The structure of molecular networks derives from dynamical processes on evolutionary time scales. For protein interaction networks, global statistical features of their structure can now be inferred consistently from several large-throughput datasets. Understanding the underlying evolutionary dynamics is crucial for discerning random parts of the network from biologically important properties shaped by natural selection.Saccharomyces cerevisiae based on several large-throughput datasets. Protein pairs resulting from gene duplications are used as tracers into the evolutionary past of the network. From this analysis, we infer rate estimates for two key evolutionary processes shaping the network: (i) gene duplications and (ii) gain and loss of interactions through mutations in existing proteins, which are referred to as link dynamics. Importantly, the link dynamics is asymmetric, i.e., the evolutionary steps are mutations in just one of the binding parters. The link turnover is shown to be much faster than gene duplications. Both processes are assembled into an empirically grounded, quantitative model for the evolution of protein interaction networks.We present a detailed statistical analysis of the protein interactions in S. cerevisiae.According to this model, the link dynamics is the dominant evolutionary force shaping the statistical structure of the network, while the slower gene duplication dynamics mainly affects its size. Specifically, the model predicts (i) a broad distribution of the connectivities (i.e., the number of binding partners of a protein) and (ii) correlations between the connectivities of interacting proteins, a specific consequence of the asymmetry of the link dynamics. Both features have been observed in the protein interaction network of  Molecular interaction networks are ubiquitous in biological systems. Examples include transcription control , signal aggregate  network features, such as the fraction of nodes with a given connectivity. This implies that only large-scale statistical features of protein interaction networks can currently be reliably identified by high-throughput approaches. We here present an empirically grounded model that explains empirically observed statistical features of such networks.In this paper we focus on protein interaction networks, whose nodes correspond to proteins, and whose links correspond to physical interactions between two proteins. Several complementary experimental techniques have been used to analyze pairwise protein and domain interactions, as well as protein complexes, in genome-scale assays -13. CommSaccharomyces cerevisiae. On evolutionary time scales, this network changes through two processes, illustrated by figure gene duplications. Duplications of a single gene result in a pair of nodes with initially identical binding partners. Segmental and global duplications of the genome lead to the simultaneous duplication of many genes. On the other hand, processes affecting the interactions between existing proteins are referred to as link dynamics. Link dynamics results primarily from point mutations leading to modifications of the interface between interacting proteins [The currently best characterized protein interaction network is that of the baker's yeast proteins . Both kiin vivo is influenced by further factors, notably gene regulation, which determines the concentrations of the proteins interacting in a living cell. The very definition of a bound state depends on the concentrations of the binding partners: A pair of proteins which binds at high concentrations may no longer form a bound state at lower concentrations. Here we concentrate on protein interactions at constant concentrations as they can be inferred from high-throughput datasets.Of course, proteome function Previous work by others -19 showsIn this paper, we present a model of network evolution that is based on observed rates of link and duplication dynamics. At these rates, the model predicts that important structural features of the network are shaped solely by the link dynamics. Hence, the evolutionary scenario of our model is quite different from the duplication-based models -19. The asymmetric. That is, this rate increases with the connectivity of only one of two the nodes involved. This reflects an asymmetry in the underlying biological process: a new protein-protein interaction is typically formed through a mutation in only one of two proteins.This paper has two parts. In the first part, we estimate the rates of link attachment and detachment from empirical data. Specifically, we do not just estimate average rates of link dynamics for the whole network, because this has been done previously , but we connectivity distribution, i.e. the fraction of nodes with a given number of neighbors. Instead, the analysis of networks under asymmetric link dynamics involves the link connectivity distribution, defined as the fraction of links connecting a pair of nodes with given connectivities.In the second part of the paper, we assemble the estimated rates of link dynamics into a model of network evolution. Unlike for most other cases studied so far ,4, the dconnectivity correlations between neighboring vertices, in accordance with recent observations in high-throughput protein interaction data [The model has only one free parameter, the average connectivity of nodes in the network. Its stationary solution correctly predicts statistical properties observed in the data. Central properties of this solution are ion data . These cpoint mutations in a gene affecting the interactions of the encoded protein. As a result, the corresponding node may gain new links or loses some of the existing links to other nodes, as illustrated in fig. attachment and detachment processes, which leave the number of nodes fixed, as link dynamics. The second kind of process consists of gene duplications followed by either silencing of one of the duplicated genes or by functional divergence of the duplicates [Two kinds of processes contribute to the evolutionary dynamics of protein interaction networks. The first consists of plicates -26. In t-2 per gene and per million years [single gene duplicates become silenced shortly after the duplication, leading to an effective rate g of duplications one order of magnitude lower, i.e., ~ 10-3 per million years [g ~ 10-3 per million years should be considered an upper bound for the growth rate of the protein interaction network by gene duplications. A crude lower bound for the link attachment rate is a ~ 10-1 new interaction partners per node and million years. For instance, [a = 2 × 294.5/1000 = 0.589, five times greater than the lower bound of 0.1. To maintain an average network connectivity at the empirically observed value κ ≈ 2.5 interaction partners per protein [a, thus d ~ a ~ 10-1 per million years. This rate of link attachment and detachment is much larger than the duplication rate of g ~ 10-3 per protein and million years. Hence, the link dynamics is decoupled from the much slower duplication dynamics. On intermediate evolutionary time-scales, the network reaches a stationary state of the link dynamics, while its number of nodes does not change significantly. This stationary state determines the structural statistics of the network, in particular the distribution of connectivities. On long time-scales, however, the network may grow through duplications. We emphasize that all these evolutionary rates are order-of-magnitude estimates, and that such estimates are suffcient for our model and the conclusions we derive from it.Individual yeast genes have been estimated to undergo duplication at a rate of the order of 10on years . Some 90on years ,25,27,28nstance,  estimatenstance, . Importak - k'|/(k + k') of duplicate protein pairs as a function of the time since duplication, parameterized by the fraction Ks of synonymous (silent) nucleotide substitutions per silent site. /2 neighbor pairs of a node of connectivity k. This fraction is small and it does not increase significantly with k. The data in this figure are also consistent with the earlier observation that the majority of duplicate pairs have few or no interaction partners in common [One basic but important empirical observation about link dynamics is the fast loss of connectivity correlations of proteins encoded by duplicate genes. Fig. on years ,27.) In on years -19, a prn common .We note that in our discussion of node dynamics we have not separately considered the effects of ancient genome duplications ,40. The a and d at which links are attached and detached in a protein interaction network allow no inference of how these processes shape the statistical properties of the network. To make such an inference, one must also know how the link dynamics depends on the connectivities of the nodes involved. The simplest possibility is that link attachment rates a and detachment rates d are functions of a node's connectivity k. The rates ak and dk at which links are attached or detached from a node of connectivity k have been estimated previously using interactions between products of duplicate genes [k.The total rates te genes . They ina, d) as functions of connectivity k (ak, dk), one assumes implicitly that that the mechanism of link attachment and detachment is identical (symmetric) for the two nodes involved in a changed link. Previous analyses of protein network evolution [ak and dk of link attachment and detachment are insuffcient to describe the dynamics of the network, since these rates will be different depending on whether the node is undergoing a mutation or not. This observation motivates the following estimate of the dependency of the link dynamics rate on node connectivities.In representing attachment and detachment rates  is proportional to N2, whereas the total number of links (which may be deleted) is proportional to N. We refer to the special case where the rates factorize, i.e. ak,k' ~ akak', as symmetric attachment . The specific form of these rates assumes that link dynamics is a local process, so the probability for the formation or destruction of a link depends on the connectivities of only the two proteins involved in this process.We define ak,k' on its arguments, k and k'. As described earlier [independently, yet the interaction between the nodes is retained, a cross-interaction is formed. This scenario does probably not account for the majority of cross-interactions, because it is inconsistent with data suggesting that self-interactions do not get lost overly frequently after duplication [k and k'. The main caveat of this approach is that the connectivity of the duplicates may have changed since the time the link between them was formed.We now explain how one can estimate the dependency of  earlier , one canlication . The seclication . From thk = k', and no systematic change along other directions. A different representation of the same data in fig. k + k'.The result of this procedure is shown in fig. k is chosen with a probability , and a second one is chosen with probability , gives an attachment rate . The attachment rate akk' ~ k + k' which we observe empirically is thus explained by an asymmetric attachment process where one node is chosen uniformly at random ( = constant), and the other node is chosen with a probability proportional to its connectivity ( ~ k). Note that the rate ak,k' itself is symmetric under interchange of the labels k and k', since either of the two nodes may take on the role of being preferentially chosen. However, the rate ak,k' does not factorize, exactly as required for an asymmetric attachment process.An attachment process where one node with connectivity ak,k'. The probability that out of nkk' pairs of duplicates with given connectivities k and k', mkk' pairs interact is , where gkk' gives the probability for a cross-interaction.  are the binomial coefficients. The probability p for observing for each pair k ≤ k' mkk' interactions in nkk' pairs of duplicates is then given by . Symmetric and asymmetric attachment differ in how the probability of a cross-interaction gkk' depends on k and k'. In the symmetric case, gkk' = gkgk'. In the asymmetric case where one node is chosen uniformly, the other with a probability fk, we have gkk' = fk + fk'. Using simulated annealing [p that the connectivity correlation pattern shown in fig. p with respect to fk and gk. We find that the maximal likelihood for asymmetric attachment exceeds that for symmetric attachment by a factor pasym/psym ~ 4. The data thus favor an asymmetric attachment process, consistently with the biological interpretation given above. In addition, in the maximum likelihood analysis of the asymmetric model, fk shows an approximately linear increase with k  The resulting detachment rate is dk,k' ~ (1/k) + (1/k'), where the inverse terms stem from nodes (rather than links) being chosen uniformly.Thus far, we have only discussed the link attachment rate. For the detachment of links, we analogously assume that links are lost due to mutations at one of two linked nodes, and that the rate of this process does not depend on the properties of the other node that is unaffected by a mutation. The simplest mechanism reflecting these assumptions is one where a protein loses on average The rates of the link dynamics discussed above, together with a slow growth of the network due to duplications, define a simple model for the evolution of protein interaction networks. Unlike previous models of the evolution of protein interaction networks -19 whichg = 10-3 per node per million years, as motivated above. Using this simplistic growth mechanism is appropriate since, as shown above, the link dynamics will quickly alter the initial connectivity of a new node, as well as connectivity correlations with its neighbors. We begin with a total number of 4600 nodes, uniformly linked at random (giving a Poissonian connectivity distribution) such that the average connectivity of nodes with non-zero connectivity is κ = 2.5, the average connectivity found in the data set we used. After a waiting time of 25 million years there are 4696 nodes in total, of which 1872 nodes have non-zero connectivity. This is the size of the pooled protein interaction data set we used. The waiting time of 25 million years is of the same order of magnitude as the time scale on which connectivity correlations of duplicate nodes decay in figure We chose the initial network size such that after a suffcient waiting time, when a stationary state has been reached, the size of the simulated network matches that of the protein interaction data set we used (see methods). Duplication of nodes is modeled simply by adding new nodes with connectivity zero to the network at a rate of a = 0.59 new interactions per node per million years, using the asymmetric preferential linking rule we motivated above. Specifically, to form a new link we chose one node uniformly and a second node preferentially  and link the two nodes. We removed links at a rate that keeps the average connectivity constant.New links are added at a rate of κ = 2.5. The link to be deleted is chosen equiprobably from the links of this node. The connectivity distribution of a network whose evolution was simulated in this manner is shown in figure g/a over at least an order of magnitude (results not shown).Specifically, at each time-step a link is deleted by choosing a node uniformly for link deletion if the average network connectivity exceeds qk,k'. This distribution is defined as the fraction of network links that connect vertices of connectivities k and k',We now turn to the consequences of this evolutionary dynamics for the statistical properties of the network. Since the link dynamics places and removes a link with a rate depending only on the connectivities of the nodes at either end, the evolutionary dynamics of the network can be represented in terms of the link connectivity distribution cij = 1 if node i is linked to j and 0 otherwise. For convenience, a factor κ has been included in the normalization, i.e., ∑k,k' qk,k' = κ. The link connectivity distribution qk,k' captures correlations between the connectivities of neighboring vertices [where vertices ,32-34. Ik > 0 and p0 = 1 - ∑k > 0 pk. The rates ak,k' and dk,k' are related to the total rates a and d of link detachment per unit time by the normalizationfor qk,k'For a network of infinite size, link and growth dynamics result in a deterministic differential equation for the evolution of the link connectivity distribution Jk,k' arise from links that are not added or removed but that change their values (k,k'),The terms g accounts for a uniform increase of the number of nodes caused by gene duplications.These are the links joining a mutated protein or its binding partner with third vertices, shown as open circles in fig. pk turns out to be inconsistent since asymmetric link dynamics generates non-trivial connectivity correlations. This distinguishes the present model from simpler models of network growth, which can be self-consistently formulated at the level of the distribution pk.In writing eq. (4), we have assumed that next-nearest neighbor connectivity correlations vanish. This assumption is self-consistent since the stationary solution has indeed only nearest-neighbor correlations. Truncating all correlations and writing down an evolution equation for the connectivity distribution qk,k' ~ kk'pkpk'. We followed the time evolution of qk,k' defined by eq. (4) until a steady state was reached using the parameters a and g given above and choosing d such that the average connectivity of connected nodes remains at a constant κ = 2.5. This procedure leads to a stationary link connectivity distribution  and a resulting connectivity distribution  independent of initial conditions. Because the evolution equation is a rate-equation that applies to a network of infinite size, the parameters determining the stationary state are the ratio between growth and attachment rate, the functional form of the attachment and detachment rates, and the average connectivity. The stationary state turns out to be asymptotically independent of the duplication rate for small duplication rates. In fact, if we solve eq. (4) numerically for any ratio g/a < 10-1, the results are statistically indistinguishable from that for g = 0, implying great robustness against errors in the rate estimates discussed above.We solved equation eq. (4), which describes the evolution of the connectivity correlations numerically for its steady state. For initial conditions we use a Poissonian connectivity distribution where the average connectivity of connected nodes is 2.5, and connectivity correlations which factorize  agrees well with the empirical data as shown in fig. dkk' = constant, and it is a crucial difference to models with symmetric attachment, where preferential attachment leads to scale-free networks, both at constant network size [The statistical properties of our model in its stationary state may now be compared with the corresponding quantities in the protein-interaction network. The connectivity distribution ork size , and in ork size ,35.k' than in an uncorrelated random network with the same connectivity distribution . Fig.  is the link connectivity distribution of the network with no connectivity correlations. Correlations with this property have recently been reported for the protein interaction network in yeast [a posteriori reason for considering asymmetric link dynamics.For the connectivity correlations, we find that vertices of high k are more frequently linked to vertices of low in yeast , but a q, that for the same tour in reverse is , which are generally equal only if the rates facorize in their arguments.A further consequence of asymmetric attachment is that our model does not obey detailed balance (as is the case of symmetric link dynamics, where attachment and detachment rates do factorize, see ). Asymmeone of them.We have presented a stochastic evolution model for protein networks, which is based on fast link dynamics due to mutations of the coding sequence of existing proteins and a slow growth dynamics through gene duplications. The crucial ingredient of the link dynamics is an asymmetric preferential attachment rule, which is supported by empirical data. The asymmetry has a simple biological interpretation, namely that mutations in one gene may lead to a new interaction of its product with that of another, unchanged, gene. Such a mechanism, where the two nodes involved in the generation of a new link play different roles, is probably the norm, rather than the exception, in biological networks. This holds particularly for regulatory networks, where a new interaction between two genes is formed by changes in the regulatory region of only qk,k', which give the fraction of links with connectivities k and k'. In our case, the model successfully reproduces the connectivity distribution found in empirically available protein interaction data. The asymmetry of the link dynamics also leads to connectivity correlations between interacting proteins, which have been observed empirically [Asymmetric link dynamics leads to a network model, where the aggregate variables necessary to describe network structure are the connectivity correlations irically . A modelirically  are of pThe protein interaction data in this paper was pooled from three sources. The first of these sources is a large-scale high-throughput experiment using the yeast two-hybrid assay   was retained if a second pair of matching amino acids was found within less than six amino acids from the first. This procedure was then repeated to the left of each alignment gap (see [Ks of synonymous (silent) substitutions per silent site, as well as the fraction Ka of replacement substitutions per replacement site were estimated using the method of Li [Data on yeast gene duplicates, generated as described in , was kingap (see  for moreod of Li .ak and dk an equilibrium distribution giving the probability of finding the network in the state given by adjacency matrix cij of . This results in a connectivity distribution  and trivial connectivity correlations , which factorize in the connectivities. This results ina constant . A model with symmetric link dynamics can thus produce any empirically observed connectivity distribution, but no networks with statistically significant connectivity correlations.The existence of non-trivial correlations may be attributed directly to the asymmetry of the link dynamics. Symmetric link dynamics, which is a standard mechanism in models of networks at constant size , leads tML and AW contributed equally to this work. All authors read and approved the final manuscript.


A critical step in processing oligonucleotide microarray data is combining the information in multiple probes to produce a single number that best captures the expression level of a RNA transcript. Several systematic studies comparing multiple methods for array processing have used tightly controlled calibration data sets as the basis for comparison. Here we compare performances for seven processing methods using two data sets originally collected for disease profiling studies. An emphasis is placed on understanding sensitivity for detecting differentially expressed genes in terms of two key statistical determinants: test statistic variability for non-differentially expressed genes, and test statistic size for truly differentially expressed genes.In the two data sets considered here, up to seven-fold variation across the processing methods was found in the number of genes detected at a given false discovery rate (FDR). The best performing methods called up to 90% of the same genes differentially expressed, had less variable test statistics under randomization, and had a greater number of large test statistics in the experimental data. Poor performance of one method was directly tied to a tendency to produce highly variable test statistic values under randomization. Based on an overall measure of performance, two of the seven methods (Dchip and a trimmed mean approach) are superior in the two data sets considered here. Two other methods (MAS5 and GCRMA-EB) are inferior, while results for the other three methods are mixed.Choice of processing method has a major impact on differential expression analysis of microarray data. Previously reported performance analyses using tightly controlled calibration data sets are not highly consistent with results reported here using data from human tissue samples. Performance of array processing methods in disease profiling and other realistic biological studies should be given greater consideration when comparing Affymetrix processing methods. Affymetrix microarrays are high throughput assays for measuring the expression levels of thousands of gene transcripts simultaneously. This type of microarray measures the expression of each transcript multiple times through a set of "probe pairs". Since the advent of the Affymetrix microarray, numerous methods have been proposed for producing numerical expression summaries for each transcript based on the probe pair data. Several systematic studies have appeared comparing a number of methods on a common basis e.g. -5). Thes. Thes5])]. While not every popular method is included in our study, several highly distinctive and original approaches are studied. For example, Dchip was one of the first approaches to attempt to learn probe weights directly from the probe data, and RMA pioneered the approach of disregarding the control mismatch probes. PDNN uses physical modeling to determine probe weights, while the two GCRMA methods use GC content of the probe sequences to reduce variance in the mismatch (control) probe levels. The MAS5 method is the current default method provided by Affymetrix.The following seven methods are considered here: Dchip , GCRMA-EIn addition to the six methods cited previously, we also include a method designated TM (trimmed mean). This is a simple method that has been used in a number of published investigations e.g. ,7), but , but 7])An important feature of this study is the use of False Discovery Rate (FDR) to quantify the sensitivity of a processing method in terms of its ability to distinguish differentially expressed genes from genes having invariant expression. This is a highly relevant property, as differential expression analysis is the most common application of microarray data. A key advantage of using FDR to compare processing methods is that FDR values can be calculated accurately using real disease profiling data where the identities of differentially expressed genes are uncertain. In contrast, most previous systematic comparisons of array processing methods have focused on calibration data sets in which concentrations of certain genes were experimentally manipulated.When it is highly likely that at least one gene is differentially expressed, false discovery rate may be defined as the expected ratio of the number of false positive calls to the total number of positive calls in a differential expression analysis between two groups of samples . If the A small FDR is due either to a small numerator, a large denominator, or both. The denominator of the FDR depends on the actual data distribution, so variation in this value may be due to factors such as accuracy in modeling the physical and chemical nature of probe binding. Variation in the FDR numerator, however, depends only on the distribution of values produced for randomized data, a purely statistical quantity reflecting the tendency of the method to incorrectly produce test statistic outliers. Our results suggest that both factors are important in determining sensitivity. The best methods produce many large test statistic values in the actual data, and also produce consistently small test statistic values for randomized data. Poor performance of one method can be directly explained by the tendency of the method to produce outlier expression values, leading to greater numbers of incorrectly large test statistics.strongly outperform the first. Based on this comparison, two of the methods considered are clearly favored, two are inferior, and results for the other three methods are mixed.For overall comparison, we evaluated every pair of methods on the basis of whether the first method is expected to call at least one truly differentially expressed gene that is not also called by the second method. If this is not expected to occur, the second method is said to 0 level, where FDR0 is a rescaled FDR (see methods). Figure Our primary basis for comparison is sensitivity – the number of genes detected at a given FDR0 = 0.1 as a reference point, there is roughly 7-fold variation across the seven methods in the number of detected genes in colon data using t-test statistics, while for rank-sum statistics the range is roughly 2-fold. In the ovary data, the range is around 1.25-fold for both statistics. Also notable is that variation in sensitivity due to the choice of test statistic (t-test or rank-sum) is smaller than variation in sensitivity due to the seven processing methods.The more challenging colon set distinguishes the seven processing methods to a greater extent than the ovary set. Using FDRNo single method stands out as having the best or worst performance in every case. However some methods generally perform better than others. The Dchip and TM methods perform consistently well, while the GCRMA-EB and MAS5 methods consistently perform poorly. PDNN performs well on the ovary data, but poorly on the colon data, and results for the other methods are mixed.0 threshold vary across the methods. Figure 0 thresholds in k or more of the seven methods to the number of probe sets falling below the threshold for at least one method is plotted against the FDR0 threshold, for k = 3, 4, 5, 6, 7. In the ovary data there is a very high level of agreement in this measure. For the rank-sum analysis, almost 90% of called genes are called by at least four methods, and more than 70% of called genes are called by all seven methods. For the t-test analysis, the agreement is slightly higher yet. For the colon data, the methods are much more inconsistent. For the rank-sum analysis, three of the methods agree on up to 90% of genes, but all seven methods only agree on around 30% of genes. The t-test analysis is even worse, with only around 10% of genes common to all seven methods.Identities of probe sets falling below a given FDRTurning to pairwise agreement, Table Complementing comparison of the statistical tests, we also compared the expression levels produced by the seven processing methods. For each pair of methods, and for each pair of samples within one of the two data sets, we calculated Pearson correlation coefficients of expression levels over all genes. These values were summarized by taking the median over all pairs of samples within a data set, shown in Table Variation in FDR across the seven methods is due to two factors – variation in the number of transcripts with large test statistics, and variation in the expected number of transcripts with large test statistics when there is no real differential expression. Here we investigate the second factor, which is driven by the tendency of each method to produce outlier expression values.The numerator of the FDR aims to correct for variation in the number of false positives, so that a method claiming large numbers of differentially expressed genes is not considered superior unless it also produces relatively small numbers of false positives. This can be viewed as a calibration, in which for each method, the test statistic must reach a certain threshold in order that the proportion of false positives is no greater than a specified value.0 less than f was calculated, and plotted against f. For example, to achieve any FDR0 value between 0.05 and 0.1 in the colon rank-sum data, GCRMA-MLE requires the lowest test statistics, RMA requires a rank-sum statistic 0.15 units larger than that of GCRMA-MLE, and MAS5 requires a rank-sum statistic 0.3 units larger than that of GCRMA-MLE.Calibration results are summarized in Figure Figure  denote the log2 expression level of transcript i in sample j for method k, where k = 1, ..., 7 denotes the seven processing methods, and letSince calibration depends only on randomized data, it should be possible to trace variation in thresholds across the processing methods to statistical properties of the expression levels. For example, if one method produces expression levels with heavier tails, it is easier to get a large t-test statistic value by chance, particularly for the colon data with small sample sizes. This would necessitate a higher threshold. To quantify this, let  is the pth quantile of , and med is the median value. This is an affine-invariant measure of the size of the right tail of the expression values. Values of Bk for the seven methods and two data sets are shown in Table B value of 3.74 when the sample size is as in the ovary data, and 3.56 when the sample size is as in the colon data. The GCRMA-EB method is seen to have a much greater propensity for producing extreme expression values, explaining its low sensitivity, poor agreement with other methods, and conservative calibration.where t, the number of probe sets for which the observed test statistic value exceeds t was calculated and plotted against t. For example, in the colon rank-sum data, PDNN had the smallest test statistics, with MAS5 having around 500 more probe sets meeting a log test statistic threshold of 5 compared to PDNN. The Dchip and TM methods have over a thousand more probe sets meeting this threshold.In addition to calibration differences, FDR variation is also influenced by the observed test statistic values. This is summarized in Figure Variation in test statistic values across the methods is greater in colon than in ovary data, and generally tracks with sensitivity. However note that in the colon rank-sum data, Dchip has substantially larger test statistics than GCRMA-MLE, even while GCRMA-MLE has better sensitivity Figure , due to An interesting possibility that can not always be excluded is that the intergroup differences are so vast that nearly every gene is affected to a small degree. If this were the case, the FDR values for the t-test and Wilcoxon statistics would converge to zero for every gene as the number of samples grows, making FDR values difficult to interpret. To further investigate this issue, we repeated the analysis using t-statistics truncated to zero when the fold change is less than 1.5 as test statistics for FDR analysis. The corresponding FDR values remain bounded away from zero for genes having true fold change smaller than 1.5, while genes with true fold change exceeding 1.5 have FDR values converging to 0. Thus the statistic identifies a meaningful subset of genes even when all genes are differentially expressed to some degree.Results for this analysis are shown in Figure N1 and N2 genes respectively at a given FDR level. Then nk = (1 - p0FDR0)·Nk estimates the expected number of truly differentially expressed genes called by method k. Now suppose that I is the number of genes called by both methods. Then nk - I is an estimated lower bound for the expected number of genes correctly called by method k but not by the other method. We will say that method 1 strongly outperforms method 2 if n1 - I ≥ 0 but n2 - I < 0. This means that in terms of differential expression, method 2 is not expected to contribute any true positives that were not called by method 1.Thus far we have focused on sensitivity as a criterion for comparing methods. However even if one method is less sensitive than another, if the overlap in the called gene sets is not too great then the less sensitive method may still contribute to our understanding of which genes are differentially expressed. Suppose two methods denoted 1 and 2 give p0 = 1 and FDR0 = 0.05, showing the number of times that each method was strongly outperformed by other methods in our study. This analysis clearly favors the TM and Dchip methods, while the MAS5 and GCRMA-EB methods are nearly always found to be strongly outperformed by the other 5 methods. These results are not sensitive to choices of p0 between 0.5 and 1 .Table The choice of processing method for Affymetrix array data evidently has a major impact on the ability to confidently report the results of differential expression analysis. The effect is greater, for example, than the choice of using a robust or a non-robust analysis, even in the colon data where robust analysis results in substantial improvements. Differences among processing methods are much greater in the more challenging colon data set compared to the ovary data, yet it should be noted that the sample sizes in the colon data are not atypical in real investigations.While results from two data sets can never conclusively determine the optimal method, it is notable that across both data sets, using both t-statistic and rank-sum analyses, there is a high degree of similarity in the rank ordering of the methods from the best to the worst performer. The trimmed mean (TM) and Dchip methods consistently perform as well or better than any of the other methods. A possible explanation for this is that the weights used by the Dchip may tend to downweight the least and greatest PM-MM differences, just as the TM method excludes these differences.When comparing array processing methods using experimental data in which the identities of differentially expressed genes are unknown, great care must be taken to ensure that apparent differences in sensitivity are not due to other factors. One critical point is that the null distribution providing the expected number of false positives at a given test statistic threshold (the numerator of the FDR) must fairly reflect the statistical behavior of null genes. Permutation approaches have been extensively used to produce empirical p-values (e.g. ) and werWhile we have focused on FDR as the basis of comparison, the pursuit of small FDR values is not the only desirable operating characteristic of an array processing method, and other reports have also emphasized the accuracy of estimating the precise size of concentration differences. However to the extent that most actual studies seek to find differential expression between groups, the use of small FDR values seems more instrumental as the basis for judging methods.Although our primary aim was to investigate variation in sensitivity due to the seven processing methods, all analysis was carried out independently for two test statistics. The t-statistic is widely used in practice, but is well-known to be sensitive to outliers, particularly when the sample size is small. We found that certain processing methods, particularly EB-GCRMA, had a tendency to produce outlier expression values in the colon data set. Thus the combination of using the EB-GCRMA method with t-statistics in the colon data led to particularly poor performance.In practice, the approach used for array normalization and for forming log-transformed expression values may be equally or more influential than the method used for producing probe set summaries . In thisNevertheless it remains of interest whether these routine processing steps are the determining factor of performance. In a future study it will be important to investigate this question further by modifying the implementations of the processing methods so that uniform log transforms and array normalizations are applied.A key point that we advocate in this work is that false discovery rates in actual disease profiling data constitute a valuable complement to benchmarking results obtained from spike-in, dilution series, and mixture experiments e.g. ,5). The . The 5])In spite of the statistical challenges in using disease profiling data for benchmarking, we argue that these data sets also offer some unique advantages. Calibration data sets are relatively few in number and are not available for all platforms. Newer platforms in particular are under-represented. Therefore overtraining to the available calibration data through manipulation of the many tuning parameters in the more complicated processing methods is an unavoidable concern. In addition, the calibration data sets likely do not represent the same degree of challenge as disease profiling data in that reproducibility of fold changes for affected and unaffected genes is quite high compared to data from, say, human tissues where a large number of uncontrolled sources of variability are present.Performances of multiple array processing methods on disease profiling data sets vary widely across the seven methods studied here, but results are generally consistent between the two data sets studied. Results of our analysis generally do not parallel results obtained using calibration data sets ,5, suggeA major determinant of sensitivity is test statistic variability for randomized data. Such variability will affect false discovery rates as well as empirical p-values, which are an often-used alternative approach for identifying differentially expressed genes (e.g. ). TherefIn this comparative analysis we did not seek to draw definitive conclusions about the "best" or "worst" methods. Such conclusions may be made after investigating a greater number of data sets, including disease profiling data, data from controlled experiments, and calibration data. Moreover, it may be that the correct choice of method may depend on the scientific question being asked. The key message of this work is that the wide range of data sets collected in actual scientific investigations may be used for comparison of processing methods, and that in at least the two data sets considered here, similar results were obtained in the rank ordering of the methods.We used two data sets – one consisting of 79 ovary tumors and the other consisting of 47 colon tumors. Both sets were generated at the University of Michigan using Affymetrix HG_U133A arrays, which consist of 22283 probe-sets, each of which is designed to assay a RNA transcript. Each probe-set consists of a set of  probe-pairs, with each probe-pair comprising a "perfect match" (PM) probe which is a 25-base oligonucleotide complementary to the transcript, and a "mismatch" (MM) probe that is identical to the PM sequence except for alteration of the central base. The MM probe is intended as a control for nonspecific hybridization, so that the difference PM-MM measures only specific binding. However not all processing methods use the MM data in this way.For differential expression analysis, the 79 ovary samples were partitioned according to histological class into 38 endometrioid and 41 serous samples. The 47 colon samples were partitioned into 40 microsatellite stable (MSS) samples and 7 microsatellite instable samples (MSI). In both data sets, the partition is based on an independently measured biological characteristic, so there almost certainly are differentially expressed genes to be found. However in neither case are the two classes highly distinct, and numerous other sources of biological variation are undoubtedly present in the data.Array normalization refers to an adjustment of data distributions within each array in order to make the arrays more comparable. Each array processing method has been coupled with a normalization procedure by its developers (see references). We followed these method-specific normalization practices in our analysis. All methods other than MAS5 use some form of quantile normalization.All analysis was based on log-transformed data. Log-transformed values, including truncations where needed, were calculated in the manner recommended by the developers of each method (see references).T is always analyzed as |T|, and the rank-sum statistic R is standardized as , where m0, m1 are the numbers of samples in the two classes, and m = m0 + m1 is the total number of samples.We compared the seven methods based on their sensitivity in detecting differential expression at a fixed false discovery rate (FDR). For each method, two different two-sample test statistics were calculated for each gene – the standard two-sample t-statistic, and the Wilcoxon rank-sum statistic . The t-test statistic t, the FDR was estimated as follows. Randomized data sets were constructed by randomly reassigning the class identifiers to the samples. The average number of transcripts with test statistic value exceeding t was calculated over 1000 randomized data sets. This number was divided by the number of actual transcripts with test statistic value exceeding t to produce a value that we denote FDR0. In practice the value of FDR0 should be scaled by the proportion p0 of non-differentially expressed genes, giving FDR = p0FDR0. Although various estimates of p0 exist, we elected to ignore this factor since it is constant across the methods for a given data set, and any estimate of p0 would add an additional source of uncertainty to our results. Thus it should be noted that the reported FDR0 values, while comparable across methods, are somewhat larger than the usual estimates. Since p0 would generally be greater than 1/2, the bias is likely less than a factor of 2.Our FDR approach closely follows the "global estimate" of Efron et al. ( equationKS, JMGT, RK, and DG participated in all phases of design and analysis. WC and JM performed the data analysis. KRC, TJG, SBG, ERF, and SH assisted in study design and supervised data collection. All authors read and approved the final manuscript.


Major histocompatibility class I molecules display tens of thousands of peptides on the cell surface for immune surveillance by T cells. The peptide repertoire represents virtually all cellular translation products, and can thus reveal a foreign presence inside the cell. These peptides are derived from not only conventional but also cryptic translational reading frames, including some without conventional AUG codons. To define the mechanism that generates these cryptic peptides, we used T cells as probes to analyze the peptides generated in transfected cells. We found that when CUG acts as an alternate initiation codon, it can be decoded as leucine rather than the expected methionine residue. The leucine start does not depend on an internal ribosome entry site–like mRNA structure, and its efficiency is enhanced by the Kozak nucleotide context. Furthermore, ribosomes scan 5′ to 3′ specifically for the CUG initiation codon in a eukaryotic translation initiation factor 2–independent manner. Because eukaryotic translation initiation factor 2 is frequently targeted to inhibit protein synthesis, this novel translation mechanism allows stressed cells to display antigenic peptides. This initiation mechanism could also be used at non-AUG initiation codons often found in viral transcripts as well as in a growing list of cellular genes. Proteins have been identified for which a unique translational machinery makes use of unconventional start codons Immune surveillance by cytotoxic T cells (CTLs) is a key mechanism for detecting and eliminating abnormal cells. These include cells infected with viruses or bacteria, and those that have suffered tumorigenic transformations . The antThe antigen-presenting cells (APCs), which include almost all nucleated cells, are also very efficient in generating peptides for display by MHC class I . In factiMet, which is specific for AUG and is always charged with the methionine residue -TFNYRNL peptide (the initiation codon is in parentheses and the remaining amino acids are in single-letter code) . AlthougRNAiMet . In the d by CAA . Becaused by CAA . TherefoTo address this question, we identified the first amino acid of CUG-initiated peptides that were presented by MHC class I molecules and thus detectable by appropriate T cells. In this assay, we cotransfected cells with cDNA constructs encoding the peptide as well as the MHC molecule that binds and presents the peptide on the cell surface. Binding to the MHC protects the initiation residue from being removed by cellular proteases, which can trim antigenic peptides in the cytoplasm or the endoplasmic reticulum . We thenb MHC molecule and the *(CTG)-TFNYRNL* peptide ([CTG]YL8). We refer to this as the xYL8 model. We placed the peptide in three different contexts: first, in the pcDNA1 vector; second, upstream of the IRES in the pIRES2-eGFP vector; and, finally, in the 5′ UTR of green fluorescent protein (GFP) in the pcDNA1 vector. The peptides translated in the transfected cells were extracted and fractionated by HPLC. Each fraction was tested for the presence of the leucine-initiated LTFNYRNL (LYL8) peptide and the methionine-initiated MTFNYRNL (MYL8) peptide using BCZ103 T cells and Kb-expressing L cells (a fibroblast cell line) as APCs. With each construct we found a single peak of antigenic activity that eluted in the same fraction as the synthetic LYL8 peptide (To determine if decoding of the CUG initiation codon as leucine was influenced by 5′ or 3′ untranslated regions (UTRs) of the mRNA, we transfected COS-7 cells with cDNA encoding the K peptide A–1C. In  peptide A and 1D.b MHC class I molecule that satisfied the conditions required for our assay: that an MHC molecule present the peptide, that HPLC allow distinction between the leucine- and methionine-initiated forms, and that a T cell cross-react with the peptides with leucine or methionine residues at the first position. When COS-7 cells were transfected with cDNA constructs encoding the Db MHC molecule and the *(CTG)-SNEN-METM peptide derived from the influenza nucleoprotein, only the leucine-initiated LM9 peptide was detected in the HPLC-fractionated cell extracts  and read through the stop codon before the CTG initiation codon in the LYL8 coding sequence. We showed that increasing the number of stop codons upstream of CTG from one to six had no effect on LYL8 expression, and that substituting the CTG codon with other leucine-encoding triplets essentially ablated peptide expression . We asse complex B and the complex C. T cell complex C. To con complex D. Again,We also considered the possibility that leucine was used as the first amino acid because of an RNA modification that introduced an AUG immediately upstream of the peptide-coding sequence. However, the experiments below show that the 5′ UTR influences the leucine start because it is affected by the Kozak context, by the presence of an upstream hairpin, and by the presence of upstream initiation codons. Together, these findings demonstrate that the mRNA remained intact.GCCACCAUGG is optimal, and that the nucleotides at positions –3 and +4 are the most influential, while the nucleotide at the –6 position exerts a smaller effect. Changing these nucleotides can change protein expression by 20-fold SEL8* into the pcDNAI vector, where N represents the degenerate nucleotides flanking the CTG initiation codon for the SLVELTSL (SEL8) peptide presented by the Kb MHC molecule to bm1BZ19.4 T cells SEL8* stimulated the T cells, demonstrating that initiation activity was restricted to the CTG codon (TCCACCCUGG, which is in close agreement with Kozak's consensus sequence (GCCACCAUGG) for the AUG start and extends earlier findings that showed that an A at +5 and U at +6 can also enhance initiation at the CUG codon YL8 model, in which the predominant T cell-stimulating activity is the leucine-initiated LYL8 peptide see A–1C. At b MHC cDNA. After 2 d the transfected cells were assayed for their ability to stimulate the BCZ103 T cell. Cells expressing the LYL8 peptide with its CTG initiation codon in the “Excellent Kozak” context (T at –6, A at –3) were superior to those with the CTG codon in a “Poor Kozak” context (G at –6, T at –3) in stimulating the T cell response  T cells A and 6B. T cells .3CTG. When the transfected cells were used directly to stimulate T cells, the upstream ATGs had little effect (We then transfected cells with a construct encoding (CTG)YL8 and another encoding (CTG)YL8 with three ATGs upstream of and out of frame with the peptide (ATG)e effect C. Howevee effect D. Thus, 3CTG. When the transfected cells were used directly to stimulate T cells, we saw a small but consistent inhibition YL8 with three CTGs upstream of and out of frame with the peptide (CTG)hibition E. Remarkhibition F. The obNote, however, that the upstream CUGs, despite an “Excellent Kozak” context, inhibited the leucine start weakly. This effect contrasts with the inhibition caused by the upstream AUGs on the AUG/methionine or the CUG/methionine starts and suggests that other features are required for an efficient CUG/leucine start. Interestingly, one form of the ASCT2 amino acid transporter is initiated with multiple CUG and GUG codons in close proximity . This reiMet onto the 40S ribosome. Cells target eIF2 by phosphorylating its α subunit (eIF2α) to inhibit protein synthesis in response to a number of stress signals, including viral infection, starvation, and the accumulation of unfolded proteins. Ribosomes release eIF2-guanosine diphosphate (GDP) after the AUG initiation codon is reached, and GDP is exchanged for guanosine triphosphate (GTP) with the assistance of another protein, eIF2B, before eIF2 can be used for another round of translation initiation. When eIF2α is phosphorylated, it binds eIF2B with unusually high affinity and thus prevents subsequent nucleotide exchange. Because eIF2B is limiting in the cell, phosphorylation of only a fraction of eIF2α can substantially inhibit translation globally , which is responsible for loading the RNAglobally .To approach this question, we transfected HeLa cells with (CTG)YL8 or (ATG)YL8 constructs. We then assayed peptide expression in cells that had or had not been treated to induce phosphorylation of eIF2α. It was a challenge to induce phosphorylation of eIF2α for long enough to see an effect on peptide expression without causing substantial toxicity. Furthermore, we could not disrupt peptide/MHC assembly in the endoplasmic reticulum, a requirement that ruled out standard reagents such as dithiothreitol, thapsigargin, and tunicamycin. We also did not want to inhibit peptide elongation, which ruled out amino acid starvation.The optimal treatment for our purposes was sodium arsenite (NaAs). Arsenite reacts with sulfhydryl groups and causes phosphorylation of eIF2α presumably by inducing an unfolded protein response, although the precise mechanism remains unknown . We tranWe assayed the HeLa cells for peptide expression using the BCZ103 T cell hybridoma B. We fouThe effect of eIF2α phosphorylation on the leucine start strikingly mirrors the effect of eIF2α phosphorylation on proteins whose synthesis is directed by the CPV-IRES, which does not require eIF2 . IntriguThe CPV-IRESs are to date the only known sequences that allow eIF2-independent initiation in eukaryotic cells. Viruses employ a host of creative strategies to prevent phosphorylation of eIF2α . InitiatIn summary, we found that when CUG acts as an alternate initiation codon, it can be decoded as leucine as well as methionine. The leucine start does not depend on mRNA structure or sequence, but its efficiency can be enhanced by the Kozak context. A set of ribosomes is scanning 5′ to 3′ specifically for the CUG initiation codon. While the methionine start is inhibited when cells are treated with NaAs, the leucine start is enhanced, suggesting that leucine initiation is independent of eIF2. This novel translation initiation mechanism provides cells not only antigenic peptides but also a potential tool for translational control.–, COS-7, Kb+B7.2+ L, Db+B7.2+ L, BCZ103, 30NX/B10Z, and DBFZ cells have been described . HeLa cells were obtained from ATCC , and cultured in Eagle's minimal essential medium modified with Earle's balanced salt solution, nonessential amino acids, 2 mM L-glutamine, 1 mM sodium pyruvate, 1500 mg/l sodium bicarbonate (ATCC #30–2003), 10% fetal bovine serum, and 10 μg/ml ciprofloxacin.Lmtkescribed . Cell liSequences for constructs depicted in Sequences for constructs depicted in Sequences for constructs depicted in Sequences for constructs depicted in Sequences for constructs depicted in 3ATG-YL8 in the BstXI/XbaI sites of the pcDNA1 vector,5′-TGTGAACCCAGGGTCGACCCAGGACCCAGGTAGTCGACCATGACCTTCAACTACCGGAATCTCTAG-3′; and ATG3ATG-YL8 in the BstXI/XbaI sites of the pcDNA1 vector,5′-TGTGAACCATGGGTCGACCATGGACCATGGTAGTCGACCATGACCTTCAACTACCGGAATCTCTAG-3′.Sequences for constructs depicted in 3CTG-YL8 in the BstXI/XbaI sites of the pcDNA1 vector,5′-TGTGAACCCAGGGTCGACCCAGGACCCAGGTAGTCGACCCTGACCTTCAACTACCGGAATCTCTAG-3′; and ATG3CTG-YL8 in the BstXI/XbaI sites of the pcDNA1 vector,5′-TGTGAACCATGGGTCGACCATGCACCATGGTAGTCGACCCTGACCTTCAACTACCGGAATCTCTAG-3′.Sequences for constructs depicted in 3CTG-YL8 in the HindIII/XbaI sites of the pcDNA1 vector mutated to alter the two CTGs in the 5′ UTR to CAG,5′-AGCTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTTAGAAACTGACCTTCAACTACCGGAATCTCTAG-3′; and CTG3CTG-YL8 in the HindIII/XbaI sites of the pcDNA1 vector mutated to alter the two CTGs in the 5′ UTR to CAG,5′-AGCTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACTGGATTACAAACTGGATTACAAACTGGATTTAGAAACTGACCTTCAACTACCGGAATCTCTAG-3′Sequences for constructs depicted in 3CTG-YL8 in the HindIII/XbaI sites of the pcDNA1 vector,5′-AGCTACAAACAGGNTTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTTAGAAACTGACCTTCAACTACCGGAATCTCTAG-3′; and CTG3CTG-YL8 in the HindIII/XbaI sites of the pcDNA1 vector,5′-AGCTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACAGGATTACAAACTGGATTACAAACTGGATTACAAACTGGATTTAGAAACTGACCTTCAACTACCGGAATCTCTAG-3′.Sequences for constructs depicted in Sequences for constructs depicted in – or COS-7 cells were transfected in 96-well plates (1 × 104 cells per well) with the indicated concentration of plasmid DNA, 0.1 mg/ml DEAE-dextran, 0.1 mM chloroquine, 10 ng/ml MHC class I cDNA, 5 ng/ml B7.2 cDNA, and 10% NuSerum  in RPMI 1640. After 90 min, the cells were shocked with 10% DMSO in PBS for 2 min. After 2 d incubation in normal medium, T cells (1 × 105 cells per well) were added to assay peptide expression. GeneJuice (Novagen, Madison, Wisconsin, United States) transfection reagent was used in The DEAE-dextran transfection method was used in 5 cells) were incubated with APCs in 96-well plates for 6–24 h. The response, accumulation of intracellular β-galactosidase, was measured with the substrate chlorophenol red-β-D-galactopyranoside (CPRG). The product was measured with a 96-well plate reader at 595 nm and 655 nm as the reference wavelength.The T cell assay has been described previously . Briefly4 per well) and T cell hybridomas (1 × 105 cells per well).The HPLC assay has been described previously . BrieflyCells were transfected with GeneJuice (Novagen) as described above, but the transfection medium was left on for only 4 h before the cells were lifted and split into multiple dishes with fresh medium. Cells were allowed to rest for 12 h before sodium arsenite treatment. They were treated with 50 μM NaAs (Sigma, St. Louis, Missouri, United States), 1× GolgiPlug containing brefeldin A , or left untreated for 4 h. The cells were then lifted and counted. For the T cell assay, they were titrated in a 96-well plate. The assay was as described above, except that 1× GolgiPlug was added in order to “freeze” the cells in their state at the end of treatment. For the Western blot, they were incubated on ice for 5–10 min in lysis buffer (20 mM HEPES [pH 7.5], 150 mM NaCl, 1% Triton X-100, 10% glycerol, 1 mM EDTA, 10 mM tetrasodium pyrophosphate, 100 mM NaF, 17.5 mM β-glycerophosphate, 0.4 U/ml aprotinin, 10 μg/ml leupeptin, 1 mM PMSF, 0.1 mM pepstatin A, and complete protease inhibitor cocktail [Roche, Basel, Switzerland]). The lysate was spun for 15 min at 4 °C, and the supernatant was transferred to a tube containing an equal volume of 2× SDS-PAGE sample buffer (100 mM Tris-HCl [pH 6.8], 20% glycerol, 4% SDS, bromophenol blue, and 5% β-mercaptoethanol). The sample was then heated in water just off the boil for 5 min, separated on a 10% SDS-PAGE gel, and transferred to a nitrocellulose membrane. The membrane was blocked for 1 h at room temperature in TBS–0.1% Tween 20–5% bovine serum albumin (TBS, 0.02 M Tris-HCl [pH 7.6] with 0.137 M NaCl), incubated for 1 h with primary antibody  in TBS–0.1% Tween 20–5% bovine serum albumin, washed four times for 5 min with TBS–0.1% Tween 20, incubated for 40 min with secondary antibody , washed four times for 5 min with TBS–0.1% Tween 20, incubated for 5 min in substrate , and exposed to film. An antibody to α-tubulin  was used as a loading control.


Human Immunodeficiency Virus (HIV) infection is a dynamic interaction of the pathogen and the host uniquely defined by the preference of the pathogen for a major component of the immune defense of the host. Simple mathematical models of these interactions show that one of the possible outcomes is a chronic infection and much of the modelling work has focused on this state.above a critical extinction rate, the host could eliminate the virus. The question then is how to increase the extinction rate of lymphocytes over a period of several weeks to several months without affecting other parameters of the system.However, the models also predict the existence of a virus-free equilibrium. Which one of the equilibrium states the system selects depends on its parameters. One of these is the net extinction rate of the preferred HIV target, the CD4+ lymphocyte. The theory predicts, somewhat counterintuitively, that Proposed here is the use of drainage, or filtration, of the thoracic duct lymph, a well-established surgical technique developed as an alternative for drug immunosuppression for organ transplantation. The performance of clinically tested thoracic duct lymphocyte depletion schemes matches theoretically predicted requirements for HIV elimination. Reports on the high turnover rates of HIV and its preferred target, CD4+ lymphocytes, during the latent phase of HIV infection -3 have eproduct of the number of uninfected cells and the number of free virions. This makes the resulting equations non-linear, and when the question of equilibrium is addressed, which is done by setting all rates (changes with time) equal to zero, there are two distinct solutions. One of these is free of virus, i.e. the number of virions (and infected cells) is equal to zero, whilst the other equilibrium state has non-zero values for all three populations and thus corresponds to a chronic infection. Which one of the two equilibrium states the system attains depends on the values of the system parameters. The most natural parameter to consider for switching the states is the difference between the rates at which uninfected cells are dying and proliferating. If this parameter, the net extinction rate of healthy lymphocytes, is increased above a critical value, the virus-free equilibrium is selected. This selection (bifurcation) is driven by the conditions of stability; the chronic infection state becomes unstable, i.e. any disturbance takes the system out of it, whilst the virus-free state becomes stable. Once the net extinction rate exceeds the critical value, the system finds its way out of infection. It just so happens that the amount by which the extinction rate needs to be changed, and this based on our current best estimates of other values, is quite modest – several percent of the total lymphocyte population needs to be removed daily.For a general medical audience it would be desirable to describe the basic features of the dynamics of HIV infection without recourse to any mathematical formulations. Dynamics implies change over time and the behavior of a dynamic system is defined by stating how the system variables affect each other during a unit of time. In the simplest model there are three system variables: (i) the number of uninfected lymphocytes, (ii) the number of infected lymphocytes and (iii) the number of free virions. The system equations describe how these populations interact. For HIV/CD4+, some of these interactions are understood and generally accepted; others are more speculative, and are subject to further study. However, even with different assumptions about these lesser known aspects, the most interesting result is little affected because it derives from the fact that the rate of infection, i.e. the number of newly infected cells in a unit of time, is proportional to the Depletion of lymphocytes as a therapy for AIDS, based on a population dynamic model, has been advocated by de Boer and Boucher . They prcontrary to the expected effect of depletion.This author has arrived at the same result independently using a population dynamics model (three populations, as described above), but also using an expanded model that includes the immune response and, in particular, Tat protein-induced apoptosis . The intActivation of latent CD4+ by OKT3 and IL-2 with intention to purge the virus has also been attempted , but theAs a further illustration of how complex different interventions with biological modifiers can be, treatments with depleting CD4 monoclonal antibody showed a preferential loss of naive T cells, but did not affect IFN-gamma secreting cells , providiThe prediction of the theoretical model calls for the removal of 5 to 10 percent of the total lymphocyte pool per day. The critical value is subject to uncertain estimates of some parameters, and it does differ between the simple, three-parameter and the five-parameter, expanded model. Perhaps the best approach would be to begin depletion, monitor the response by the viral RNA, and then adjust the depletion rate. All of this suggests that some means of physical removal would be best suited. Extracorporeal blood cell separation is a possibility, but the estimate of several hours that the patient would have to spend on the machine daily for several weeks to months, is very discouraging. However, filtration of the thoracic duct lymph, where lymphocytes are present in high concentration, seems almost ideal. The technique of duct drainage for lymphocyte depletion was developed in the sixties and the seventies in order to reduce the risk of organ rejection -21. It hAn alternative to removal of lymphocytes by duct drainage or filtration is their diversion from the lymphatic system into the gastro-intestinal tract, which has been demonstrated in experimental animals -32. Cellweakens along with the infection. This suggests a possibility for a pharmacological intervention based on Tat protein that could sustain the apoptotic signal without introducing molecular modifiers with potentially broader effects. If vaccinations were to work, upon entry of the pathogen, they should provoke apoptosis of lymphocytes, not their proliferation.Unfortunately, theoretical predictions of system dynamics are not very encouraging for the prospects of HIV vaccines. In principle, the vaccination primes the system for a faster, stronger response, including proliferation of the responding lymphocytes. As the same cells are targets for the virus, the system moves away from the stability condition for the virus-free equilibrium. Apoptosis of uninfected CD4+ lymphocytes in HIV infection is an appropriate response (for the host), albeit insufficient, since the cause of apoptosis is Tat protein produced by only the infected cells themselves. This prevents the system from eliminating the virus because the apoptotic signal Until such discoveries are made, and to test perhaps their ultimate potential, a simple surgical intervention to allow for removal of lymphocytes merits further investigation. Dangers posed to the patient would be significant, both due to morbidity of the procedure itself, and the expected, but difficult to precisely predict cumulative effects on immunocompetence. An attractive aspect of using physical means of depletion is the possibility to terminate the treatment instantly and completely, as soon as any major deviations from the expected response would arise, indicating a failure of the model and alarming for unexpected risks.None declared.


During cytotoxic lymphocyte (CL) mediated killing of target cells, granzyme B is released from the CL into the immune synapse. Recent studies have found that ELISPOT-detection of granzyme B correlated well with conventional assays for CL mediated killing. In this way, the released granzyme B can be used to mark the spot where a target cell was murdered. We discuss the benefits and potential limitations of using this assay to measure CL mediated killing of target cells. The alcein-AM , that ha51Cr remains the gold standard. Recently, Shafer-Weaver et al and others have utilized an interesting strategy aimed at measuring the functions of effector cells rather than death of the target cell [et al., [51Cr release during antigen specific target cell death induced by cytotoxic T-lymphocytes and now report utility of this assay for measuring MHC non-restricted killing by natural killer cells [Shafer-Weaver et al., measured granzyme B by ELISPOT and found that the number of SPOTS correlated well with results obtained by the 51Cr release assay. Unlike the 51Cr release assay, this ELISPOT assay measures a specific and well-characterized event that occurs following target recognition. Assessing granzyme B by ELISPOT appears superior to other markers, such as IFNγ, because it assays a molecule that directly participates in CL mediated killing. Furthermore, the assay is non-radioactive and under the experimental parameters reported, it appears possible to detect cytolytic activity using fewer cells than are required for 51Cr release.Various alternative assays have been developed to assay CL-induced killing of target cells -10, howeget cell ,11. Duriget cell ,12. In tget cell ,14. Shafer cells . FollowiThis assay appears to provide an effective alternative method for assessing CL-mediated cell death, however, users should be aware of possible limitations. The assay measures granzyme B release, not cell death. Frequently, the two will be closely correlated, but under certain circumstances using granzyme B release as a marker could lead either to an under or over estimate of target cell death. For example, perforin-deficient CLs are unable to kill targets ,17, yet 51Cr and granzyme B ELISPOT shown under the conditions used by Shafer-Weaver et al [51Cr). Increasing the effectors generated too many spots to count. Therefore an experiment optimised for 51Cr assay, (0–70% release as reported in Table 1), will only have a dynamic range of between 0 and 0.6% using the ELISPOT assay. In contrast, an assay optimized for ELISPOT is likely to be off scale in a 51Cr release assay. These data suggest that a small amount of killing (e.g in a sample with low level killing) may easily generate a positive result by ELISPOT. It is therefore likely that stringent titration of both effectors and targets over a narrow range will be essential.The limits of detection of this assay are not clear. It is not known whether the granzyme B released at a single death-inducing synapse are sufficient to produce a spot or whether a CL must degranulate several times, possibly killing multiple targets, to facilitate detection. Even if one spot reflects degranulation by one CL and is directly equivalent to one target cell death, it remains possible that CLs expressing granzyme B below the level of detection by ELISPOT may express sufficient granzyme B to kill their targets. These are difficult issues to address, but the correlation between er et al  suggestsThe granzyme B-ELISPOT introduces a new assay for measuring CL mediated toxicity that will have a widespread utility in experimental systems where granzyme B is present in the effector cell and the target is susceptible to CL mediated killing. However, no assay used in isolation can be the answer to everyone's prayers and the granzyme B ELISPOT, like all others, has limitations. There is no doubt that this assay measures triggering of degranulation, but it does not directly address the question of cell death. Therefore it is likely that the greatest utility of this assay will be found by using it in combination with other existing measures of cytotoxic activity. It may also be extremely valuable as a quick reference to determine whether killing can occur in an assay with defined targets and effectors.CL, cytotoxic lymphocytes; ELISPOT, enzyme linked immunospot; Cr, Chromium;The authors declare that they have no competing interests.All authors contributed to the ideas, discussion and preparation of this manuscript.


Recent developments in sequence databases provide the opportunity to relate the expression pattern of genes to their genomic position, thus creating a transcriptome map. Quantitative trait loci (QTL) are phenotypically-defined chromosomal regions that contribute to allelically variant biological traits, and by overlaying QTL on the transcriptome, the search for candidate genes becomes extremely focused.We used our novel data mining tool, ExQuest, to select genes within known diabesity QTL showing enriched expression in primary diabesity affected tissues. We then quantified transcripts in adipose, pancreas, and liver tissue from Tally Ho mice, a multigenic model for Type II diabetes (T2D), and from diabesity-resistant C57BL/6J controls. Analysis of the resulting quantitative PCR data using the Global Pattern Recognition analytical algorithm identified a number of genes whose expression is altered, and thus are novel candidates for diabesity QTL and/or pathways associated with diabesity.Transcription-based data mining of genes in QTL-limited intervals followed by efficient quantitative PCR methods is an effective strategy for identifying genes that may contribute to complex pathophysiological processes. Completion of the mouse genome holds considerable promise in the discovery of genes responsible for genetically determined complex diseases. Quantitative trait loci (QTL) are allelically variant regions detected by virtue of their contribution to the overall complex disease phenotype and thus are "experiments in nature", which mark chromosomal intervals carrying genes with a proven disease involvement. Since gene expression is a key link between the genome and the plethora of phenotypic traits exhibited, tools that permit the analysis of the tissue expression pattern of genes in their chromosomal context provides a bridge between QTL and the genes responsible.Understanding the molecular etiology of disease processes is a pressing goal of 21Global microarray gene expression technologies offer a promising, unbiased approach toward this goal in that they reveal gene expression changes, which can be correlated with the disease phenotype. However, such global methods of analysis are not routine analytical tools and can suffer from incomplete gene coverage, as well as lack of sensitivity. Because only a small fraction of the transcriptome is typically involved in any given etiopathological process, bioinformatic data mining tools that allow for the intelligent prioritization of genes would make it possible to employ more routine and sensitive expression technologies, such as quantitative polymerase chain reaction (QPCR). ExQuest ,2 organiType II diabetes (T2D), also referred to as non-insulin dependent diabetes mellitus, is characterized by insulin resistance of target cells combined with insufficient insulin production and/or abnormal secretion by pancreatic β-cells, which eventually results in chronically elevated glucose levels in the circulation. Obesity is a major risk factor for T2D, and the term "diabesity" has been coined to collectively describe these overlapping conditions . This pain silico expression data with QTL intervals. We were particularly interested in genes with known or potential metabolic function whose expression patterns were biased towards high expression in three diabesity relevant-tissues, pancreas, liver and adipose tissue, for which there was good EST library representation.  An example is illustrated in Figure Tafat [Ela2), with a pattern of expression that includes pancreas, stomach and tongue  and two paralogs, Pnliprp1 and Pnliprp2. From all QTL intervals, we chose the 71 "best expression" candidates , and diabetes or obesity. The number of articles retrieved were typically one to two orders of magnitude lower for the differentially expressed ExQuest selected genes in comparison to the genes analyzed with known diabesity or metabolism involvement, with many having no literature citations Table . QTL-basin silico data mining focused on gene expression in diabesity-affected tissues and limited to intervals containing diabesity-specific QTL (and thus enriched in genes that contribute to diabesity) is an efficient method to identify genes whose expression is altered in T2D-susceptible mice. This analysis not only demonstrated expression alterations in genes known to be associated with the development of diabesity, but also identified a number of novel genes whose expression changes may contribute to the development of diabesity. Expression changes in genes mapping within TH QTL could be considered as candidates for transcriptional polymorphisms contributing to the associated TH QTL. However, genes selected on the basis of other diabesity QTL that showed transcriptional differences in the TH/B6 comparison are more likely minor QTL in the TH model or symptomatic effects of diabesity rather than a major genetic cause of the TH disease.Our results suggest that Pparg and Lep. This included a ~5-fold increase in leptin expression in TH adipose with no accompanying increase in expression of the leptin receptor (Lepr). Since leptin is responsible for the regulation of food intake [Pparg), both in TH adipose tissue and liver, is consistent with a decrease in adipocyte differentiation often observed in obese states [Pparg agonists are generally associated with promoting insulin sensitization in the context of obesity [As other diabesity studies commonly observe, there were highly significant changes in adipose tissue expression of d intake , this ine states ,8. In ad obesity .Glut2) in adipose, a 2.9-fold decrease in 1-acylglycerol-3-phosphate O-acyltransferase 2 (Agpat2) in adipose, and a 1.2-fold decrease in lipoprotein lipase (Lpl) in liver. Their reduced expression in the TH model might be in response to disease onset.Other genes previously associated with diabesity and showing differential expression in TH vs. B6 tissues included a 28.1-fold decrease of glucose transporter 2 . The prVldlr). As a deficiency in Vldlr has been reported to reduce adipocyte size and obesity in the ob/ob mouse model [Hsd17b9), which localizes to the early body weight QTL Bgeq8 QTL, was elevated 9.5-fold in TH livers. While the function of this gene is not described in the literature, its paralog 11beta-hydroxysteroid dehydrogenase type 1 (Hsd11b1) bidirectionally catalyzes the conversion of cortisol to the inactive metabolite cortisone. Over 150 articles describe an association of Hsd11β1 with diabesity, and an intronic Hsd11b1 polymorphism is associated with obesity and insulin resistance in children [Hsd17b9 paralog in TH liver is a candidate for Bgeq8 remains to be established.Altered expression of a number of ExQuest-selected genes was also found. TH livers showed a 15.6-fold expression increase compared with B6 for the very low-density lipoprotein receptor (se model , this exPnlip) and two paralogs, Pnliprp1 and Pnliprp2, were expressed preferentially in pancreas and mapped very close to the peak of the LOD values for the overlapping diabesity QTL, T2dm2 and Tannidd1 , which maps to the Obq4 obesity QTL, was the most significantly changed of all genes analyzed. It is quite transcriptionally active in control B6 liver but virtually undetectable in TH liver. Trefoils are small, stable secretory proteins expressed in goblet cells in the gastrointestinal mucosa where they stabilize the mucus layer and promote epithelial healing [Tff3 are highly susceptible to colon damage [Tff3 has also been reported to be expressed in the bile ducts of normal human liver and is upregulated in diseased livers [Tff3 expression in TH liver with diabesity remains to be established. healing . Mice den damage , which id livers . HoweverEla2 was ranked by GPR as the gene most significantly changed in the pancreas, with a 55-fold reduction in TH vs. B6 mRNA (Table Ela2 is located within 75 kb of the peak LOD score for the TH QTL Tafat. ExQuest expression profiling suggests that Ela2 expression is biased strongly towards digestive tissues (pancreas, stomach and tongue) of normal mice  encodes a serine protease present in neutrophil endosomal granules, which are known to be important in myelopoiesis . The suin silico candidate genes to those with expression biased to tissues normally affected by this disease, we have shown that sensitive, high-throughput QPCR methods reveal expression changes in novel genes in the TH model. The search within additional diabesity QTL using this technique may facilitate the identification of a limited number of genes that comprise a 'complete' diabesity molecular phenotype. Moreover, this general approach may be an efficient method to identify genes that contribute to complex pathophysiological processes.We have tested the concept that transcription-based ExQuest data mining of genes in QTL-limited intervals is an effective method to identify genes that contribute to the complex genetic disease, diabesity. By limiting the Obesity and T2D QTL on mouse chromosomes 1,4,10,17,19, and X, were identified by a LocusLink search . InformaExQuest ,2 is a g2 asphyxiation, pancreas, adipose, and liver tissues were collected. Each biological replicate sample was processed in parallel. Whole pancreas tissue was collected, placed in 3.5 mL lysis/binding solution from the RNAqueous®-4PCR Kit (Ambion # 1914), homogenized and stored in an ethanol/dry ice bath until the RNA could be extracted. Approximately 25 mg of adipose and liver tissue were collected and stored in 1 mL of RNALater (Ambion # 7020) until RNA was extracted.Three 7 week old, male TH mice (nonfasting blood glucose levels 295 to 433 mg/dl) and their respective T2D-resistant, sex- and age-matched C57BL/6J (B6) controls, were maintained on a 4% fat diet [For pancreatic RNA extraction, we prepared a 5.7 M CsCl solution containing 0.05 M EDTA, pH 7.0. The solution was made RNase free using RNASecure (Ambion # 7005), conforming to manufacturer's recommendations. 5.0 mL SW 55.1 ultracentrifuge tubes were made RNase free by treatment with RNAZap (Ambion # 9780) for five minutes followed by washing with Nuclease-Free water (Ambion # 9932). 2.0 mL of the 5.7 M CsCl solution was added to each tube. 3.0 mL of pancreatic solution was layered carefully on top. The tubes were centrifuged at 36,000RPM for 16–20 hours at 25°C in a SW 55.1 rotor. The supernatant was carefully removed 1.0 mL at a time, using fresh pipette tips. When approximately 1.0 mL remained, the tubes were quickly inverted and dried with filter paper to prevent residual RNases from contact with the RNA pellet at the bottom of the tube. The RNA was resuspended in 2 portions of 150 μL of RNA resuspension solution (Ambion #7010), conforming to manufacturer's recommendations.®-4PCR Kit, conforming to the manufacturer's recommendations.Adipose and liver tissues stored in RNAlater were processed using the RNAqueous® ND-1000 Spectrophotometer. The collected RNA was converted into cDNA via MessageSensor™ RT Kit (Ambion # 1745), conforming to manufacturer's recommendations. The adipose tissue cDNA reaction synthesis contained 350 ng of RNA while pancreas and liver samples utilized 1000 ng of RNA. All RNA was stored at -80°C when it was not being processed.All RNA was subsequently digested with DNAse in accordance with the aforementioned protocol and analyzed for purity using the Agilent Bioanalyzer 2100. RNA concentrations were determined using a NanoDropSequences for the candidate genes were extracted from GenBank and imported into Primer Express software v2.0 from Applied Biosystems, Inc (ABI). All primers were prepared in accordance with universal thermocycling parameters as described for real-time PCR on the ABI 7900HT. The primer sequences were then blasted  to ensur2O, 1.5 μL of 0.5 μM forward and reverse primer solution, and 0.5 μL of cDNA. Pancreas and liver cDNA was diluted 1:10 before addition to the master-mix, while adipose cDNA was diluted 1:3. A 384-well plate format was utilized such that 4 samples × 96 genes were amplified per plate. The plate was sealed with Optical Adhesive Covers (ABI # 4311971) and centrifuged. The samples were assayed on the ABI Prism 7900HT Signal Detection System v2.0 using default conditions, and baseline range values were set from 3 to 10 cycles.Each reaction consisted of 5.0 μl of 2X SYBR Green Master Mix (ABI # 4309155), 3.0 μL of dHThe data were then analyzed using Global Pattern Recognition (GPR) analytical software ,28. In tQPCR, quantitative polymerase chain reaction; QTL, quantitative trait loci; GPR, global pattern recognition; ExQuest, expressional quantification of ESTsACB and DCR conceived of the study and were primarily responsible for its coordination and design. ACB and WIO were responsible for maintenance and execution of software algorithms as well as candidate gene selection. WIO and DJS preformed all tissue extraction and QPCR. CJD and MEM provided supercomputer and database management. JKN provided all mice, animal husbandry, and diabesity technical expertise. ACB, WIO and DCR drafted the manuscript and figures.Gene names and primer sequences used for QPCR.Click here for fileProvides adipose tissue raw Ct values and GPR report.Click here for fileProvides liver raw Ct values and GPR report.Click here for fileProvides pancreas raw Ct values and GPR report.Click here for file


The bsr gene encodes for blasticidin S (BS) deaminase, which has a specific activity upon BS. Therefore, its expression is supposed to be harmless in cells. The work reported on herein consisted of experiments to verify a possible toxicity of bsr on mammalian cells, which include several cell lines and primary cultures.The blasticidin S resistance gene (bsr, namely bsrm, died in five days after the transduction. Meanwhile the control vector LBSN, which expresses bsr, did not provoke cell death. The lethal activity of bsrm was observed only in human keratinocytes and BALB/MK cells among the cell types tested here. Death appears to be mediated by a factor, which is secreted by the BALB/MK transduced cells.Murine keratinocyte BALB/MK and human primary keratinocyte cells transduced with the retroviral vector LBmSN, which has an improved expression system of bsrm gene is toxic to human keratinocytes and BALB/MK cells. It is likely over expression of BS deaminase gene is responsible for the death.By our study we demonstrated that the expression of  Streptomyces griseochromogenes, and has been used as a fungicide against rice blast disease [bsr) was isolated from Bacillus cereus K55-S1 strain [bsr gene possesses only 420 bp [Blasticidin S (BS) is a nucleoside antibiotic isolated from  disease . BS inhi disease . Later, 1 strain ,3. The by 420 bp , thus itbsr could resist against BS at several fold higher concentrations [bsr pair has been used as an efficient selection system for gene transfer experiments in many different cell types.BS antibiotic is highly toxic to mammalian cells; even 2 μg/ml is enough to kill HeLa cells in a few days. However, the cells transfected with trations . Hence, bsr gene (bsrm) resulted in an increase  of bsr gene expression, and consequently, NIH3T3 cells transduced by retroviral vectors could be selected with higher concentrations of BS in just a few days [bsr/BS selection system, no side effects in response to bsr gene expression have been observed.Recently, we reported that some modifications introduced into the non-coding regions of the few days . Even wibsrm. A detailed investigation about the death of keratinocytes, which was mediated by an unknown molecule and secreted by the BALB/MK cells transduced with LBmSN, is discussed below.Using the murine keratinocyte cell line BALB/MK  and huma4 cells/well were incubated in a 24 well plate and 2 days later the media were replaced with a fresh one containing various concentrations of BS for 9 days. A BS concentration of at least 2 μg/ml was necessary to kill the cells in 5 days. At concentrations of BS greater than 8 μg/ml the majority of cells died within 3 days.To determine the range of BS concentrations and the time required for BALB/MK cell death to occur, 1 × 10bsrm gene in BALB/MK cells, the cells were transduced with the LBmSN retroviral vector, which expresses the bsrm and neoR genes, and then incubated with BS. The bsrm gene was obtained by modifying the bsr gene at a non-coding region and, consequently, there was no alteration of the amino acid sequence [R gene) [To verify the effect of sequence . The add[R gene)  were incbsrm gene died in the presence of 8 μg/ml of BS . Thus, the BS antibiotic counteracted the death effect of 3 to 4 × 104 cells in 25 cm2 flasks and the cell death was monitored by optical microscope. The flasks containing a higher number of cells had faster cell death, even if the ratio of virus per cell was maintained constant in all flasks  of LBmSN vector. The transduced cells incubated with 8 μg/ml of BS produced resistant colonies proportionally to the used virus concentration , because of the origin of the BALB/MK cells [The lethal effect provoked by MK cells , were albsrm, the cells were transduced with a ten-fold higher number of viruses than cells. Even in such conditions no cell types suffered with the expression of bsrm.Until 7 days post-infection none of the above cells, which were transduced with LBmSN retroviral vector, did not die, whereas the control cell line BALB/MK died 5 days after the transduction (not shown). As the viral transduction rate is essential to analyze the possible death effect by the expression of bsrm in the cells, those transduced cells were selected with 8 μg/ml of BS from the non-transduced ones that die in 4 days. The selected cells were distributed to two plates, and in one plate the initial concentration of BS was maintained and in from the other plate the BS was removed. During the 7 days of observation no death was observed in both plates (not shown), which confirm the previous result that the bsrm gene is not lethal to those cell types.To ensure the transduction and expression of bsrm and bsr respectively, on BALB/MK and NIH3T3 cells. Transduction of LBSN vector on BALB/MK or NIH3T3 cells did not cause cell death; meanwhile LBmSN caused cell death as expected  is added in the medium before three days after transduction. It was a paradox, since the toxic antibiotic, BS, was able to rescue the murine keratinocyte BALB/MK induced to die by the apparently inoffensive bacterial bsrm gene.An interesting phenomenon of the death of BALB/MK cells expressing bsrm-transduced keratinocytes could be understood as a consequence of inhibition of the death factor (DOKEB) production by BS. Because those transduced keratinocytes could be maintained for long period (at least two months) simply by changing the medium every two days for a fresh one containing 8 μg/ml of BS (not shown). However, we do not know if the inhibition of DOKEB production is caused by the inhibition of protein synthesis by BS or just occupation of the active site of the BS deaminase by BS, and consequently inhibiting the binding of the first target molecule which is a responsible for the induction of the death process. We believe more in the last explanation, because if the analogs of BS were added to the medium at concentrations higher than 1 mM, cell death can be avoided . However, the keratinocytes transduced with LBmSN behaved in an opposite way. In the absence or presence of BS at low concentrations (lower than 2 μg/ml) the transduced cells died, as it was observed with the BALB/MK cells transduced with LBmSN. Additionally, the protection against the death of the BALB/MK cells expressing bsrm by the addition of a lethal concentration of BS was also observed with the human keratinocytes expressing bsrm . Therefore, DOKEB should not be BS deaminase, or even any protein. Further purification and molecular analysis are in progress.vitro that the expression of the reporter gene bsrm has a lethal effect on keratinocytes. However as most of the gene therapy experiments using keratinocytes are carried out ex vivo with retroviral vectors, our finding has a very important meaning. Because the cells transduced with retroviral vector carrying bsrm and selected with BS can survive until the antibiotic is maintained in the medium, but when those cells are returned to the own organism, which has no BS in it, DOKEB will be produced and can provoke serious lesion in the body.In this study we demonstrated only in By this study we also point out the danger of using heterologous genes, in particular those isolated from the microorganisms, in gene transfer and gene therapy experiments without proper controls.bsrm has a lethal effect on the murine BALB/MK cell line and human primary keratinocytes. It is likely over expression of the BS deaminase gene is responsible for the death. The death appears to be mediated by a factor, which is secreted by the BALB/MK transduced cells. By this study we point out the danger of using heterologous genes, in particular those isolated from the microorganisms, in gene transfer experiments without proper controls.We demonstrated in this study that the expression of the reporter gene R), bsr and bsrm, respectively. The LBSN and LBmSN vectors were constructed inserting the bsr and bsrm genes into the Hpa I site of LXSN, which is located in the cloning site [The retroviral vectors used in the present study are based on the Moloney murine leukemia virus: LXSN , LBSN 88 and LBm2. The mouse fibroblast CCL-92 (ATCC) was cultured as above, except that the FBS was replaced with the bovine calf serum (InVitrogen, São Paulo, Brazil). For the culture of the murine keratinocyte BALB/MK cells , EMEM (Biofluids, Rockville, MD) containing 0.05 mM CaCl2, 10 ng/ml of EGF and 10 % FBS was used.The amphotropic retrovirus producing cell clones PA317/LBmSN, PA317/LBSN and PA317/LXSN , the mur3, HEPES 10 mM, pH 7.3, 40 μg/ml garamicine and 10 % FBS at 37°C in a humidified atmosphere with 5 % CO2.The human colorectal carcinoma cell line HCT-8 and the murine melanoma cell line B16F10  were culA primary culture of rat smooth muscle cells was prepared as previously described , digesti4 cells per well with an appropriate medium as mentioned above. In parallel, 1 × 106 of virus producing cells (PA317/LBmSN, PA317/LBSN or PA317/LXSN) were seeded in a 25 cm2 flask. After 24 h, the media from the target cells and the virus producing cells were replaced with a fresh one used for target cells. On the next day, the media of the target cells were replaced with 500 μl of the virus solution collected from the supernatant of the PA317/LBmSN cell culture and filtered in 0.45 μm syringe filter. Polybrene was added to the virus solution at the final concentration of 8 μg/ml. One day after the infection, the media were replaced with a fresh one, maintained in the CO2 incubator and the cells were observed using a microscope everyday.The target cells were seeded on a 24 well plate at 1 × 10In parallel, after two days of the infection, a new set of the transduced cells was split and only 1/10 part of the cells was maintained in the same well. The BS antibiotic was added to the wells at concentrations between 0 to 32 μg/ml. When the cells reached confluence they were split and transferred to two wells of a 12-well plate. To one well, BS was added at the concentration used for selection, and another well was maintained without BS. The cells were observed under the microscope everyday.2 flask containing 2 × 106 cells of the irradiated CCL-92 cells (60 Gy) as a feeder-layer. The medium used for the culture of the keratinocytes was composed of DMEM and Ham's F12 (2:1) containing 10 % FBS, 4 mM glutamine, 50 IU/ml streptomycin- penicillin, 0.18 mM adenine, 5 μg/ml insulin, 5 μg/ml transferrin, 0.4 μg/ml hydrocortisone, 0.1 nM choleric toxin and 20 pM triiodotyronin [2.Normal human keratinocytes from healthy adult volunteers were obtained by biopsy, cut in small pieces and incubated in a trypsin solution (0.05 %) containing 0.01 % EDTA at 37°C for 3 h under constant agitation. Every 30 min the detached cells were transferred to a new 75 cmotyronin . The medFor retroviral transduction, the packaging clones PA317/LBmSN and PA317/LXSN were irradiated with 30 Gy and used as a feeder-layer for the prepared previously keratinocyte cultures.FMB performed experiments with NIH3T3, keratinocytes and smooth muscle cells, and CBS with HCT-8, B16F10 and BALB/MK cells. DT and AKC performed purification and characterization of DOKEB. TRM tested analogs of BS in BALB/MK and BALB/MK/LBmSN cells. MBM participated in the preparation of the human primary keratinocytes. SWH drafted the manuscript and conducted all experiments.