A computationally efficient statistical framework for estimating networks of coexpressed genes is presented that exploits first-order conditional independence relationships among gene expression measurements. Saccharomyces cerevisiae. We demonstrate the biological utility of this approach by showing that a large number of metabolic pathways are coherently represented in the estimated network. We describe a complementary unsupervised graph search algorithm for discovering locally distinct subgraphs of a large weighted graph. We apply this algorithm to our coexpression network model and show that subgraphs found using this approach correspond to particular biological processes or contain representatives of distinct gene families.We describe a computationally efficient statistical framework for estimating networks of coexpressed genes. This framework exploits first-order conditional independence relationships among gene-expression measurements to estimate patterns of association. We use this approach to estimate a coexpression network from microarray gene-expression measurements from  That is, the number of variables of interest is very large (thousands to tens of thousands of genes), yet we have relatively few observations  upon which to base our inferences and interpretations. Recognizing this, many investigators studying quantitative genomic data have focused on the use of either classical multivariate techniques for dimensionality reduction and ordination  or on various types of clustering techniques, such as hierarchical clustering , k-meansustering , self-orustering  and othe [et al. ) can allG = {V,E}, composed of vertices  and edges . We use the terms 'graph' and 'network' interchangeably throughout this paper. The advantage of network models over common clustering techniques is that they can represent more complex types of relationships among the variables or objects of interest. For example, in distinction to standard hierarchical clustering, in a network model any given gene can have an arbitrary number of 'neighbors' (that is n-ary relationships) allowing for a reasonable description of more complex inter-relationships.An alternate mode of representation that has been applied to the study of whole-genome datasets is network models. These are typically specified in terms of a graph, While network models seem to be a natural representation tool for describing complex biological interactions, they have a number of disadvantages. Analytical frameworks for estimating networks tend to be complex, and the computation of such models can be quite hard (NP-hard in many cases ). CompleTwo major classes of network-estimation techniques have been applied to gene-expression data. The simpler approach is based on the notion of estimating a network of interactions by defining an association threshold for the variables of interest; pairwise interactions that rise above the threshold value are considered significant and are represented by edges in the graph, interactions below this threshold are ignored. Measures of association that have been used in this context include Pearson's product-moment correlation  and mutuO(n3). Therefore, the FOCI model is readily computable even for very large datasets.We have developed an analytical framework, called a first-order conditional independence (FOCI) model, that strikes a balance between these two categories of network estimation. Like graphical modeling techniques, we exploit information about conditional independence relationships - hence our method takes into account higher-order multivariate interactions. Our method differs from standard graphical models because rather than trying to account for conditional interactions of all orders, as in Gaussian graphical models, we focus solely on first-order conditional independence relationships. One advantage of limiting our analysis to first-order conditional interactions is that in doing so we avoid some of the problems of power that we encounter if we try to estimate very high-order conditional interactions. Thus this approach, with the appropriate caveats, can be applied to datasets with moderate sample sizes. A second reason for restricting our attention to first-order conditional relationships is computational complexity. The running time required to calculate conditional correlations increases at least exponentially as the order of interactions increases. The running time for calculating first-order interactions is worst case We demonstrate the biological utility of the FOCI network estimation framework by analyzing a genomic dataset representing microarray gene-expression measurements for approximately 5,000 yeast genes. The output of this analysis is a global network representation of coexpression patterns among genes. By comparing our network model with known metabolic pathways we show that many such pathways are well represented within our genomic network. We also describe an unsupervised algorithm for highlighting potentially interesting subgraphs of coexpression networks and we show that the majority of subgraphs extracted using this approach can be shown to correspond to known biological processes, molecular functions or gene families.We used the FOCI network model to estimate a coexpression network for 5,007 yeast open reading frames (ORFs). The data for this analysis are drawn from publicly available microarray measurements of gene expression under a variety of physiological conditions. The FOCI method assumes a linear model of association between variables and computes dependence and independence relationships for pairs of variables up to a first-order  conditioning variable. More detailed descriptions of the data and the network estimation algorithm are provided in the Materials and methods section.On the basis of an edge-wise false-positive rate of 0.001 , the estimated network for the yeast expression data has 11,450 edges. It is possible for the FOCI network estimation procedure to yield disconnected subgraphs - that is, groups of genes that are related to each other but not connected to any other genes. However, the yeast coexpression network we estimated includes a single giant connected component  with 4,686 vertices and 11,416 edges. The next largest connected component includes only four vertices; thus the GCC represents the relationships among the majority of the genes in the genome. In Figure The mean, median and modal values for vertex degree in the GCC are 4.87, 4 and 2 respectively. That is, each gene shows significant expression relationships to approximately five other genes on average, and the most common form of relationship is to two other genes. Most genes have five or fewer neighbors, but there is a small number of genes (349) with more than 10 neighbors in the FOCI network; the maximum degree in the graph is 28 Figure . Thus, aTo assess the biological relevance of our estimated coexpression network we compared the composition of 38 known metabolic pathways Table  to our yp < 0.05; see Materials and methods). Most of the pathways of carbohydrate and amino-acid metabolism that we examined are coherently represented in the FOCI network. Of each of the major categories of metabolic pathways listed in Table We used a pathway query approach to examine 38 metabolic pathways relative to our FOCI network. For each pathway, we computed a quantity called the 'coherence value' that measures how well the pathway is recovered in a given network model . Of the 38 pathways tested, 19 have coherence values that are significant when compared to the distribution of random pathways of the same size , valine, leucine, and isoleucine biosynthesis (10 of 15 genes), methionine metabolism (6 of 13 genes), phenylalanine, tyrosine, and tryptophan metabolism (two subnetworks each of 6 genes). Several coherent subsets of the FOCI network generated by these pathway queries are illustrated in the Additional data file 1.In addition to being consistent with individual pathways, a useful network model should capture interactions between pathways. To explore this issue we queried the FOCI network on combined pathways and again measured its coherence. We illustrate one such combined query based on four related pathways involved in carbohydrate metabolism: glycolysis/gluconeogenesis, pyruvate metabolism, the TCA cycle and the glyoxylate cycle.ICL1 (encoding an isocitrate lyase) and ICL2 (a 2-methylisocitrate lyase).Figure PCK1, DAL7, MDH2, MLS1, ACS1, ACH1, LPD1, MDH1} and the other including {GLO1, GLO2, DLD1, CYB2}. This second set of genes encodes enzymes that participate in a branch of the pyruvate metabolism pathway that leads to the degradation of methylglyoxal -S-lactoyl-glutathione → D-lactaldehyde → D-lactate → pyruvate) [S-lactoyl-glutathione, methyglyoxal is condensed with glutathione [GRX1 (a neighbor of GLO2) and TTR1 (neighbor of CYB2), encode proteins with glutathione transferase activity.In this combined pathway query the TCA cycle, glycolysis/gluconeogenesis, and glyxoylate cycle are each represented primarily by a single two-step connected subgraph . Pyruvate metabolism on the other hand, is represented by at least two distinct subgraphs, one including {yruvate) ,13. In ttathione . InteresFBP1 in the combined query is also interesting. The product of FBP1 is fructose-1,6-bisphosphatase, an enzyme that catalyzes the conversion of beta-d-fructose 1,6-bisphosphate to beta-D-fructose 6-phosphate, a reaction associated with glycolysis. However, in our network it is most closely associated with genes assigned to pyruvate metabolism and the glyoxylate cycle. The neighbors of FBP1 in this query include ICL1, MLS1, SFC1, PCK1 and IDP3. With the exception of IDP3, the promoters of all of these genes (including FBP1) have at least one upstream activation sequence that can be classified as a carbon source-response element (CSRE), and that responds to the transcriptional activator Cat8p [FBP1 in the combined pathway query we find that ACS1, IDP2, SIP4, MDH2, ACH1 and YJL045w have all been shown to have either CSRE-like activation sequences and/or to be at least partially Cat8p dependent [et al. [The position of or Cat8p . This seor Cat8p . Consideependent . The ass [et al. , suggestet al. [YER053c as among the set of genes whose expression levels changed in TCA cycle mutants.The inclusion of a number of other genes in the carbohydrate metabolism subnetwork is consistent with independent evidence from the literature. For example, McCammon et al.  identifiLSC1, PTR2, PAD1, OPT2, ARO10 and PSP1 has no clear known relationship.Although many of the associations among groups of genes revealed in these subgraphs can be interpreted either in terms of the query pathways used to construct them or with respect to related pathways, a number of association have no obvious biological interpretation. For example, the tail on the left of the graph in Figure vis-à-vis this prior knowledge. Conversely, one might want to find interesting and distinct subgraphs within the FOCI network without the injection of any prior knowledge and ask whether such subgraphs correspond to particular biological processes or functions. To address this second issue we developed an algorithm to compute 'locally distinct subgraphs' of the yeast FOCI coexpression network as detailed in the Materials and methods section. Briefly, this is an unsupervised graph-search algorithm that defines 'interestingness' in terms of local edge topology and the distribution of local edge weights on the graph. The goal of this algorithm is to find connected subgraphs whose edge-weight distribution is distinct from that of the edges that surround the subgraph; thus, these locally distinct subgraphs can be thought of as those vertices and associated edges that 'stand out' from the background of the larger graph as a whole.The analysis of metabolic pathways described above provides a test of the extent to which known pathways are represented in the FOCI graph. That is, we assumed some prior knowledge about network structure of subsets of genes and asked whether our estimated network is coherent p-values less than 10-5 . This indicates that most locally distinct subgraphs are highly enriched with respect to genes involved in particular biological processes or functions. Members of the 21 largest locally distinct subgraphs are highlighted in Figure We constrained the size of the subgraphs to be between seven and 150 genes, and used squared marginal correlation coefficients as the weighting function on the edges of the FOCI graph. We found 32 locally distinct subgraphs, containing a total of 830 genes ; ribosome biogenesis and assembly (subgraph C); response to stress and carbohydrate metabolism (subgraph K); and sporulation (subgraph N). Several of these subgraphs show very high specificity for genes with particular GO annotations. For example, in subgraphs A and B approximately 97% (32 out of 33) and 95.5% (64 out of 67) of the genes are assigned the GO term 'protein biosynthesis'.Subgraph P is also relatively large and contains many genes with roles in DNA replication and repair. Similarly, 21 of the 34 annotated genes in Subgraph F have a role in protein catabolism. Three medium-sized subgraphs  are strongly associated with the mitotic cell cycle and cytokinesis. Other examples of subgraphs with very clear biological roles are subgraph R (histones) and subgraph Z . Subgraph X contains genes with roles in methionine metabolism or transport.STI1, SIS1, HSC82, HSP82, AHA1, SSA1, SSA2, SSA4, KAR2, YPR158w, YLR247c. The other group contains genes primarily involved in carbohydrate metabolism. These two subgroups are connected to each other exclusively through HSP42 and HSP104.Some locally distinct subgraphs can be further decomposed. For example, subgraph K contains at least two subgroups. One of these is composed primarily of genes encoding chaperone proteins: YRF1-2, YRF1-3, YRF1-4, YRF1-5, YRF1-6) correspond to ORFs encoding copies of Y'-helicase protein 1 [YBL113c, YEL077c, YHL050c, YIL177c, YJL225c, YLL066c, YLL067c, YPR204w) assigned to this subgraph also encode helicases. This helicase subgraph is closely associated with subgraph P, which contains numerous genes involved in DNA replication and repair  are members of the seripauperin gene family [Three of the locally distinct subgraphs - Q, W and CC - are composed primarily of genes for which there are no GO biological process annotations. Interestingly, the majority of genes assigned to these three groups are found in subtelomeric regions. These three subgraphs are not themselves directly connected in the FOCI graph, so their regulation is not likely to be simply an instance of a regulation of subtelomeric silencing . Subgraprotein 1 . Eight ae family . Anothere family .YAR010c, YBL005w-A, YJR026w, YJR028w, YML040w, YMR046c, YMR051c) all of which are parts of Ty elements, encoding structural components of the retrotransposon machinery [r ~ 0.62). Despite this, the local distribution of edge weights in FOCI graph is such that this group is highlighted as a subgraph of interest. Locally strong subgraphs such as these can also be used as the starting point for further graph search procedures. For example, querying the FOCI network for immediate neighbors of the genes in subgraph FF yields three additional ORFs - YBL101w-A, YBR012w-B, and RAD10. Both YBL101w-A and YBR012w-B are Ty elements, whereas RAD10 encodes an exonuclease with a role in recombination.As a final example, we consider subgraph FF, which is composed of seven ORFs . with a threshold value of ± 0.5. The resulting relevance network has 13,049 edges and a GCC with 1,543 vertices and 12,907 edges. The next largest connected subgraph of the relevance network has seven vertices and seven edges. There are a very large number of connected subgraphs  that are composed of pairs or singletons of genes.We estimated a relevance network for the same 5007-gene dataset used to construct the FOCI network. The scoring function employed was To compare the performance of the relevance network with the FOCI network we used the pathway query approach described above to test the coherence of the 38 metabolic pathways described previously. Of the 38 metabolic pathways tested, nine have significant coherence values in the relevance network. These coherent pathways include: glycolysis/gluconeogenesis, the TCA cycle, oxidative phosphorylation, ATP synthesis, purine metabolism, pyrimidine metabolism, methionine metabolism, amino sugar metabolism, starch and sucrose metabolism. Two of these pathways - amino sugar metabolism and starch and sucrose metabolism - are not significantly coherent in the FOCI network. However, there are 12 metabolic pathways that are coherent in the FOCI network but not coherent in the relevance network. On balance, the FOCI network model provides a better estimator of known metabolic pathways than does the relevance network approach.et al. [et al. [et al. [et al. and the website that accompanies their report [To provide a common basis for comparison with hierarchical clustering and Bayesian networks, we explored the dataset of Spellman et al.  which in [et al.  analyzed [et al.  used ther report . For theet al. [On the basis of hierarchical clustering analysis of the 800 cell-cycle-regulated genes, Spellman et al.  highlighet al.  for descet al. [et al. [Applying our algorithm for finding locally distinct subgraphs to the FOCI graph based on these same data  we found 10 locally distinct subgraphs. Seven of these subgraphs correspond to major clusters in the hierarchical cluster analysis . The FOCI analysis additionally shows that this set of genes is linked to another subgraphs that includes AGA2, STE2, MFA1, MFA2 and GFA3. This second set of genes are also involved in conjugation, sexual reproduction, and pheromone response. AGA1 and AGA2 form the bridge between these two subgraphs . These Because hierarchical clustering constrains relationships to take the form of strict partitions or nested partitions, this type of analysis seems best suited to highlight the overall coarse structure of co-regulatory relationships. The FOCI method, because it admits a more complex set of topological relationships, is well suited to capturing both global and local structure of transcriptional interactions.et al. [et al. [STE2-MFA2, CTS1-DSE2(YHR143w), OLE1-FAA4, KIP3-MSB1, SHM2-GCV2, DIP5-ARO9 and SRO4-YOL007c. All of these relationships, with the exception of SRO5-YOL007c, are present in the FOCI analysis of the same data.Graphical models, like the FOCI method, exploit conditional independence relationships to derive a model that can be represented using a graph or network structure. Unlike the FOCI model, general graphical models represent a complete factorization of a multivariate distribution. In the case of Bayesian networks it is also possible to assign directionality to the edges of the network model. However, these advantages come at the cost of complexity - Bayesian networks are costly to compute - and generally this complexity scales exponentially with the number of vertices (genes). The estimation of a FOCI network is computationally much less complex than the estimation of a Bayesian network. Both methods allow for a richer set of potential interactions among genes than does hierarchical clustering. We therefore expect that both methods should be able to highlight biologically interesting interactions, at both local and global scales. Friedman et al.  analyzed [et al.  and highet al. demonstrated an interaction between ASH1 and FAR1, both of which are known to participate in the mating type switch in yeast. This relationship is absent in the FOCI network. Similarly, the relationship between AGA1 and AGA2 that is highlighted in the FOCI analysis does not appear in the multinomial Bayesian network analysis.Comparisons of the local topology of each network, based on examining the edge relationships for a number of query genes, suggests that the FOCI and Bayesian networks are broadly similar. There are of course, examples of biologically interpretable interactions that are present in the FOCI analysis but not in the Bayesian network and vice versa. For example, using a multinomial model, Friedman per se. While the clusters and patterns of coexpression summarized by the FOCI network may result from particular regulatory dynamics, no causal hypothesis of regulatory interaction is implied by the network.As with all analytical tools, careful consideration of the assumptions underlying the FOCI network method is necessary to understand the limits of the inferences one can draw. For example, our current framework limits consideration to linear relationships as measured by correlations and partial correlations. These assumptions may be relaxed, allowing for other types of distributions and relationships among variables , but there is an inevitable trade-off to be made in terms of computational complexity and statistical power. However, as seen in our analysis, many biologically interesting relationships among gene expression measures appear to be approximately linear. Biologically speaking, it is important to keep in mind that the graphs resulting from a FOCI analysis of gene-expression measurements should properly be considered coexpression or co-regulation networks and not genetic regulatory networks Biology demands that the analytical tools we use for functional genomics should be able to capture and represent complex interactions; practical considerations stemming from the magnitude and scope of genomic data require the use of techniques that are computable and relatively efficient. The FOCI framework we have used for representing genomic coexpression patterns in terms of a weighted graph satisfies both these constraints. FOCI networks are readily computable, even for very large datasets. Comparisons with known metabolic pathways show that many key biological interactions are captured by FOCI networks, and the algorithm we provide for finding locally distinct subgraphs provides a mechanism for discovering novel associations based on local graph topology. The subgraphs and patterns of interactions that we are able to demonstrate based on such analyses are strongly consistent with known biological processes and functions, indicating that the FOCI network method is a powerful tool for summarizing biologically meaningful coexpression patterns. Furthermore, the kinds of interactions captured by network analysis are typically more natural than the clustering family of analyses where biased and unstable results can be forced by the algorithm. Secondary analysis based on the network properties also reveal additional subtle structure. For example, our procedure for finding locally distinct subgraphs reveals associated genes whose pairwise interactions may be globally weak but relatively strong compared to their local interactions. While the results reported here focus on the analysis of gene expression measurements, the FOCI approach can be applied to any type of quantitative data making it a generally suitable technique for exploratory analyses of functional genomic data.The approach we employ to estimate coexpression networks is based on a general statistical technique we have developed for representing the associations among a large number of variables in terms of a weighted, undirected graph. The technique is based on the consideration of so-called 'first-order' conditional independence relationships among variables, hence we call the graphs that result from such analyses first-order conditional independence, FOCI, networks. The network representation that results from a FOCI analysis also has a dual geometrical interpretation in terms of proximity relationships defined with respect to the geometry of correlations and partial correlations. We outline the statistical and geometrical motivations underlying our approach below.G = {V,E}, where the vertex set, V, represents the variables of interest and the edge set, E, represents interactions among the variables. eij is an edge in G, if and only if there is no other variable in the analysis, k(k ≠ k ≠ k) such that  or , whereA FOCI network is a graph,  is a modified partial correlation between i and j conditioned on k.  takes values in the range -1 ≤  ≤ 1.  is approximately zero when i and j are independent conditional on k.  is positive when the marginal correlation, ρij, and the standard partial correlation, ρij.k, agree in sign, and is negative otherwise. Cases where the marginal and conditional correlations are of opposite sign are examples of 'Simpson's paradox', which usually indicates that there is a lurking or confounding effect of the conditioning variable . If i and j are conditionally independent given k we write this as (i ⊥ j|k). Using an information theoretic interpretation suggested by Lauritzen [i ⊥ j|k) implies that if we observe the variable k, there is no additional information about i that we gain by also observing j (and vice versa). Because the edges of the FOCI network indicate pairs of variables that are not conditionally independent, one can interpret the FOCI graph as a summary of all the pairwise interactions that can not be 'explained away' by any other single variable in the analysis.While true biological interactions may sometimes lead to inverted conditional associations, their interpretation can be complicated; therefore in the analysis presented above, we did not connect edges when the relationships became inverted. However, one can also keep such edges for subsequent analysis if there is reasonable functional justification. When such sign-reversed edges are ignored, we will call this the sign-restricted FOCI network. This definition means that variables auritzen , the staUnlike standard graphical models, a FOCI network does not represent a factorization of a multivariate distribution into the product of simpler distributions. However, below we show that a sign-restricted FOCI graph has a unique geometric interpretation in terms of proximity relationships in the multidimensional space that represents the correlations among variables. This geometric interpretation suggests that the FOCI model should be a generally useful approach for exploratory analyses of very high-dimensional datasets.Our FOCI approach is similar to a framework developed by de Campos and Huete  for estiAbove we described the FOCI network model in statistical terms. Here we provide a geometrical interpretation of FOCI graphs. We show that a FOCI network is equivalent to a proximity graph of the variables of interest . More specifically, we demonstrate that a sign-restricted FOCI network is a 'Gabriel graph' in the geometric space that represents the relationships among the variables.B denote an open n-sphere centered at the point x with radius r, and let d denote the Euclidean distance function. Given a set of points, P = {p1 p2, ..., pn}, in an n-dimensional Euclidean space,  is an edge in the Gabriel graph if no other point, pk  in P falls within the diameter sphere defined by B((pi = pj)/2, d/2). That is, pi and pj are connected in the Gabriel graph if no other point falls within the sphere that has the chord pi, pj as its diameter [A Gabriel graph, introduced by Gabriel and Sokal , is a tydiameter .n-dimensional hypersphere (where n is the number of observations). In this representation, the correlation between two random variables, x and y, is given by the cosine of the angle between their vectors. We will refer to this construction as the 'correlational hypersphere'. The partial correlation between x and y given z is equivalent to the cosine of the angle between the residual vectors obtained by projecting x and y onto z. The vectors x, y and z form the vertices, A, B, and C, of a spherical triangle on that hypersphere with associated angles γ, λ, and φ. Then, ρxy.z = cos(φ), ρxz.y = cos(λ), and ρyz.x = cos(γ) [ρxy.z = 0 for the multivariate normal, is obtained when cos(φ) = 0 . The set of z vectors that satisfy this condition defines a circle  on the hypersphere whose diameter is the spherical chord between x and y. If the projection of z onto the hypersphere lies outside of this circle then ρxy.z is positive, inside the circle ρxy.z is negative .One can represent random variables as vectors in the space of the observations . In = cos(γ) . Given ti and j only if no third variable falls within the diameter sphere defined by i and j on the correlational hypersphere, or by the diameter sphere defined by i and -j when rij < 0 . This is the same criteria of proximity that defines a Gabriel graph. A FOCI graph is therefore a summary of relative proximity relationships among the variables of interest, defined with respect to the geometry of correlations when restricted to the cases when the partial correlation signs are consistent with the marginal correlations.The sign-restricted FOCI network construction corresponds to the graph obtained by connecting variables A simple algorithm for estimating a network based on first-order conditional independence relationships is described below. The results of this algorithm can be represented as a graph where the vertices represent the variables of interest (genes) and the edges represent interactions among variables that show at least first-order conditional dependence. A library of functions for estimating FOCI networks, implemented in the Python programming language, is available from the authors on request.We use vanishing partial correlations ,36 to teEstimate marginal associations. For a set of p variables, indexed by i and j, calculate the p × p correlation matrix, C, where Ci,j = corr for all i, j; i = 1...p, j = 1...p.1. Construct saturated graph. Construct a p × p adjacency matrix, G. Let Gi,j = 1 for all i, j.2. Prune zero-order independent edges. For each pair of variables, , if Ci,j <Tcrit  <Tcrit), where Tcrit is a threshold value for determining marginal/conditional independence (see below), then set Gi,j = 0. G defines a marginal independence graph.3. Estimate first-order relationships. For each pair of variables  in G calculate , the minimum partial correlation between i and j, conditioned on each of the other variables in the analysis taken one at a time.  for all k such that i ≠ k and j ≠ k and  and  are both edges in G.  is the sample modified partial correlation coefficient as defined in equation (1).4. Prune first-order independent edges. If  <Tcrit  or with weights defined by some function of corr or . If we assume multivariate normality we can use Fisher's z-transformation [Tcrit for a given edge-wise false-positive rate. Alternatively, one can define Tcrit by other methods such as via permutation analysis to define a null distribution for . While the FOCI approach requires that one define a critical threshold for determining conditional independence, this threshold is in theory a function of the sample size and the null distribution of  rather than the somewhat fuzzier distinction between 'strong' and 'weak' correlation that most pairwise network estimation approaches require.The resulting adjacency matrix ormation  to normaSaccharomyces cerevisae. The data used in our analysis are drawn from publicly available microarray measurements of gene expression described in DeRisi et al. [et al. [et al. [2-transformed, duplicate and missing data were removed and any ORFs listed as 'dubious' in the Saccharomyces Genome Database as of December 2003 were filtered out. The final dataset consisted of expression measurements for 5,007 ORFs represented by 87 microarrays , making [et al. , have be [et al. ,10,45. Tp < 0.05). While a majority of the univariate distributions are approximately normal, a significant proportion of the trivariate distributions are clearly not multivariate normal. As a crude test of linearity for bivariate relationships we calculated linear regressions for 10,000 random pairs of gene expression measures (randomly choosing one of the pair as the dependent variables), and performed runs tests [p < 0.05). We therefore conclude that the assumption of quasi-linearity is valid for a large number of the pairwise relationships.As noted above, zero partial correlations are exactly equivalent to conditional independence only for multivariate normal distributions. However, from the perspective of exploratory analyses, the more important assumption is that the relationships among the gene expression measures are predominantly linear. We tested each of these assumptions as follows. We used a Cramer-von Mises statistic  to test ns tests  for randTcrit, for these data we considered both permutation tests and false-positive rates based on asymptotic expectations for the distribution of first-order partial correlations (see above). Permutation tests were carried out by independently randomizing the values for each gene expression variable such that each gene had the same mean and variance as its original observation vector, but both the marginal and partial correlations had an expected value of zero. We then sampled 1,000 such randomized variables and examined the distribution of  for every pair of variables in this sample. For p ≤ 0.001 the permutation test indicates a value of Tcrit ~ 0.3. The asymptotic threshold for p ≤ 0.001 based on Fisher's z-transform is Tcrit ~ 0.3. We used the slightly more conservative value of Tcrit ~ 0.34.Given these observations, in order to define an appropriate partial correlation threshold, We used 38 metabolic pathways as documented in KEGG release 29.0, January 2004 ,49 to teH, is two-step connected in the graph G if no vertex in H is more than two edges away from at least one other element of H. Given a set of genes assigned to a pathway (the query genes), we computed the set of two-step connected subgraphs for the query genes in the GCC of our yeast coexpression network. This procedure yields one or more subgraphs that are composed of query (pathway) genes plus non-query genes that are connected to at least two pathway genes. We used two steps as a criterion for our pathway queries because our estimate of the distribution of path distances  whose vertex set completely overlaps P . For cases in which the query pathway is less than perfectly represented in the estimated network we measure the degree of coherence as |FPmax ∩ P| / |P|). We refer this ratio the 'coherence value' of the pathway P in the network of interest. However, we note that in a completely connected graph , every possible pathway query would be maximally coherent but so would any random set of genes. It is therefore necessary to compare the coherence of a given pathway to the distribution of coherence values for random pathways composed of the same number of genes drawn from the same network. We estimated this distribution by using a randomization procedure in which we used 1,000 replicate random pathways to estimate the distribution of coherence values for pathways of different sizes. In Table Suppose we have a set of query genes from a known pathway denoted as We describe an algorithm for extracting a set of 'locally distinct' subgraphs from an edge-weighted graph. We assume that the edge-weights of the graph are measures of the strength of association between the variables of the interest. We define a locally distinct subgraph as a subgraph in which all edges within the subgraph are stronger than edges that connect subgraph vertices to vertices not within the subgraph. Such subgraphs are 'locally distinct' because they are defined not by an absolute threshold on edge strengths, but rather by a consideration of the local topology of the graph and the distribution of edge weights. We describe an algorithm for finding locally distinct subgraphs below.G = {V, E} and w:E → R be an edge-weighted graph where w(e) is the edge weight function, and |V| = p and |E| = q. Define an ordering on E, O(E) = , such that w(ei) ≥ w(ej) for all i ≤ j . Let G(τ)= {V, E(τ)} be a subgraph of G obtained by deleting all edges, e, such that w(e) <eτ. G(τ) an edge-level graph. Also let  denote the k connected components of G(τ). Let Ω = C1 ∪ C2 ∪ … Cn. Define Lα,ζ = {l1,l2,...,lm} where li ⊆ Ω, li ∩ lj =  (i ≠ j) and α ≤|li|≤ ζ. That is, Lα,ζ is a collection of disjoint subgraphs of G, where every li is a connected component of some G(τ) and the size of li is between α and ζ. We call the elements of Lα,ζ the α,ζ-constrained locally distinct subgraphs of G. We say Lα,ζ is optimal if |li ∪ lj … lm| is maximal and |Lα,ζ| is minimal. Our goal is to find the optimal Lα,ζ for the graph G given the constraints α and ζ. A simple algorithm for calculating the Lα,ζ is as follows:Let L ← , i = 01. let i ≤ q:2. while G(i) and Ci3.  calculate  in Ci:4.  for  :5.   if l in L:6.    for  :7.     if L ← L - {l}8.      9.     i = i + 110. Lα,ζ ← L11. i, we calculate the connected components of the edge-level graph, G(i), and add those components which satisfy the size constraints to the candidate list L. Lines 6-8 of the algorithm serve to eliminate from L any non-maximal components.The algorithm is straightforward. At each iteration, α = 7, ζ = 150. The subgraph search given these constraints yielded 32 locally distinct subgraphs  of the estimated yeast FOCI network and additional examples of coherent subgraphs of the FOCI network generated by querying with known metabolic pathwaysClick here for additional data fileA table detailing each of the 32 locally distinct subgraphs generated from the yeast FOCI network via the unsupervised graph search algorithm described in the textClick here for additional data file


To describe immune and endocrine responses in severe hymenoptera hypersensitivity requiring venom immunotherapy (VIT) during in vitro fertilization (IVF).A 39-year old patient was referred for history of multiple miscarriage and a history of insect sting allergy. Four years earlier, she began subcutaneous injection of 100 mcg mixed vespid hymenoptera venom/venom protein every 5–6 weeks. The patient had one livebirth and three first trimester miscarriages. Allergy treatment was maintained for all pregnancies ending in miscarriage, although allergy therapy was discontinued for the pregnancy that resulted in delivery. At our institution ovulation induction incorporated venom immunotherapy (VIT) during IVF, with a reduced VIT dose when pregnancy was first identified. Serum IgE was monitored with estradiol during ovulation induction and early pregnancy. Response to controlled ovarian hyperstimulation was favorable while VIT was continued, with retrieval of 12 oocytes. Serum RAST (yellow jacket) IgE levels fluctuated in a nonlinear fashion (range 36–54%) during gonadotropin therapy and declined after hCG administration. A healthy female infant was delivered at 35 weeks gestation. The patient experienced no untoward effects from any medications during therapy.Our case confirms the safety of VIT in pregnancy, and demonstrates RAST IgE can remain <60% during IVF. With proper monitoring, VIT during IVF can be safe and appropriate for selected patients and does not appear to adversely affect blastocyst implantation, early embryo development or perinatal outcome. Further studies will be needed to develop VIT guidelines specifically applicable to IVF. Insect sting allergies affect approximately 3% of the general population, and patients with insect sting allergy during pregnancy are generally advised to continue venom immunotherapy (VIT). However, there have been no descriptions of VIT during infertility therapy despite increased utilization of the advanced reproductive technologies . In this4P1031 was referred for evaluation and management of recurrent pregnancy loss. Medical history was significant for known carrier state for β-thalassemia. Mild hypothyroidism had been diagnosed in 2002 with immediate initiation of replacement therapy. The patient was a non-smoker, in good general health and had no gynecologic complaint. BMI was 21.7 kg/m2. In 1997, she experienced a severe hypotensive anaphylactic reaction following a yellow jacket sting (Vespula spp.) resulting in a full allergy work-up. The patient began subcutaneous injection of 100 mcg mixed vespid hymenoptera venom/venom protein  every 5–6 weeks, which was well tolerated.A 39 year-old Caucasian GAll four conceptions were established without medical assistance, involved the same partner, and were achieved after the hymenoptera hypersensitivity diagnosis. The initial pregnancy occurred three years before presentation and resulted in a first trimester spontaneous abortion. No adjustment was made to the allergy injection regimen during that pregnancy. Fetal cardiac activity was initially present, but was lost at 10 weeks' gestation for unknown reasons. No curettage was performed.One year later, a second pregnancy was established but for this pregnancy hymenoptera venom therapy was discontinued when pregnancy was first recognized (~6 weeks). A 3170 g female infant was delivered vaginally at 40 1/2 weeks' gestation. In 2001 and 2002, the patient established two additional pregnancies and hymenoptera therapy was maintained at 5–6 week intervals for both; both resulted in first trimester spontaneous abortions. For these miscarriages, dilation and curettage was undertaken but no karyotype was performed and no cause for the losses was identified.At our institution, euthyroid status was verified, the thalassemia carrier state was confirmed, and we identified a new homozygous A223V mutation at the methyltetrahydrofolate reductase (MTHFR) locus. Folic acid intake was immediately increased to 800 mcg/d, although a baseline serum homocysteine level was not measured. Factor V Leiden, protein S, protein C, and other coagulation tests were normal, as were karyotypes obtained from both partners. Anticardiolipin, antiphospholipid and antiovarian antibody titres were all negative. However, transvaginal saline uterine sonography revealed a uniform 5 mm echodense lesion consistent with an endometrial polyp. Outpatient hysteroscopic polypectomy was performed without complication. After discussing various infertility therapies and associated success rates given her age, the patient elected to undergo IVF.®, Ferring Pharmaceuticals Inc.; Tarrytown, NY USA and 300 IU/d Gonal-F®, Serono Labs; Norwell, MA USA). Pre-treatment pituitary downregulation was achieved via 5 u/d leuprolide acetate and was continued × 3 d after gonadotropin therapy commenced. No alteration was made in the patient's allergy injection sequence during ovulation induction , and serum yellow jacket RAST IgE measurements were obtained via commercial fluoroimmunoassay including positive and negative controls . While absolute IgE levels remained <0.35 kU/l throughout therapy, percentage IgE results were variable and these data are summarized in Figure In March 2003, the patient began programmed ovarian hyperstimulation using a combined recombinant-FSH+hMG protocol  was started on the day of oocyte retrieval. On post-fertilization day three, the ultrasound-guided transfer of four embryos was performed. Immediately following embryo transfer, the patient was placed on oral aspirin (81 mg/d) and subcutaneous heparin . Luteal phase support was administered as daily 50 mg IM progsterone in oil injections.On cycle day 10, subcutaneous hCG  was given  with serth gestational week. Immunology and perinatology consultants agreed with reduced dose allergy protocol through the third trimester, and hymenoptera venom protein extract (75 mcg) treatment was maintained to 32 weeks gestation. The patient experienced no untoward reaction or hypersensitivity to gonadotropins, VIT, or supplementary progesterone during therapy.Two weeks after embryo transfer, serum hCG was 72 mIU/ml. On May 5, 2003, transvaginal ultrasound confirmed a single intrauterine pregnancy with fetal cardiac rate at 126/min. Progesterone was discontinued at the 10At 32 weeks, obstetrical sonogram suggested reduced amniotic fluid levels and the patient was given intramuscular betamethasone (12 mg/d × 2 days) and placed on bedrest. At this point allergy injections were discontinued since the patient was not outdoors and risk for insect sting was regarded as low. Intravenous oxytocin was started at 35 weeks due to oligohydramnios and resulted in vaginal delivery of a 2495 g female infant. Mother and baby were discharged home after an uncomplicated two-day postpartum course. Allergy injections resumed (100 mcg every 5–6 weeks) when breastfeeding was completed three months later.Overall, the incidence of allergy to insect stings is ~3% in adults , yet theAlthough continuation of allergy therapy during pregnancy is generally recommended , the inti.e., reduction of miscarriage risk).Evaluation of this patient with a history of multiple spontaneous abortions identified additional factors which might contribute to a poor reproductive outcome. Specifically, endometrial polyps  and homoIt has been hypothesized that early production of IL-10 associated with VIT may induce T-cell anergy, dampening T helper type 2 response and resulting in a T helper type 1 dominant cytokine response. As the role of T helper 1 type immune response in blastocyst implantation becomes more completely characterized, T helper type 1 function may prove to be important in early placental dysfunction or recurrent pregnancy loss . These pThe authors declare that they have no competing interests.ESS was the principal physician and coordinated the research. SCC, CRK and MP edited the manuscript. MJT was chief embryologist and edited the manuscript.


Journal of Autoimmune Diseases is created as an independent open access journal. In addition to the obvious advantages of the open access, the Journal will practice a double-blind reviewing of the manuscripts, which means that both the reviewers and the authors remain anonymous to each other. We believe that such a policy will reduce the influence of personal and other non-scientific factors on the reviewer's decision making.A new online  Autoimmune diseases are common and can affect virtually every organ in the body. They range from organ specific diseases such as thyroiditis or type 1 diabetes to life-threatening multi-system diseases such as systemic lupus erythematosus and the systemic vasculitides. Clinicians from every field of medicine may encounter these patients and both generalists and specialists need to keep up to date with clinical and experimental developments in autoimmunity. The advent of targeted therapies for example the anti-TNF-α agents for the treatment of rheumatoid arthritis exemplify the application of basic science research that can lead to effective therapies.Journal of Autoimmune Diseases. There are several journals devoted to autoimmune diseases already, with at least four having the word "autoimmunity" in the title. In addition, a number of immunological journals and journals that specialise in particular diseases publish papers on autoimmunity. So is there a need for yet another journal?Inspired by the publisher, BioMed Central, a group of enthusiastic professionals gathered to launch the Journal of Autoimmune Diseases certainly thinks so. This Journal is one of the new breed of online-only journals which are proving extremely successful. But it is not the magic word "internet' that makes the difference. Two distinct features provided by, BioMed Central and the Editorial Board offer special advantages to readers and authors.The Editorial Board of Journal of Autoimmune Diseases is universally and freely available online to everyone, its authors retain copyright, and it is archived in internationally recognised free repositories such as PubMed Central [The aim of publishing is to share information with the community. Authors are also keen to know that their work is being read. One of the syndromes of authorship has been the little card politely requesting a reprint. This card is sometimes forgotten or filed in the circular filing cabinet thus inducing considerable guilt both in the paper's author and the recipient. The era of reprint requests may be drawing to a close since most of the journals can now be found on-line. However, the access to a paper on-line often requires a subscription or a purchase of an individual article ("pay-per-view"). The high publication costs of printed journals deprive their publishers of the generosity of complete free access, but this tends to limit the dissemination of research. An alternative approach provided by BioMed Central is to make access totally free for everybody, as part of their Open Access policy . It mean Central ; e-Depot Central ; Potsdam Central  and INIS Central .Journal of Autoimmune Diseases enables scientists from countries and institutions with limited funds to read the same material as wealthier ones [ier ones . This haier ones .Journal of Autoimmune Diseases aims to provide a high standard of double-blinded peer review, in which the reviewer's name is not disclosed to the authors, and the authors remain anonymous to the reviewers. A few journals already use this system, so why do we believe that this will benefit the scientific community?The We have all come across situations where an honest, laborious piece of work was hard to publish. Some journals reject up to 95% of manuscripts , and thipersonally, a good paper is rejected or additional possibly unnecessary experiments are required which will take a year or two. Unfair rejection can occur due to a conflict of interest, be it personal or financial. For instance, an individual may be rejected on the basis of ethnicity [It is the nature of human beings that if a reviewer does not like you thnicity , so may An unfair acceptance takes place when the reviewer is benevolent to a weak paper that has an outstanding name as the last author. Some authors may successfully exploit this weakness of a reviewer's human being by placing the famous name intentionally. This results in a ghost authorship of which the celebrity may be unaware! One of us remembers an anecdotal conversation he heard a few years ago in one of the universities:Dorit (a secretary to the Professor): Robert? There is a new paper of yours I see on Medline. Do you wish me to update your publication list?Professor: Who are the other authors?(Dorit reads 20 last and first names)Professor: Hm-m-m...I don't know any of these people. What is the title there?(Dorit reads the title)Professor: I don't remember even discussing anything like that or being consulted...Dorit: Should I ignore it then?Professor: Could you print it out for me? I will read it first. If the paper is good enough...Well, then I'll have nothing to do but to add it to my list of publications.What are the remedies then? Two policies that could help to overcome these difficulties are completely open peer review and completely anonymous, or double-blind peer review. In open peer review , the revJournal of Autoimmune Diseases – we all need that important first paper to get going. The Editorial Board has been carefully selected from leading authorities in their own fields to help achieve our ambitious aim of developing a high class international journal that is accessible by all who have internet access and we look forward to receiving your submissions.We hope to attract clinical and basic science reports from leading and innovative authors. We call for established authors to publish with us in order to be easily accessible by all other scientists, to stop fighting with the windmills of the elite journals, to write detailed and clear papers in the unlimited space provided by internet. We would also strongly encourage more junior authors who are making their way in this field to consider publishing high quality science in the


Prior literature has shown that physicians with healthy personal habits are more likely to encourage patients to adopt similar habits. However, despite the possibility that promoting medical student health might therefore efficiently improve patient outcomes, no one has studied whether such promotion happens in medical school. We therefore wished to describe both typical and outstanding personal health promotion environments experienced by students in U.S. medical schools.We collected information through four different modalities: a literature review, written surveys of medical school deans and students, student and dean focus groups, and site visits at and interviews with medical schools with reportedly outstanding student health promotion programs.We found strong correlations between deans' and students' perceptions of their schools' health promotion environments, including consistent support of the idea of schools' encouraging healthy student behaviors, with less consistent follow-through by schools on this concept. Though students seemed to have thought little about the relationships between their own personal and clinical health promotion practices, deans felt strongly that faculty members should model healthy behaviors.Deans' support of the relationship between physicians' personal and clinical health practices, and concern about their institutions' acting on this relationship augurs well for the role of student health promotion in the future of medical education. Deans seem to understand their students' health environment, and believe it could and should be improved; if this is acted on, it could create important positive changes in medical education and in disease prevention. Our purpose was to describe both typical and outstanding personal health promotion environments experienced by medical students in U.S. medical schools. Our interest in health promotion among medical students was based on compelling data showing that physicians who have healthy personal habits are more likely to encourage patients to adopt related habits . HoweverWe collected information through four different modalities: a literature review, a written survey of medical school Deans and students, focus groups of preclinical and clinical medical students and dean, and site visits at and interviews with medical schools with reportedly outstanding student health promotion programs.5–7 Methodology for gathering medical student data in HD has been more fully described elsewhere  having a designated person to whom students can turn at any time. That would be a hotline . . . A counselor." Deans generally agreed with the concept of putting a mentoring support system in place. However, both students and deans see few resources in the medical schools directed toward student wellness and what programming that is offered is reactive and small in nature. Both the students and the deans discussed wellness in terms of stress and mental well-being, rather than including physical health factors such as nutrition and exercise. Students felt that the best way to teach prevention would be through skill development and role modeling from faculty who incorporate prevention into their practice. The deans proposed that prevention be integrated throughout the curriculum and not be offered as a separate course; students concurred that more prevention instruction would be optimal and acknowledged that a separate course gives the impression that the content is less important and optional.We visited three medical schools with especially good and abundant practices around medical student health , and several other schools with some activities that seemed also to merit mention. These schools were selected for in-depth interviewing, with the best practices outlined in Table Five being used on medical school campuses.] has typically examined limited populations of medical students regarding personal health promotion, with few assessments of student well-being or of the success of various interventions, so only limited conclusions can be drawn (a situation that will be improved with this and other publications from HD). However, some trends may be emerging, such as students' health practices being good in some spheres [Prior literature [ref  spheres , but not spheres  and resi spheres ,5, with  spheres . Poor me spheres . While m spheres , some st spheres .Medical student and physician health is of inherent interest, but it is especially of concern because of the well-documented link between physicians' personal health practices and their patient counseling practices . DespiteWe found consistent support from both Deans and students for medical schools' encouraging healthy student behaviors, though modest follow-through on this support. Though students seemed to have thought little about the relationships between their own personal and clinical health promotion practices, we were especially impressed with the Deans' unanimity that faculty members should model healthy behaviors. The deans' support of the relationship between physicians' personal and clinical health practices, and concern about their institutions' acting on this relationship bodes well for the role of HD principles in the future of medical education. The correlation between students' and deans' responses suggests that deans understand well their students' health environments. If acted on, this finding (coupled with deans' beliefs that the environment can and should be improved) could create important positive changes in medical education and in disease prevention.The author(s) declare that they have no competing interests.EF co-developed the protocol, helped guide analyses, and drafted and revised the manuscript. JH co-developed the protocol, obtained funding, and helped edit the manuscript. LE co-developed the protocol, performed analyses, and helped edit the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Leucocyte recruitment and inflammation are key features of high dose radiation-induced tissue injury. The inflammatory response in the gut may be more pronounced following radiotherapy due to its high bacterial load in comparison to the response in other organs. We designed a model to enable us to study the effects of radiation on leucocyte-endothelium interactions and on intestinal microflora in the murine ileum. This model enables us to study specifically the local effects of radiation therapy.A midline laparotomy was performed in male C57/Bl6 mice and a five-centimetre segment of ileum is irradiated using the chamber. Leucocyte responses (rolling and adhesion) were then analysed in ileal venules 2 – 48 hours after high dose irradiation, made possible by an inverted approach using intravital fluorescence microscopy. Furthermore, intestinal microflora, myeloperoxidase (MPO) and cell histology were analysed.Enterobacteriaceae and Lactobacillus decreased significantly after 16 hours. In the radiation groups, the bacterial count showed a progressive increase from 2 to 24 hours after radiation.The highest and most reproducible increase in leucocyte rolling was exhibited 2 hours after high dose irradiation whereas leucocyte adhesion was greatest after 16 hours. Radiation reduced the intestinal microflora count compared to sham animals with a significant decrease in the aerobic count after 2 hours of radiation. Further, the total aerobic counts, This study presents a refinement of a previous method of examining mechanisms of radiation enteropathy, and a new approach at investigating radiation induced leucocyte responses in the ileal microcirculation. Radiation induced maximum leucocyte rolling at 2 hours and adhesion peaked at 16 hours. It also reduces the microflora count, which then starts to increase steadily afterwards. This model may be instrumental in developing strategies against pathological recruitment of leucocytes and changes in intestinal microflora in the small bowel after radiotherapy. The microscopic images were televised using a charge-coupled device video-camera  and recorded on videotape (Sony SVT-S3000P S-VHS recorder) for subsequent off-line analysis. To prevent drying during microscopic observations the intestinal segment was placed on a saline moistened cotton gauze and thereafter positioned under the microscope. After a 5-min equilibration period, quantitative measurements were taken. Analysis of leucocyte-endothelium interactions (rolling and adhesion) was made in venules (inner diameter 15–30 μm) with stable resting blood flow. Blood perfusion within individual microvessels was studied after contrast enhancement by eviously .2O2 . Values are expressed as MPO units per g tissue.The enzyme myeloperoxidase (MPO) is abundant in neutrophil leucocytes and has been found to be a reliable marker for the detection of neutrophil accumulation in inflamed tissue. To determine tissue MPO content, radiated ileal tissue was collected, weighed, homogenized in 10 ml 0.5% hexadecyltrimethylammonium bromide, and freeze thawed, after which the MPO activity of the supernatant was assessed. The enzyme activity was determined spectrophotometrically as the MPO-catalysed change in absorbance occurring in the redox reaction of HSamples from the irradiated small intestine were placed in 4% phosphate buffered formaldehyde. Paraffin-embedded samples were sliced and studied under light microscopy after staining with hematoxylin and eosin. At least 3 slides were studied from each specimen in a blinded fashion.Tissue samples from the irradiated small intestine were first placed in 5 ml of sterile transport medium . Samples20 μl blood was mixed with Turk's solution  in a 1:10 dilution. Leucocytes were counted and differentiated as polymorphonuclear (PMNL) or mononuclear (MNL) cells in a Burker chamber.P < 0.05.Statistical evaluations were performed using the Kruskal-Wallis one way analysis of variance on ranks for unpaired samples (Dunn's post hoc test was used). For bacterial microflora in comparing 2 groups we used Mann-Whitney Rank sum test, and for the comparison of the different time points within the radiated groups we used One Way ANOVA followed by multiple comparisons versus control group (Dunnett's method). The results are presented as mean values ± SEM. Differences were considered to be significant at i.e. the number of rolling and adherent leucocytes was 2.4 ± 1.2 cells/min and 1.7 ± 1.7 cells/mm, respectively. In contrast, radiation (19 Gy) evoked a marked time-dependent leucocyte response, i.e. a significant increase in both leucocyte rolling and firm adhesion over time . We observed that leucocyte rolling peaked two hours after radiation , whereas leucocyte adhesion was maximum after 16 hours showing a marked response of 59 ± 14 cells/mm . Interestingly, both the leucocyte rolling and adhesion responses to radiation returned to baseline levels 48 hours after radiation . There was no difference in the hemodynamic parameters between the different experimental groups  revealed only occasional interactions between leucocytes and the microvascular endothelium, At 2 hours we could not observe any marked differences in the number of inflammatory cell types compared to the controls Figure . At 6 hoEnterobacteriaceae, Lactobacillus and anaerobic counts had decreased two hours after radiation  associated with clinical symptoms, was observed. Patients receiving radiotherapy may thus benefit from the intake of oral bacteriotherapy [Endogenous bacterial flora produces nutrients (e.g. short-chain fatty acids) for the mucosa; prevents overgrowth of potentially pathogenic micro-organisms; stimulates the immune system especially the gut-associated lymphoid tissue; helps eliminate toxins from the lumen and participates in intestinal regulation, motility and blood flow . Radiatin number . Bacterin number . Changesn number . Post-ran number . The micotherapy . The impThe histological changes following radiation are both time and dose dependent ,30. SoonNo differences MPO values could be seen between the controls and the radiated groups. This is probably because it is a crude method of measurement and thus may not be sensitive enough to detect early changes of inflammation.This study therefore presents a refinement of previous methods of examining effects of radiation enteropathy, and a new approach at investigating radiation induced leucocyte responses in the ileal microcirculation. This new model may be instrumental in developing strategies against pathological recruitment of leucocytes and changes in intestinal microflora in the small bowel.None declared.LBJ designed the study and participated in construction of the chamber. Performed experimental studies and drafted the manuscript.AAR performed experimental studies and drafted the manuscript.DA participated in the design of the study and construction of the chamber. Performed experimental studies, drafted the manuscript and performed the statistical analysis.LW participated in the radiological design of the study, construction of the chamber and the implementation of radiotherapy.SB participated in the radiological design of the study, chamber and the implementation of radiotherapy.CT participated in the implementation of radiotherapy.NO carried out bacteriological studies.VC performed the histological analysis.HT assisted with issues related to intravital microscopy.BJ conceived of the design, participation in construction of the chamber, co-ordination of the study as well as supervision and draft of the manuscript.The pre-publication history for this paper can be accessed here:


Tumour regression observed in many conditional mouse models following oncogene inactivation provides the impetus to develop, and a platform to preclinically evaluate, novel therapeutics to inactivate specific oncogenes. Inactivating single oncogenes, such as c-Myc, can reverse even advanced tumours. Intriguingly, transient c-Myc inactivation proved sufficient for sustained osteosarcoma regression; the resulting osteocyte differentiation potentially explaining loss of c-Myc's oncogenic properties. But would this apply to other tumours?L over-expression or a favourable microenvironment, respectively. Moreover, tumours progress despite reacquiring a differentiated phenotype and partial loss of vasculature during c-Myc inactivation. Interestingly, reactivating c-Myc in β-cell tumours appears to result not only in further growth of the tumour, but also re-expansion of the accompanying angiogenesis and more pronounced β-cell invasion (adenocarcinoma).We show that brief inactivation of c-Myc does not sustain tumour regression in two distinct tissue types; tumour cells in pancreatic islets and skin epidermis continue to avoid apoptosis after c-Myc reactivation, by virtue of Bcl-xGiven that transient c-Myc inactivation could under some circumstances produce sustained tumour regression, the possible application of this potentially less toxic strategy in treating other tumours has been suggested. We show that brief inactivation of c-Myc fails to sustain tumour regression in two distinct models of tumourigenesis: pancreatic islets and skin epidermis. These findings challenge the potential for cancer therapies aimed at transient oncogene inactivation, at least under those circumstances where tumour cell differentiation and alteration of epigenetic context fail to reinstate apoptosis. Together, these results suggest that treatment schedules will need to be informed by knowledge of the molecular basis and environmental context of any given cancer. The ability to switch expression of a given oncogene 'on' or 'off' ewed in: ,2). Giveewed in: ). Similaewed in: ,5.Several studies using conditional mouse models of various cancers have unexpectedly shown that inactivation of the initiating oncogene is sufficient for reversal not only of the primary tumour but also of invasive and metastatic lesions, many of which contain multiple genetic and epigenetic alterations -18.The tumour regression observed in many of these models following sustained oncogene inactivation provides a powerful platform on which to build a deeper understanding of fundamental tumour biology and with which to preclinically evaluate novel therapeutics to target specific genes. A recent study has shown that brief inactivation 10 days) of c-Myc was sufficient for the sustained regression of c-Myc induced invasive osteogenic sarcomas in transgenic mice ; subsequ0 days ofL overexpression) induced β-cell growth arrest and re-differentiation into mature β-cells, accompanied by the collapse of tumour vasculature and tumour cell mass resulting from apoptosis, despite the constitutive expression of Bcl-xL in the tumour cells  30 cycles, 72°C 10 min 1 cycle. PCR product size: 413 bp. Primers used for the detection of RIP7-Bcl-xL cDNA: (forward) 5' AGC ACT TTC TGC AGA CCT AGC AC 3'; (reverse) 5' CAG CTC CCG GTT GCT CTG AGA C 3'. PCR program:  30 cycles, 72°C 3 min 1 cycle.Heterozygous  Hanahan . Littersad libitum.Transgenic mice were housed under barrier conditions with a 12 hour light/dark cycle and access to food and water TAM, was targeted to pancreatic β-cells using a rat insulin promoter, or to suprabasal keratinocytes using the human involucrin promoter. As shown in our previous publications [TAM protein remains inactive due to association of the cells' own hsp90 with the ERTAM. Upon administration of 4-hydroxytamoxifen (4-OHT), hsp90 is displaced allowing association of c-Myc's partner, Max, to form transcriptionally active heterodimers [Expression of the chimeric protein, c-MycERications ,9, the trodimers .TAM protein in pancreatic β-cells of adult transgenic mice, 1 mg of 4-OHT (Sigma) sonicated in peanut oil (1 mg/0.2 ml) was administered daily by IP injection. To activate c-MycERTAM protein in skin epidermis of adult transgenic mice, 1 mg of 4-OHT (Sigma) dissolved in ethanol (1 mg/0.2 ml) was administered daily by topical application to a shaved area of dorsal skin.To activate c-MycERTAM protein was achieved following withdrawal of 4OHT. As c-MycERTAM RNA and protein levels remain unchanged in the presence or absence of 4OHT, Northern and Western blot analysis will not confirm whether the protein is inactive. Thus, to confirm inactivity of the c-MycERTAM protein in pancreatic β-cells, we show reversal of several markers of Myc activation by day 4 of 4OHT withdrawal -growth arrest, re-differentiation, re-establishment of cell-cell contact – by immunohistochemistry (see ref [Inactivation of c-MycER(see ref  and ResuTAM protein in skin epidermis was confirmed using immunohistochemistry for markers of re-differentiation (K1 and K14) and growth arrest (see Results section). The tight regulation of the c-MycERTAM protein in skin epidermis was also previously shown in [in situ hybridisation for detection of ODC RNA, a known c-Myc target gene; by day 5 following withdrawal of 4OHT, ODC RNA is no longer detected.Similarly, inactivation of c-MycERshown in  using inPancreata or skin were excised from mice and 5–10 mm pieces of tissue were fixed overnight in neutral-buffered formalin, embedded in paraffin wax and sectioned (5–10 μm). Frozen sections were prepared from tissue embedded in OCT and frozen in foil on a bath of dry ice and ethanol. Prior to staining, frozen sections were air-dried and fixed in 4% paraformaldehyde for 15 minutes. Alternatively, for frozen sections, tissue was fixed in 4% paraformaldehyde for 2 hours followed by incubation in 30% sucrose overnight at 4°C. For pancreata analysis, sections (5–10μm) were cut throughout the entire pancreas and every tenth section was selected for histological and immunohistochemical examination. For skin, sections (5–10μm) were cut through two 10 mm pieces of tissue and every tenth section was selected for analysis.Primary antibodies were as follows: rabbit polyclonals Ki-67 (Novacastra) and Nkx6.1 ; rabbit anti-mouse laminin (Sigma); guinea-pig anti-porcine insulin (Dako); rat anti-mouse E-cadherin, (Zymed); rabbit anti-mouse keratin 1 (BabCo); rat anti-mouse CD45 (AbCam). E-cadherin and laminin antibodies were found to label reliably only frozen tissue sections. Other antibodies were effective when used on both paraffin-embedded and frozen sections, although Ki-67 and Nkx6.1 required epitope retrieval by microwaving paraffin-embedded sections at 700 W for 2 × 10 minutes in 0.01 M citrate buffer, pH6.0 (Vector) followed by immersion in cold water.Antibodies were diluted in incubation buffer: PBS/0.5% Triton X-100 containing 1:25 dilution of serum from the same species as the secondary antibody. Primary antibodies for insulin and Ki-67 were applied together to sections for 1 hour. Sections were then incubated in Texas Red-conjugated goat anti-guinea pig Ig secondary antibody together with FITC-conjugated goat anti-rabbit secondary antibody (Vector). After washing, sections were mounted in Vectashield mounting medium (Vector).To detect cells undergoing apoptosis, costaining with TUNEL/insulin, TUNEL/laminin and TUNEL/K1, immunofluorescent staining was performed by applying insulin, laminin or K1 antibodies to sections for 1 hour at room temperature followed by Texas Red-conjugated goat anti-guinea pig (for insulin antibodies) and goat anti-rabbit (for laminin and K1 antibodies) Ig secondary antibody (Vector). TUNEL staining was subsequently performed using ApopTag Fluorescein Direct kit (Chemicon) for frozen tissue sections and ApopTag Fluorescein Indirect kit (Chemicon) for paraffin-embedded tissue sections.SP participated in the design of the study, administered 4OHT to relevant mice, collected tissue, carried out immunohistochemical staining, coordinated and analysed data, drafted the manuscript, and provided part of the funds. SA carried out genotyping, assisted with the administering of 4OHT to mice, collected tissues, cut sections and assisted with capturing of images. LC assisted with genotyping, cutting of sections, and immunohistochemical staining. VI assisted with genotyping and immunohistochemical staining. SZ assisted with genotyping and immunohistochemical staining. MK participated in the design of the study, assisted with the coordination and analyses of data, helped draft the manuscript and provided funding.


Chronic meningitis is defined as symptoms and signs of meningeal inflammation and persisting cerebrospinal fluid abnormalities such as elevated protein level and pleocytosis for at least one month.Neisseria meningitidis group B. The patient made a dramatic recovery with high-dose intravenous ceftriaxone antibiotic therapy for meningococcal meningitis.A 62-year-old woman, of unremarkable past medical history, was admitted to hospital for investigation of a four-week history of vomiting, malaise an associated hyponatraemia. She had a low-grade pyrexia with normal inflammatory markers. A CT brain was unremarkable and a contrast MRI brain revealed sub-acute infarction of the right frontal cortex but with no evidence of meningeal enhancement. Due to increasing confusion and patient clinical deterioration a lumbar puncture was performed at 17 days post admission. This revealed gram-negative coccobacilli in the CSF, which was identified as 1) Chronic bacterial meningitis may present highly atypically, particularly in the older adult. 2) There may be an absent or reduced febrile response, without a rise in inflammatory markers, despite a very unwell patient. 3) Early lumbar puncture is to be encouraged as it is essential to confirm the diagnosis.4) Despite a delayed diagnosis appropriate antibiotic therapy can still lead to a good outcome. Bacterial Meningitis usually presents as an acute illness, predominantly affecting children and young adults. It typically presents with the classical clinical triad of fever, neck stiffness and an altered mental state. However, it may rarely present as a chronic illness, without the classic clinical features noted in acute meningitis.A 62-year-old retired woman was admitted to hospital via her GP for investigation of a four-week history of vomiting and malaise associated with hyponatraemia. She was initially diagnosed as suffering from viral gastroenteritis. However, the vomiting had persisted and had become associated with a mild frontal headache. She had an unremarkable past medical history and was not taking any regular medication. She had never smoked and there was no recent antecedent foreign travel.On examination she appeared clinically dehydrated but otherwise looked well, and was alert and orientated. She was apyrexial and had no rash, photophobia, neck stiffness or stigmata of endocarditis. She had a sinus tachycardia of 104/minute, with normal heart sounds, and a blood pressure of 130/76 mmHg. Chest, abdominal and neurological examinations were unremarkable. She had a plasma sodium of 127 mmol/L (135–145 mmol/L), potassium of 3.4 mmol/L (3.5–5.0 mmol/l), urea 4.8 mmol/L (3.0–6.5 mmol/L) and creatinine of 68 mmol/L (60–125 mmol/L). There was no biochemical evidence of the syndrome of inappropriate antidiuretic hormone (SIADH) production . Serum complement and plasma immunoglobulin levels were unremarkable with no evidence of immunosuppression. In addition, she had a normal full autoimmune profile and thyroid function. Random cortisol level was mildly elevated at 799 nmol/L  consistent with a stress response.9/L  with a neutrophilia of 10 × 109/L . Her ECG and chest X-ray were normal. Her C-reactive protein (CRP) was slightly elevated at 10 mg/L  and erythrocyte sedimentation rate (ESR) was normal at 5 mm/hour. Her Chest X-ray and electrocardiogram were normal. Initial microbiological investigations  were normal.Her initial white cell count (WCC) was mildly elevated at 13.0 × 10Initial management consisted of slow intravenous rehydration with normal saline and antiemetic therapy, which led to a mild symptomatic improvement. Upper gastrointestinal endoscopy revealed mild oesophagitis.9/L). However, on day 4 of admission she developed a low-grade pyrexia of 37.5°C, which persisted (<38°C). A CT scan of the head revealed periventricular patchy white matter changes but no features of raised intracranial pressure or space occupying lesion.During the ensuing two weeks her laboratory investigations remained stable . In addition, her nausea and vomiting had failed to fully settle with supportive treatment. The LP results were as follows: cerebrospinal fluid (CSF) appearance was pale yellow and clear; protein = 5.69 g/L (0.15–0.4 g/L); CSF glucose 1.7 mmol/L versus plasma glucose 5.7 mmol/L ; CSF WCC = 106/mL  – 99% lymphocytes. Gram's stain revealed gram-negative coccobacilli; acid-fast bacilli were not seen. She was commenced on intravenous ceftriaxone.On day 18 she underwent a lumbar puncture (LP) as she still had a low-grade pyrexia (temperature 37.5°C) and neutrophilia of 9.3 × 10Contrast MRI brain revealed sub-acute infarction of the right frontal cortex but with no evidence of meningeal enhancement. EEG demonstrated slow wave activity, which was consistent with a meningo-encephalitis.Neisseria meningitidis group B, type NT, subtype NT P1.16/nt. She underwent contact tracing and completed a 10-day course of intravenous ceftriaxone. She continued to make a slow but progressive recovery. After a period of rehabilitation and intense physiotherapy she was discharged home 40 days after admission, with mild residual gait ataxia.Within 48 hours of intravenous antibiotics she was more alert, orientated, and sitting out of bed. CSF culture grew gram-negative cocci, which was identified as This case report presents two important clinical concepts: firstly, the presentation of chronic meningitis and secondly, the clinical presentation of bacterial meningitis in the older adult (defined as > 60 years old). The diagnosis was delayed due to the highly atypical clinical presentation .Chronic meningitis is defined as symptoms and signs of meningeal inflammation and persisting cerebrospinal fluid (CSF) abnormalities such as elevated protein level and pleocytosis for at least one month ,3. It afThere are several distinguishing features that may help to differentiate chronic meningitis from adult acute bacterial meningitis table . The claHyponatraemia (as in our patient), whilst very uncommon in acute bacterial meningitis, is seen in the vast majority of cases of chronic meningitis ,11. AlthAcute bacterial meningitis is usually a rapidly progressive and highly lethal disease in older adults . Rapid dGiven the success of childhood immunization, and an increasingly aging population, the proportion of older adults presenting with bacterial meningitis is increasing . There aThe clinical presentation of bacterial meningitis is more variable in the older as compared with the younger adult, with fewer patients manifesting with the classic symptoms of fever, neck stiffness and altered mental state than among younger adults . It has Listeria monocytogenes meningitis or alternative causes such as tuberculous and fungal infection [Our patient's CSF showed a lymphocytosis, raised protein, and low glucose ratio, which are seen in only 10% of bacterial meningitis cases. This CSF profile would normally suggest infection with nfection ,31.Neisseria (N) meningitidis is a leading cause of bacterial meningitis in the Western World and tends to predominate in young adults [N. meningitidis is a gram-negative, aerobic diplococcus. It is classified into serogroups  according to the immunological reactivity of their polysaccharides [g adults ,20,25. Ncharides ,20. The charides . Meningocharides -37.This case highlights the diagnostic challenge associated with bacterial meningitis presenting in an older patient. The presentation was made even more difficult owing to the blunted febrile response, the lack of inflammatory response observed in laboratory tests and the chronicity of the patient's symptoms. The diagnosis required thorough investigation during the inpatient stay. Early lumbar puncture is to be encouraged as it is essential to confirm the diagnosis. Despite a delayed diagnosis appropriate antibiotic therapy can still lead to a good outcome.The authors declare that they have no competing interests.MD generated the idea of writing the case report and was the consultant in charge of the patient. CD reviewed the case notes of the patient and wrote the original draft of the case presentation. CB significantly revised the original draft and added the conclusions, references and figures. AH offered considerable help with the manuscript revisions. All authors contributed to the final version of the manuscript.The pre-publication history for this paper can be accessed here:


Inter-individual variation in normal human mammary epithelial cells in response to oxythioquinox (OTQ) is reported. Gene expression signatures resulting from chemical exposures are generally created from analysis of exposures in rat, mouse or other genetically similar animal models, limiting information about inter-individual variations. This study focused on the effect of inter-individual variation in gene expression signatures.p value ≤ 0.05 in three of four cell strains analyzed.Gene expression was studied in primary normal human mammary epithelial cells (NHMECs) derived from four women undergoing reduction mammoplasty [Cooperative Human Tissue Network ]. Gene transcription in each cell strain was analyzed using high-density oligonucleotide DNA microarrays  and changes in the expression of selected genes were verified by real-time polymerase chain reaction at extended time points (ABI). DNA microarrays were hybridized to materials prepared from total RNA that was collected after OTQ treatment for 15, 60 and 120 min. RNA was harvested from the vehicle control (DMSO) at 120 min. The gene expression profile included all genes altered by at least a signal log ratio (SLR) of ± 0.6 and p53 polymorphisms. The two strains expressing the major variant of p53 had 83 common genes altered  at one or more time point by at least a 0.6 signal log ratio (SLR). The intermediate variant strains showed 105 common genes altered  in both strains.RNA species were clustered in various patterns of expression highlighting genes with altered expression in one or more of the cell strains, including metabolic enzymes and transcription factors. Of the clustered RNA species, only 36 were found to be altered at one time point in three or more of the cell strains analyzed . Cluster analysis examined the effects of OTQ on the cells with specific Differential changes in expression of these genes may yield biomarkers that provide insight into inter-individual variation in cancer risk. Further, specific individual patterns of gene expression may help to determine more susceptible populations. Oxythioquinox  is a prototypical pesticide that was first used in 1968 on crops such as apples, pears, cucumbers, and gherkins. However, its use was later confined to non-food crops, limiting exposure to nursery and greenhouse employees. OTQ is a member of the quinoxaline class of pesticides, which also includes chlorquinox and thioquinox. Principal agricultural use of OTQ was limited to the states of California, Washington, Florida, New York, Pennsylvania, Ohio and Michigan ,3. Furthin vivo studies in rats found alterations of a variety of metabolic enzymes following OTQ exposures, including alkaline phosphatase pyridine (PhIP) and 7,12-dimethylbenz [a]anthacene (DMBA), and was able to show that while both chemicals altered expression in some genes in a similar fashion, each induced unique gene expression patterns.This study is similar to work currently being carried out to determine gene expression profiles of a variety of environmental agents, including chemicals, physical agents and physiologic stresses -19. The The primary goal of this study was to look for consistent changes common to all donors that could potentially be used as biomarkers of exposure, creating a gene expression profile following OTQ exposure in normal human cells. Given previous research in hepatoxicity, ideally normal human liver cells would have been used as a model system. However, we needed a normal human tissue that was readily accessible. Therefore, gene expression was studied in primary normal human mammary epithelial cells from four different donors in response to OTQ exposure. Current microarray analysis of pesticide exposure focuses on animal studies, not allowing for analysis of inter-individual variation. With the use of microarrays including clinical diagnosis, genetic susceptibility to disease and treatment, the need to determine the potential role of inter-individual variation on gene expression patterns in all potentially exposed tissues. Genes found to be altered in multiple cell strains could be considered biomarkers for individual populations. Given the small number of cell strains used, follow-up analysis in a larger number of samples is required to confirm any potential biomarkers. Biomarkers derived from this study could potentially be used in future epidemiology studies analyzing the effects of pesticide exposure. Further, this gene expression profile could be compared to those of other pesticides and of known carcinogens to develop a more detailed mechanistic definition of these chemicals.2.Primary normal human mammary epithelial cells (NHMECs) were derived from tissues salvaged at reduction mammoplasty obtained though the Cooperative Human Tissue Network . Development and characterization of cell strains was achieved using standard methods . Cells wp53 expression with minimal toxicity at 2 hours with a final concentration of 6.25 μM. Cells were treated by diluting the stock OTQ/DMSO mixture in media and adding this solution to aspirated cells, allowing even exposure to all cells. DMSO (0.001%) alone was used as a vehicle control. At the end of the treatment period, cells were removed for RNA isolation. Cell viability was determined by Trypan Blue exclusion assay.Treatment was performed on cells in passage six at 70% confluency, as routinely performed in our laboratory. Preliminary studies analyzed a range of OTQ concentrations (0 – 12.5 μM) selected based on previous research ,7 and tip53 antibody  and incubated overnight at 4°C. The next day, the media was again removed and the secondary antibody  was incubated for one hour at room temperature. Slides were washed in triplicate with phosphate buffered saline (PBS) and cover slips were added. Slides were dried for one hour at room temperature before viewing, using the laser scanning confocal microscope BX50 (Olympus), and quantitative analysis was performed pyrene exposure in our laboratory have also found similar results for p53 expression [DNA microarray analysis found no change in pression . The effAlthough inter-individual variation between donors in response to OTQ was evident, there were also some genes found to be increased consistently in all strains by microarray analysis Table . Self-or.SOM clustering was used to show patterns of expression in each cell strain, and followed by further subclustering of those clusters of interest across all cell strains Figure . This ancytochrome P4502A13 (CYP2A13) and dihydrodiol dehydrogenase. Although expression was slightly increased at one time point, the temporal patterns were slightly different , immune response (cyclophilin), and apoptosis (MAD-3). These genes showed a consistent increase in expression following exposure to OTQ (Table MAP kinase kinase), cell metabolism genes , and transcription factors (E1A enhancer).From the full list of genes that fit these criteria, selected genes were chosen due to their potential role in carcinogenesis, whether by cell cycle control, immune response or other specific functions. Functions were described as annotated by NetAffx . These graf oncogene, GADD45, EWS, Cyclin D1) as well as immune response , cell proliferation (TGFβ), and RNA processing (HnRNP F protein).Some genes of interest were variably altered by strain, examples of which are also listed in Table prohibitin, DDH, and CYP2A13 at 24 h showed a decrease in expression in some of the cell strains analyzed. Samples not available for RT-PCR analysis are listed as N/A in Table Real-time PCR was used to confirm and extend results seen by microarray analysis for selected genes. Following the original microarray analysis, patterns of some genes appeared to be changing at the latest time point 120 min), so extended time points were selected (12 and 24 h) to see a more complete expression profile for these genes. Due to limited amount of cDNA, genes were analyzed at 15 min and/or 120 min, and then analyzed at 12 and 24 h by RT-PCR. Extended time points were selected to look at specific genes found altered at the earlier time points. These genes were selected due to their function and/or pattern of expression, and determining their expression pattern at later time points was of interest. Results are shown in Table 0 min, soThe purpose of this study was to determine if microarray analysis of four normal human mammary cell strains with a known haplotype could be used to find biomarkers related to either exposure in general or the specific haplotype in question. The use of only four cell strains was determined to have enough power to provide basic information to lead to further study if necessary. This study would be followed up for specific genes of interest in a larger number of cell strains, preferably bypassing the more expensive and time-consuming microarray analysis for RT-PCR only.DNA microarray analysis was used to profile the cellular response to OTQ, a quinoxaline pesticide. Analysis revealed genes with common response across the four human cell strains studied as well as inter-individual variation in response. Using only four normal human cell strains, our goal was to discover any distinctly altered genes in response to OTQ exposure. Future analysis with a larger number of cell strains will be used to follow-up this analysis on specific genes of interest.The majority of studies looking at gene expression profiles have used animal models, limiting any knowledge obtained to genetically similar organisms. Analysis with normal human cell strains, like those described here, will give more information on inter-individual variation in response to various chemicals. This information will yield clues to the metabolic pathways of the specific chemicals, and this increased knowledge will aid in determining potential hazards in the environment and the workplace. Given the large number of pesticides in use today, further examination of the effect of these chemicals on individuals is warranted.junB and cfos (the AP1 complex). A number of genes involved in carcinogenesis were found altered after exposure to OTQ, both induced and down-regulated. For example, prohibitin expression is found to be up-regulated in most cell strains after OTQ exposure, with similar expression patterns associated with a decrease in cancer incidence [Following exposure to OTQ, NHMECs showed alterations in genes involved in a variety of functions. These included xenobiotic metabolism, transcription, and DNA synthesis. Genes altered as a result of OTQ exposure in all strains analyzed included transcription factors like ncidence .cytochome P4502A13 (CYP2A13) and dihydrodiol dehydrogenase, are involved in xenobiotic metabolism. It has been suggested that CYP2A13 is the main metabolic activator of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), a tobacco-specific nitrosamine [CYP2A13 begins to wane at the final time point tested, suggesting this alteration to be somewhat transient. This is an unusual expression pattern for a cytochrome p450, as genes in this family tend to show a gradual increase in induction, and an equally gradual decrease in expression. Further analysis by RT-PCR showed that this p450 was increased at later time points , cyclin D1 (Z29087), and BTF2 (U72649) may further support an association between OTQ exposure, p53 variant status and carcinogenesis [GADD45 transcription in response to DNA damage, which is associated with an increase in cell cycle arrest and DNA damage repair, while increased levels of raf oncogene have been associated with lung carcinogenesis [Inter-individual variation as a result of genetic polymorphisms in genes of interest would focus on specific at-risk worker populations. For example, the four cell strains analyzed in this study have been genotyped for a variety of genes, in particular those involved in cell cycle control and xenobiotic metabolism. Two of the four cell strains selected for analysis are heterozygote for the minor variant haplotype of p53, a cell cycle control gene (cell strains 3 and 4). Although no biological mechanism for the role of this variant in carcinogenesis has been defined, several studies associating this haplotype with various cancers support such role -35. Analogenesis -38. Of togenesis -41.p53 variants in gene expression differences. Due to the expense of microarray analysis, this is performed in only a limited number of cell strains (4), and selected genes will be further analyzed with |RT-PCR in a larger number of cell strains. Over 80 cell strains have been established in our laboratory to date, with half of these having been genotyped for p53. However, given that this haplotype is found only in a limited portion of the population, this varied pattern of expression in key genes in cell cycle control may highlight a specific at-risk population.Given the small number of cell strains used, this analysis needs to be extended to additional cell strains to determine the role of the Searches for similar natural compounds to replace these potentially disruptive chemicals can also use gene expression profiles . ProfileCYP2A13 and dihydrodiol dehydrogenase. The results shown here do not suggest a direct role of OTQ in carcinogenesis. They do, however, suggest OTQ exposure leads to an increase in expression of genes that do play a direct role in carcinogen; metabolism  [The overall goal of this project was to create a gene expression profile for OTQ or related pesticide analogues with the hopes of finding genes to be used as potential biomarkers of exposure. This expression profile may also be used to determine the final role of OTQ in carcinogenesis by comparing it to profiles of known carcinogens. It is possible that the main effect of OTQ exposure is not on the direct alterations in many genes, but on alterations in genes potentially involved in carcinogenesis, among them the examples of  cancer) ,43.Discovery of genes altered following exposure to OTQ in human cell strains may aid in future epidemiology studies on pesticide exposures. Gene expression profiling can be used to yield genetic biomarkers of exposure that, after validation, could be used in a clinical setting for early determination of organophosphate exposure, increasing early treatment of pesticide illness and thereby increasing the recovery rate of exposed individuals.OTQ, oxythioquinoxDDH, dihydrodiol dehydrogenaseDMSO, dimethylsulfoxideSLR, signal log ratioDMT, Data Mining Tool™MAS, Microarray Suite™SOM, self-organizing mapNHMEC, normal human mammary epithelial cellRT-PCR, real-time polymerase chain reactionNone declared.MRG participated in the design of the study, and performed all experiments. DLW was responsible for the growth and maintenance of all cell strains used. AW conceived of the study and participated in the design and coordination. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here: