Retinoids are recognized as important regulators of vertebrate development, cell differentiation, and tissue function. Previous studies, performed both in vivo and in vitro, indicate that retinoids influence several reproductive events, including follicular development, oocyte maturation and early embryonic development. The present study evaluated in vitro effects of retinol addition to media containing maturing bovine oocytes and developing embryos in both a low oxygen atmosphere (7%) and under atmospheric oxygen conditions (20%). In the first experiment, abbatoir collected bovine oocytes were matured in the presence or absence of varying concentrations of retinol. After a 22–24 hour maturation period the oocytes were fertilized, denuded 18 hours later and cultured in a modified synthetic oviductal fluid (mSOF) in a humidified atmosphere at 38.5 degrees C, 5% CO2, 7% O2 and 88% N2. Cleavage rates did not differ among control and retinol-treated oocytes in all three experiments. Addition of 5 micromolar retinol to the maturation medium (IVM) tended (p < 0.07) to increase blastocyst formation  compared to the controls (21.9% +/- 1.9%). Further analysis revealed when blastocyst development rates fell below 20% in the control groups, 5 micromolar retinol treatment dramatically improved embryonic development, measured by blastocyst/putative zygote rate . The 5 micomolar retinol treatment also enhanced the blastocyst/cleaved rate by nearly 10% . In the second and third experiments addition of 5 micromolar retinol to the embryo culture medium (IVC) under low oxygen conditions did not significantly improve cleavage or blastocyst rates, but 5 micromolar retinol significantly increased blastocyst development under 20% O2 conditions (p < 0.001). These studies demonstrate that supplementation of 5 micromolar retinol to the maturation medium may improve embryonic development of bovine oocytes indicated by their increased blastocyst rate. A significant improvement in the blastocyst development with the 5 micromolar retinol treatment under atmospheric conditions suggests a beneficial antioxidant effect during embryo culture. Vitamin A is essential for reproduction, and deficiencies and excesses may result in embryonic loss and/or congenital defects . Retinoltrans and 9-cis retinoic acid (RA) are natural cellular metabolites of retinol and mediate biological activity through interaction with nuclear retinoic acid receptors (RAR) and retinoid X receptors (RXR), respectively. Ligand-bound RARs and RXRs influence transcription by interacting with response elements in the promoter regions of retinoid-regulated genes [Retinol is transported systemically and intercellularly bound to retinol-binding protein (RBP). Cellular retinol-binding proteins (CRBP) and cellular retinoic acid-binding proteins function in intracellular vitamin A transport, metabolism and homeostasis . All-traed genes . Within ed genes . Concented genes . RBP syned genes ,12.cis RA to in vitro oocyte maturation medium affects trophectoderm differentiation, total cell number and inner cell mass-trophoblast cell ratios, following fertilization in cattle oocytes [The cumulus oocyte complex (COC) may be a target for retinol, since the cells that nurture and communicate with the oocyte, contain transcripts and protein for several RARs and RXRs, RBP and retinaldehyde-2 dehydrogenase (RALDH-2) a metabolizing enzyme . Bovine  oocytes ,16. TogeThe mechanism by which retinol or retinoic acid administration influences oocyte maturation and positively impacts early embryonic development is not known and is the subject of much investigation. Retinoic acid may influence oocyte maturation through its effects on FSH or LH receptor expression as demonstrated in porcine  and rat 2 and reduced O2 conditions. Results suggest beneficial effects of retinol administration during maturation especially to less competent oocytes, and improved development of embryos cultured under atmospheric oxygen conditions, indicating protection from oxidative stress.In the present study, we investigated the effects of retinol administration to in vitro matured oocytes, and cultured bovine embryos under atmospheric O3, 12 mM HEPES, and supplemented with 2 mM glutamine, 2% fetal bovine serum , and penicillin/streptomycin . Oocyte maturation medium (OMM) consisted of bicarbonate buffered TCM-199 supplemented with 50 μg/mL of gentamycin, purchased from Specialty Media, 5 μg/mL of FSH purchased from Vetrepharm Canada, Inc. (Ontario, Canada), 0.3 μg/mL of luteinizing hormone (LH) that was generously provided by the USDA, Beltsville, MD, 10% FBS, 0.2 μM sodium pyruvate and 2 mM glutamine. Modified Tyrode's Albumin Lactate Pyruvate (TALP) media used in sperm preparation (SP-TALP), removal of cumulus cells from oocytes (HEPES-TALP) and in vitro fertilization (IVF-TALP) were prepared as described by Parrish et al. [All chemicals were purchased from Sigma Chemical Company, St. Louis, MO unless otherwise noted. Bovine oocyte collection medium (OCM) was composed of modified M199, 4.2 mM NaHCOh et al. . In vitrh et al.  supplemetrans retinol was dissolved in 100% ethanol, appropriate dilutions made, and aliquots were stored at -80°C until use. Retinol was prepared fresh each month and checked on a spectrophotometer for accuracy. The concentration of ethanol during maturation or culture was less than 0.1%.All-2, ambient air, and saturated humidity.Ovaries from mature, cycling cattle were obtained from an abbatoir and pooled. Cumulus oocyte complexes (COCs) were quickly harvested by slicing follicles (2–5 mm) with a sterile surgical blade, and collecting them in OCM. Intact COCs with homogeneous ooplasm and two or more layers of cumulus cells were selected, washed, and approximately 50 were transferred to 500 μl of pre-equilibrated OMM, and matured for 22–23 hours in a 38.5°C incubator with an atmosphere of 5.0% COg. The pellet was removed and resuspended in SP-TALP and centrifuged for 8 minutes at 460 g. After removal of the supernatant, the sperm sample was reconstituted in 500 μL of IVF-TALP for a final concentration of 1 × 106 spermatozoa/mL. The plate was incubated for 22 hours at 38.5°C in an atmosphere of 5.0% CO2 and ambient air with saturated humidity.Fertilization (Day 0) was performed with combined semen from two bulls of proven fertility prepared according to the method by Parrish and coworkers . Briefly2, 7% O2 and 88% N2 (first and second experiments) with saturated humidity. The mSOF medium was changed every 48 hours. Cleavage was assessed on Day 3 and blastocyst rate was calculated on Day 8.Approximately 18 hours after fertilization putative zygotes were denuded of cumulus cells by vortexing in 500 μl of HEPES-TALP for four minutes (Day 1). Putative zygotes (approximately 35–40) were cultured in 500 μL of mSOF for eight days in a 38.5°C incubator in an atmosphere of 5% COtrans retinol (0, 1.0, 5.0, or 10.0 μM) and embryos were allowed to develop under low oxygen conditions. In the second experiment all-trans retinol was added only to embryo culture medium (0, 1.0, 2.0, 5.0, or 10μM) on days 1, 3, 5, and 7, and the embryos developed in a low oxygen atmosphere. In the third experiment embryos were cultured under atmospheric oxygen conditions (air and 5% CO2) and all-trans retinol (0 or 5μM) was added to embryo culture medium on days 1, 3, 5, and 7.In the first experiment maturation medium alone was supplemented with all-Data were analyzed as an incomplete block design (experiments 1, 2, and 3), or a randomized block design (experiment 4), blocked on plate using mixed model procedures of SAS . At leasIn the first experiment addition of 5μM retinol during IVM tended to improve (p < 0.07) embryonic development to the blastocyst stage, compared to controls Table . The conFurther analysis of the maturation data Table  revealedFurther experiments were conducted during IVC under both low and atmospheric oxygen tensions. Under low oxygen conditions concentrations of 1, 2, and 5μM retinol were not statistically different from controls, and 10μM was deleterious Table . Prelimicis retinoic acid was shown to improve subsequent blastocyst development but high concentrations were detrimental [In the present study, over 3000 bovine oocytes were used to evaluate effects of retinol supplementation during IVM and IVC on embryonic development to the blastocyst stage. Retinol administration during the maturation period alone resulted in concentration-dependent effects. Whereas the presence of 1μM retinol had no effect on development, 5μM retinol tended to improve blastocyst rate of development, at the p < 0.07 level, compared to controls. At a concentration of 10μM, retinol did not significantly improve embryo development compared to controls. In preliminary studies, higher concentrations (100μM) were observed to be cytotoxic (data not shown). Similarly, exposure of bovine oocytes to low concentrations of 9-rimental .A more striking effect on embryonic development (p < 0.02) was observed by supplementation of 5 μM retinol to groups of oocytes with reduced developmental competence in which development of control oocytes to blastocyst was less than 20%. These results indicate that retinol supplementation during maturation may not benefit oocytes competent to progress, but rather, it improves the viability of oocytes that are developmentally challenged. In support of this, we have shown previously that retinol supplementation during maturation improves developmental competence of bovine oocytes compromised by heat stress .Since most transcription in the oocyte occurs prior to maturation during preovulatory development, in vitro culture deprives oocytes of much of this activity. Meiotic inhibitors have been used as a potential means of investigating regulation of oocyte transcription and mRNA processing in vitro . Treatme2 (air and 5% CO2) but not in an atmosphere of low O2 (7% O2, 5% CO2 and 88% N2). The present study, and all previous in vitro studies demonstrating a positive effect of retinoid administered during maturation, were performed in an atmosphere of approximately 20% O2 [Retinol supplementation of embryo culture medium dramatically improved development to the blastocyst stage (p < 0.001) when cultured in an atmosphere of approximately 20% Oy 20% O2 ,16,34, ay 20% O2 .2 has been demonstrated to be beneficial to in vitro survival of embryos from a variety of species [Mammalian cells, including the oocyte and those of the early embryo, have evolved several mechanisms to protect against ROS damage and maintain appropriate balances in REDOX reactions. Antioxidants present in the oocyte, embryo and/or its environment include vitamins A (retinol), C and E, pyruvate, glutathione (GSH), hypotaurine, taurine, and cysteamine . Antioxi species .Retinoids participate in a biological antioxidant network, and have been implicated as important regulators of redox signaling pathways ,24. CaroRetinoids may also protect against oxidative damage by maintaining adequate endogenous levels of antioxidant compounds and enzymes. Glutathione is the major non-protein sulphydryl compound found in mammalian cells responsible for strong basal ROS scavenging activity . MaintenResults from the present study indicate that retinol administration during in vitro maturation particularly improved embryonic development in those oocytes that may have been developmentally compromised. Moreover, retinol addition during in vitro culture, under atmospheric conditions, also improved embryonic development compared to those embryos incubated in a 7% oxygen atmosphere. The mechanisms by which retinoids affect the developmental capacity of oocytes and early embryos may include modulation of expression of growth factors and other developmental genes, improving mRNA quality, and direct and/or indirect affects on antioxidant defense mechanisms.TL Performed the experiments under low oxygen conditions, helped to coordinate experiments, and drafted the manuscript.DE Performed the experiments under high oxygen conditions and helped to coordinate experiments.JE Coordinated and assisted in the experimental design.JG Conceived of and coordinated the experiments and drafted the manuscript.


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 (that is, single) 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 (GCC, the largest subgraph such that there is a path between every pair of vertices) 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 (S, T, U) 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 (3,341) 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 (with size constraints min = 7, max = 75) 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 (for example, monotone and curvilinear relationships), 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(x,r) denote an open n-sphere centered at the point x with radius r, and let d(p,q) denote the Euclidean distance function. Given a set of points, P = {p1 p2, ..., pn}, in an n-dimensional Euclidean space, (pi, pj) is an edge in the Gabriel graph if no other point, pk (i ≠ k, j ≠ k) in P falls within the diameter sphere defined by B((pi = pj)/2, d(pi, pj)/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 (that is, when the φ = π/2). 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(i, j) 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, (i, j), if Ci,j <Tcrit (or some appropriately chosen function, f(Ci,j) <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 (i, j) 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 (i, k) and (j, k) 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(i, j) 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 (that is, |Fp ∩ P| = |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 (that is, every vertex is connected to every other vertex), 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) = (e1,e2,...,eq), such that w(ei) ≥ w(ej) for all i ≤ j (that is, order the edges from strongest to weakest). 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 (i.e., 100 mcg every 5–6 weeks), 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 (5,000 IU b.i.d). Luteal phase support was administered as daily 50 mg IM progsterone in oil injections.On cycle day 10, subcutaneous hCG (10,000 IU) 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 [There were 17 respondents to the Dean Survey (DS), representing 12 of the 16 schools in the nationally representative Healthy Doc (HD) project [In analyses comparing DS and HD data, DS schools with two respondents were first averaged so that each school is represented by one value . Variation between deans representing a school was quite low for all but one pair. By averaging for the five dean pairs (and consequently having a sample size of 12 rather than 17), the tests are conservative. Student opinion scores were also averaged for each of the twelve schools from which we received Dean responses; these averages were then correlated with the Dean's scores using Spearman's correlation method. For questions with fairly uniform responses by either Deans or students, Wilcoxon's Signed Rank Test was used to test if there were consistent differences between student and Dean opinion. The two variables to be correlated were ordinal variables, each with 5 levels. The type of correlation method was therefore limited to a non-parametric method. Additionally, the raw student data was clustered by school, requiring methods suitable for correlated data. Since the non-parametric method needed is not available for correlated data, we determined that the best method was to take the student mean values at each school to correlate with the dean values. While this ignored the student variability within school, this deficit was balanced by the fact that the much smaller n would require much stronger evidence of a relationship to evince a significant result.Deans were also asked to rate their school relative to other schools. To compare these ratings to students' opinions, schools were ranked using their mean student scores on each question related to prevention and healthy activities encouraged by the school. All 16 schools in the HD cohort were used in the ranking process (1 = highest, 16 = lowest), not just the 12 schools represented by the responding deans, as the 16 were the intended sample, and are representative of US medical schools [For our focus groups (conducted in 2002), we identified opportunities where there would be a wide and nationally representative range of medical schools. The first focus group was convened at the AMSA Chapter Officers' Training Conference (COC) attended by student leaders (primarily rising second years) from every U.S. osteopathic and allopathic medical school. AMSA invited a random sampling of those attending the COC to participate in the focus group. Since the first focus group of students attracted 10 first and second year students, the second focus group was a random sample of 12 clinical students; both student focus groups had an even gender mix. Because Philadelphia has so many medical schools (five), we sampled for the second focus group from those Philadelphia students who were listed in AMSA's membership database. Deans of Primary Care were invited to the third focus group convened at the annual conference of the Association of American Medical Colleges. AMSA used the list of Primary Care Deans and invited a random sample of them to attend the focus group; four attended. An outside contractor  assisted AMSA in developing the focus group guide, conducted all three focus groups, transcribed the conversations and analyzed the notes for trends in responses.In 2002–2003, we identified medical school campuses with intensive programs in medical student well-being through literature and web searches, recommendations from project advisory panel members, results from the Association of Academic Health Centers' American Network of Health Promoting Universities assessment, and participants in the HRSA-funded UME-21 project. Site visits and in-depth interviews were conducted using a protocol which sought information and recommendations on the following topics:• Student well-being programming, including the policies, activities, and evaluation for such efforts as stress reduction, exercise, diet, and mentoring.Healthy People 2010 objectives and how the various topics are integrated, taught, and evaluated.• Prevention in the curriculum using the • Deans' office support  for prevention in the curriculum and student wellness activities.• Student assessments and recommendations regarding their schools' efforts.Most surveyed Deans reported that their schools generally support students' health, though fewer Deans believe that their school encourages healthy eating Table . Both DeDeans were essentially unanimous in agreeing that faculty members should model healthy behaviors, and that schools should promote health with their students Table . HoweverThree-quarters of Deans believed that their medical schools' attitude toward alcohol was that drinking in moderation was acceptable, though students had more mixed impressions about schools' alcohol attitudes Table . Deans bWe also asked a few narrative questions of the Deans only. Deans indicated that Student Affairs and Student Health offices most often had responsibility for handling medical student wellness (responses of 10 and 4 Deans, respectively). Funds for student wellness activities primarily came from student fees and University budgets (9 and 13 Deans, respectively). Activities' effectiveness was usually unassessed, though some Deans used occasional surveys, data from health programs, student evaluations, and student feedback at meetings/events to help evaluate their programs.Our three hours of focus groups yielded little information about students' perceptions of the relationships between their personal and clinical health promotion practices; most students either had not considered this link, or had little to say about it. A few preclinical students reported that their personal wellness is generally linked to their competence as physicians, asserting that "if we sacrifice our own health from studying too long, staying up too late, stressing out too much about exams, we can't take care of other people if we don't watch our own health first." Several clinical students stated that wellness was difficult to achieve , and that having access to help/mentorship might help promote wellness for them: "I [would like] 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 (Emory, Mercer, and Loma Linda Universities), 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. Gastrointestinal tract damage by radiotherapy limits its efficacy in cancer treatment. The small bowel is highly radiosensitive and very mobile and is thus an important dose-limiting organ during radiation therapy for abdominal and pelvic cancer . Radiatintegrins ,6.Several animal models exist in order to investigate radiation-induced leucocyte endothelium cell responses, which can broadly be divided into two groups. Topical radiation  -9 and moThe purpose of this study was to refine a small bowel radiation model in order to study the time responses in terms of leucocyte rolling, adhesion, myeloperoxidase (MPO) levels, histology and intestinal floral changes in response to high dose radiation of the ileum, where the exact biologically effective dose could be calculated.ad libitum. All experimental procedures were performed in accordance with legislation on the protection of animals and were reviewed and approved by the Lund University Ethic's Committee for Animal Experimentation.Male C57Bl/6J mice weighing 22–26 g were kept under standard laboratory conditions maintained on a 12 hour light and 12 hour dark cycle and were allowed free access to animal chow and tap water i.p.) injection. The animals were placed in supine position on a heating pad (37°C) for maintenance of body temperature. A small midline incision (1.0–1.5 cm) was performed and a 5 cm segment of ileum located 5 cm from the ileocaecal valve was exteriorised and marked with 5-0 non-absorbable sutures. Any other visible prolapsed abdominal content was replaced back into the abdomen and the animal was placed on the specially designed frame/chamber  and 2.5 mg Xylazine (Janssen Pharmaceutica, Beerse, Belgium) per 100 g body weight by intraperitoneal (n = 6/time point) were exposed to 19 Gy of radiation and leucocyte-endothelium interactions were measured 2, 6, 16, 24 and 48 hours after induction of radiation using an IIVM (each group n = 6, likewise the R- groups). At the end of the procedure samples were collected for histology, and measurement of intestinal microflora, MPO and systemic leukocyte counts.The animals were divided into two different groups; Radiation & Surgery group (R+) and Sham Radiation & Surgery group (R-) which served as negative controls. The irradiated groups of mice . The exteriorised mouse intestine was positioned between Perspex slabs to accomplish sufficient secondary radiation scatter and thereby a reproducible and homogenous dose distribution. The absorbed dose was verified with independent measurements and was found to be within 5% throughout the intended volume using this technique. Using an asymmetrically half blocked 6 MV beam and extra lead shielding -labelled dextran (MW 150000), . In vivo labelling of leucocytes with rhodamine-6G  enabled quantitative analysis of leucocyte flow behavior in the ileum microcirculation. Due to its relatively higher molecular weight FITC-dextran stains/labels the intravascular plasmatic phase of the blood under epi-illumination with blue light  whereas the lower molecular weight of Rhodamine 6G allows for labelling of leucocytes and platelets using green fluorescent light . Quantification of microcirculatory parameters was performed off-line by frame-to-frame analysis of the videotaped images. Leucocyte rolling was determined by counting the number of leucocytes passing a reference point in the venule per 20 sec and is expressed as cells/min. Firm adhesion was measured by counting the number of cells adhering to the venular endothelium (200–300 μm long segments) and remained stationary for 20 sec and is given as cells/mm venule length. Blood flow velocities were analysed by means of a video assisted computer image analysis programme, CapImage software (Zeintl, Heidelberg, Germany). The staining of the plasmatic phase by FITC-dextran gives an indirect enhancement of red blood cells which appear dark in the illuminated surrounding plasma. The CapImage uses the FITC-dextran image to calculate the red blood cell velocity. The velocity was calculated as a mean value from 5–8 measurements per venule and is expressed as mm/sec. Venular wall shear rate was determined based on the Newtonian definition: wall shear rate = 8 [(red blood cell velocity/1.6)/venular diameter] as described previously [Observations of the intestinal microcirculation were made using an inverted Olympus microscope  equipped with different lenses (x10/NA 0.25 and x40/NA 0.60). The microscopic images were televised using a charge-coupled device video-camera (FK 6990 Cohu, Pieper GmbH, Schwerte, Germany) 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 (460 nm, 25°C). 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: