firFour hundred and seventeen of the 753 respondents were adult caregivers with children in the family and their responses were analysed. An antibiotic knowledge score was created based on the caregivers' responses to eight common drugs, which included four antibiotics. A high Antibiotic Knowledge Score (AKS) was defined as that at or above the median score. Data were analysed using SPSS version 11.0 (Chicago), and associations were determined by the Chi square test for antibiotic knowledge and caregiver's education, beliefs and practices and recent and past antibiotic use for URTIs.The majority of respondents was female (70%), ≥ 31 years, 72%) and had completed high school (pre-university) education (77%). There was a marginally high (57%) representation from the rural area  and 'antibiotics are free from side effects' (61% 253/417]). Few (11% [49/417]) respondents believed antibiotics are generally safe. and some caregivers (18%–24%) remained non-responsive to all questions. The AKS of caregivers did not influence their beliefs (Table /417). FeCaregivers reported as many as 450 episodes of URTIs in children within the previous 30 days (1.07 episodes per family) and 149 (35%) caregivers self-administered antibiotics in 64% (288/450) of these episodes. Cough and cold was the most frequently reported URTI symptom (48%, 214/450) followed by fever (28%,128/450) and sore throat (24%,108 / 450). Caregivers were more likely to give children antibiotics when they perceived URTIs to be severe [241/288 (84%)] (p < 0.001), and administered these drugs for the common cold [112/136, (82%)] fever [75/87 (83%)] and sore throat [54/65 (86%)]  was significantly associated with higher knowledge scores. Similar associations between knowledge and antibiotic use were reported in a study in the Indian state of Kerala . In the A significant proportion of caregivers in the present study had misconceptions that could contribute to the inappropriate use of antibiotics. Equal proportions of caregivers with high and low knowledge scores believed that antibiotics cure all infections and are free from side-effects. Even though URTIs are generally of viral aetiology ,22, thesPractices such as demanding a prescription for antibiotics from a physician, and keeping antibiotics at home (hoarding) were higher in caregivers with a low AKS. In Israel Shlomo et al  found loEarlier in describing the prescribing practices of Caribbean physicians we reported that respiratory tract infection was the most frequent reason for antibiotic prescriptions by physicians in the English and Dutch speaking Caribbean . The infInappropriate antibiotic use is a common practice in the out patient setting in a clinic, or a physician's office ,32, and Inappropriate antibiotic use for paediatric URTIs in Trinidad and Tobago may have been facilitated by low knowledge, erroneous beliefs and easy availability of these drugs without the required prescription, at a retail pharmacy. A combination of education and communication to combat patients' expectations for treatment, and the physicians' appropriate prescription for antibiotics can halt inappropriate antibiotic use in children. Using narrow-spectrum antibiotics, promoting dialogue with caregivers to discuss symptom relief and antibiotic resistance, and encouraging active management of the child's illness with follow-up calls is recommended. Pharmacists have a serious responsibility not to dispense these agents without prescriptions and to discourage patients from obtaining these drugs for self-treatment. Widespread educational campaigns targeting the general public in Trinidad and Tobago, particularly parents and caregivers of young children should focus on the difference in bacterial and viral infections and the futility of treating viral infections with antibiotics. A multidisciplinary approach to rational antibiotic use, dispensing these drugs as 'prescription only medicine' and educating the public can halt inappropriate use and contain resistance.ABRS = Acute Bacterial RhinosinusitisAKS = Antibiotic Knowledge ScoreSAHR = Sinus and Allergy Health PartnershipURTI = Upper Respiratory Tract InfectionThe author(s) declare that they have no competing interests.PP conceptualised the study and drafted the protocol, NP did data collection, and contributed to the draft manuscript, LMPP prepared the final manuscript. All authors contributed to the statistical analysis and the literature search.The pre-publication history for this paper can be accessed here:


Colorectal cancer screening is widely recommended, but often under-utilized. In addition, significant demographic differences in screening utilization exist. Insurance coverage may be one factor influencing utilization of colorectal cancer screening tests.We conducted a retrospective analysis of claims for outpatient services for Washington state Medicare beneficiaries in calendar year 2000. We determined the proportion of beneficiaries utilizing screening fecal occult blood tests, flexible sigmoidoscopy, colonoscopy, or double contrast barium enema in the overall population and various demographic subgroups. Multiple logistic regression analysis was used to determine the relative odds of screening in different demographic groups.Approximately 9.2% of beneficiaries had fecal occult blood tests, 7.2% had any colonoscopy, flexible sigmoidoscopy, or barium enema (invasive) colon tests, and 3.5% had invasive tests for screening indications. Colonoscopy accounted for 41% of all invasive tests for screening indications. Women were more likely to receive fecal occult blood test screening  and less likely to receive invasive tests for screening indications than men (OR 0.80, 95%CI 0.77, 0.83). Whites were more likely than other racial groups to receive any type of screening. Rural residents were more likely than urban residents to have fecal occult blood tests (OR 1.20, 95%CI 1.17, 1.23) but less likely to receive invasive tests for screening indications .Reported use of fecal occult blood testing remains modest. Overall use of the more invasive tests for screening indications remains essentially unchanged, but there has been a shift toward increased use of screening colonoscopy. Significant demographic differences in screening utilization persist despite consistent insurance coverage. Screening for colorectal cancer is now recommended by several organizations -5, and iThe study was approved by the University of Washington Institutional Review Board. We used the calendar year 2000 Physician/Supplier Part B Standard Analytic File and the Denominator File, which are administrative databases covering Medicare beneficiaries and maintained by the Centers for Medicare and Medicaid Services. The Denominator File contains information about date of birth, gender, race, place of residence, vital status, and enrollment in Medicare Part A, Medicare Part B or capitated health plans. The Physician/Supplier Part B Standard Analytic File contains claims data for outpatient physician and supplier services, including the date of the visit, associated diagnoses , and procedures performed .All Medicare beneficiaries listed as Washington State residents in the Denominator File in calendar year 2000 were eligible for inclusion (n = 772,153). Beneficiaries who were less than 65 years old (n = 132,711), who died during the study year (n = 32,678), or who were not enrolled in both parts A and B throughout the study year (n = 33,263) were excluded. We excluded patients enrolled in capitated health plans during any part of the study year (n = 170,232) because they may have received screening tests while in these plans for which claims were not submitted. Based on ICD9 codes in the Physician/Supplier Standard Analytical File, we also excluded patients with a diagnosis code for a personal history of colon polyps (V12.72, n = 683), colon or rectal cancer (V10.05 or V10.06, n = 398), or inflammatory bowel disease (555.x, 556.x, n = 227) in this calendar year, since these patients are at increased risk of colorectal cancer and may need more frequent surveillance. Patients without one of these diagnoses were analyzed as average risk. However, if patients did have a history of one of these conditions, but this code was not listed in calendar year 2000, they could have been misclassified as being at average risk. We did not exclude patients with a family history of colorectal cancer, as we felt they could not be reliably identified from the available ICD9 diagnosis codes. We had 401,961 eligible beneficiaries for analysis.We identified screening fecal occult blood tests using the HCPCS code assigned by the Centers for Medicare and Medicaid Services for this test (G0107). We also examined the use of invasive colon tests (flexible sigmoidoscopy, colonoscopy, and double contrast barium enema). However, using ICD9 codes, it can be difficult to designate a given invasive colon test as screening or diagnostic . To defiWe determined the proportion of average risk beneficiaries who received screening fecal occult blood tests or who underwent flexible sigmoidoscopy, colonoscopy, or double contrast barium enema. For these tests, proportions were calculated using all tests identified by all CPT and/or HCPCS codes  or only tests identified by the algorithm described above (invasive tests for screening indications).We also analyzed screening test utilization in population subgroups as defined by age, sex, race, and place of residence . We compared differences in proportions of beneficiaries undergoing screening using chi-square tests. Place of residence was defined as urban or rural depending on the health service area in which the patient lived. Rural health service areas include all ZIP codes that are closest to a rural hospital, as defined by the Washington State Department of Health. Multiple logistic regression analysis was used to determine the relative odds of screening in different demographic groups (Stata 8.0, Stata Corp., College Station, TX). Significance of the regression models was tested using the log-likelihood statistic, and the method of Hosmer and Lemeshow was used to assess goodness of fit of the regression models.Beneficiaries were predominantly white, and there were more females than males Table . In the Overall, 7.2% had any invasive test (colonoscopy, flexible sigmoidoscopy, or barium enema for diagnostic or screening indications) in the 9-month study period Table . UtilizaHowever, when examining utilization of colonoscopy, sigmoidoscopy, and barium enema separately, some interesting demographic differences were seen Table . Men, beWe developed multiple logistic regression models to determine the relative odds of receiving screening tests in different population subgroups Table . ParalleWe developed similar multiple logistic regression models to look individually at utilization of colonoscopy, sigmoidoscopy, or barium enema for diagnostic or screening indications Table . UtilizaWe previously showed that colorectal cancer screening tests are under-utilized and that utilization did not increase shortly after introduction of the Medicare screening benefit . In thisIn addition, we show that insurance coverage for screening does not eliminate disparities in screening utilization. In fact, disparities actually increased over time in some instances. Compared to 1994–1998 , the relThe precise reasons for the observed demographic disparities in screening are unknown. The sex and race-related disparities are consistent with other data showing differential use of medical services in general in these population subgroups. Screening in general was most common in beneficiaries age 65 to 74. As the potential benefit of screening decreases with age and shorter life expectancy, the age-related decrement in screening after age 75 may be clinically appropriate. Regarding geographic differences, the availability of screening services, especially for the invasive and more resource intensive tests such as colonoscopy and sigmoidoscopy, may be greater in urban than in rural areas. Fecal occult blood testing is less resource intensive and is likely to be more available in rural areas, potentially explaining some of the geographic differences in screening.Projecting these data out over a longer period gives a more complete picture of utilization differences. For example, 3.5% of whites, 2.8% of blacks, and 1.6% of Hispanics had invasive screening tests done over the 9-month study period. Assuming constant screening rates, over a 5-year period, 23% of whites would undergo invasive tests, compared to only 19% of blacks and 11% of Hispanics. These differences are further magnified if fecal occult blood test utilization is also considered. These screening disparities may contribute to the known differences in colorectal cancer incidence and survival in different racial groups ,19.This study has several limitations. First, we cannot clearly separate the effects of Medicare coverage from secular trends towards increasing utilization of screening tests. Second, we used administrative claims databases to assess health services utilization. Although the accuracy of coding for the diagnoses and procedures studied here is not established, claims coding surgical services and procedures is fairly reliable and accurate -24. ThirOur study only included patients age 65 and older, and we did not assess utilization in patients younger than this. This was a cross-sectional study, and patients who may have previously been screened with procedures that are not recommended annually would have been classified as unscreened in our analysis. Utilization of colonoscopy and colonoscopy practice patterns may have changed substantially since 2000 -28. LastAlthough we developed an algorithm to distinguish screening from diagnostic tests, we cannot be certain that tests we designated as screening were truly intended as screening tests. We excluded patients with physician visits for gastrointestinal symptoms over the prior 3 months, but it may take longer to have a colonoscopy scheduled for these indications. This may influence our estimates of screening frequency. In our previous study, we found that 82% of procedures designated as screening from the HCPCS codes would have been classified as screening from our algorithm . HoweverThis study extends our previous work and shows that provision of insurance coverage of screening tests does not necessarily increase utilization of such tests in the medium term. Even 2 to 3 years after beginning universal coverage and widespread publicity about colorectal cancer screening , screeniOverall utilization of colorectal cancer screening tests increased only modestly 2 to 3 years after institution of Medicare coverage, but there was a shift towards screening colonoscopy and away from less invasive tests. Demographic differences in screening persisted despite consistent insurance coverage.CPT: Current Procedural TerminologyHCPCS: Health Care Common Procedures Coding SystemICD9: International Classification of Diseases – 9The author(s) declare that they have no competing interests.CK conceived the study, participated in data analysis and drafted the manuscript. WK participated in design and analysis of the study. LMB participated in design of the study and data analysis. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here: