The identification of the molecular mechanisms of human immunodeficiency virus type 1, HIV-1, transcriptional regulation is required to develop novel inhibitors of viral replication. NF-κB transacting factors strongly enhance the HIV/SIV expression in both epithelial and lymphoid cells. Controversial results have been reported on the requirement of NF-κB factors in distinct cell reservoirs, such as CD4-positive T lymphocytes and monocytes. We have previously shown that IκB-αS32/36A, a proteolysis-resistant inhibitor of NF-κB, potently inhibits the growth of HIV-1 and SIVmac239 in cell cultures and in the SIV macaque model of AIDS. To further extend these observations, we have generated NL(AD8)IκB-αS32/36A, a macrophage-tropic HIV-1 recombinant strain endowed to express IκB-αS32/36A.In this work, we show that infection with NL(AD8)IκB-αS32/36A down-regulated the NF-κB DNA binding activity in cells. NL(AD8)IκB-αS32/36A was also highly attenuated for replication in cultures of human primary monocytes.in vivo.These results point to a major requirement of NF-κB activation for the optimal replication of HIV-1 in monocytes and suggest that agents which interfere with NF-κB activity could counteract HIV-1 infection of monocytes-macrophages  HIV-1 infection is characterized by a long period of clinical latency followed by the development of acquired immunodeficiency syndrome, AIDS. During latency and when viral replication is being controlled in patients treated with antiretroviral therapy, HIV-1 is present in cellular reservoirs and continues to replicate, with each ensuing round of replication giving rise to escape mutants, which further replenish viral reservoirs . This grin vivo by HIV-1 is thought to occur in CD4-positive, CCR5-positive lymphocytes and monocytes. Accordingly, when HIV-1 envelope protein in its oligomerized g160 form contacts the cell surface receptor a signalling cascade is triggered that results in transcriptional activation of specific gene arrays, such as the inflammatory cytokines IL-1 β, IL-6, IL-8, TNF-α, TGF-β; these cytokines, in turn, function to enhance the transcriptional activity of the proviral long terminal repeat (LTR) promoter [Initial infection promoter ,4. This promoter  and threpromoter . Both sepromoter . Howeverpromoter . It is cpromoter -13. Thesnef region of the respective viral genome [in vivo in rhesus macaque [IκB inhibitors regulate NF-κB activity . In respl genome ,17. We fl genome ,17. ThesTo generate a macrophage-tropic HIV-1 expressing the IκB-αS32/36A cDNA fused to the FLAG epitope, the CXCR4-tropic envelope of pNLIκB-αS32/36A  was replTo assess the functional impact of IκB-αS32/36A expressed from the recombinant NL(AD8) genome, 293T cells were transfected individually with pNL(AD8), pNL(AD8)IκB-αS32/36A or pNL(AD8)IκB-antisense, and the respective nuclear extracts were evaluated for NF-κB Fig.  and Sp1 We next analyzed the replication properties of the recombinant HIV-1 genomes in cultured human monocytes from different individuals. Based on normalized amounts of input virus, we found that NL(AD8)IκB-αS32/36A was highly attenuated for replication when compared to NL(AD8) and NL(AD8)IκB-antisense Fig. . Accordiin vivo. In addition, monocytes are programmed to produce a large amount of inflammatory cytokine, including IL1-β, IL-6, TNF-α, which are strong inducers of HIV-1 replication [Substantial numbers of monocytes are preserved in infected individuals even at later clinical stages of AIDS, when T cell numbers are dramatically reduced. Consistently, in animal models of HIV-1 infection, monocytes are the major reservoir after acute depletion of CD4-positive T cells ,20. Thislication . Indeed,lication ,4.Based on the published literature, the role of NF-κB in HIV-1 replication has been controversial ,16,21. FBecause IκB-αS32/36A constitutively inhibits NF-κB , the potIn this study we have addressed the role of NF-κB/IκB proteins in the replication of HIV-1 in primary human monocytes. We show a strong attenuation in the replication of a macrophage-tropic HIV-1 strain expressing the IκB-αS32/36A repressor of NF-κB in primary cultures of human monocytes. These results are consistent with previous evidence of HIV/SIV inhibition by IκB-αS32/36A in PBMCs and in macaques ,17. In a6) with viral plasmids (10 μg) using calcium phosphate. Forty hours later, the cell culture supernatant was passed through a 0.45-μm filter and measured for RT activity as previously described [293T cells were cultured in Dulbecco's modified Eagle's medium supplemented with 10% v/v heat-inactivated fetal bovine serum and 3 mM glutamine. Viral stocks were produced by transfecting 293T cells  and lysed in RIPA buffer (150 mM NaCl, 1 % Nonidet P-40, 0.5 % sodium deoxycholate, 0.1% sodium dodecyl sulfate, 50 mM Tris-HCl pH 8.0) 24 hours later. Proteins (10μg) were separated by electrophoresis in 10% SDS-polyacrylamide gel and transferred to Immobilon-P (Millipore). Filters were blotted with an AIDS patient serum or with anti-FLAG monoclonal antibody by using Western-Light Chemiluminescent Detection System (Tropix, Bedford, MA).g, 5 min), rinsed with Nonidet P-40-free lysing buffer, and resuspended in 150 μl of buffer containing 250 mM Tris-HCl, pH 7.8, 20% glycerol, 60 mM KCl, 1 mM DTT, 1 mM phenylmethylsulfonyl fluoride. Nuclei were then subjected to three cycles of freezing and thawing. The suspension was cleared by centrifugation (7000 × g, 15 min), and aliquots were immediately tested in gel retardation assay or stored in liquid phase N2 until use. The HIV-1 NF-κB oligonucleotide probe was 5'-CAAGGGACTTTCCGCTGGGGACTTTCCAG-3'; the Sp1 oligonucleotide probe was 5'-GGGAGGTGTGGCCTGGGCGGGACTGGGGAGTGGCG-3'. The probes were end-labelled with [γ-32P]ATP (Amersham Int., Buckinghamshire, UK) using polynucleotide kinase (New England Biolabs, Beverly, MA). Equal amounts (5 μg) of cell extracts were incubated in a 20 μl reaction mixture containing 10% glycerol, 60 mM KCl, 1 mM EDTA, 1 mM DTT, and 2 μg of poly [d(G-C)] (Boehringer Mannheim, Germany) for 5 min on ice. One μl of [γ32P]-labelled double-stranded probe (0.2 ng, 5 × 104 cpm) was then added with or without a 100-fold molar excess of competitor oligonucleotide. The reactions were incubated at room temperature for 15 min and run on a 6% acrylamide:bisacrylamide (30:1) gel in 22.5 mM Tris borate, 0.5 mM EDTA. Gels were dried and autoradiographed.Nuclear extracts and gel retardation assays were performed as described previously . Briefly5 cells) were infected with 105 - 106 cpm of RT activity. The cell culture supernatants were collected every two days and replaced with fresh medium. The viral production was measured as RT activity in the culture supernatants as previously described [Human monocytes were isolated from PBMC by elutriation, cultured in RPMI, 10% FCS and GMCSF (20 ng/ml) for 48 hours. Infections were performed with viral stocks measured by reverse-transcriptase (RT) activity . Usuallyescribed . The synNF-κB, nuclear factor kappa BIκB, inhibitor of nuclear factor kappa BIL-1, interleukin-1IL-6, interleukin-6IL-8, interleukin-8TNF-α, tumor necrosis factor alphaTGF-β, transforming growth factor-betacpm, counts per minuteFCS, fetal calf serumGMCSF, granulocyte-macrophage colony-stimulating factorThe author(s) declare that they have no competing interests.CP carried out the analysis of viral growth and DNA band-shift assays. FT was responsible for cell cultures. AP performed the immunoblotting analysis. GF produced the viral plasmids and viral stocks, and performed the artwork of the paper. GS participated in the design of the study and discussion of results. IQ designed this study and edited the manuscript.


Clustering is one of the most commonly used methods for discovering hidden structure in microarray gene expression data. Most current methods for clustering samples are based on distance metrics utilizing all genes. This has the effect of obscuring clustering in samples that may be evident only when looking at a subset of genes, because noise from irrelevant genes dominates the signal from the relevant genes in the distance calculation.We describe an algorithm for automatically detecting clusters of samples that are discernable only in a subset of genes. We use iteration between Minimal Spanning Tree based clustering and feature selection to remove noise genes in a step-wise manner while simultaneously sharpening the clustering.Evaluation of this algorithm on synthetic data shows that it resolves planted clusters with high accuracy in spite of noise and the presence of other clusters. It also shows a low probability of detecting spurious clusters. Testing the algorithm on some well known micro-array data-sets reveals known biological classes as well as novel clusters.© programs for the iterative clustering algorithm are available from The iterative clustering method offers considerable improvement over clustering in all genes. This method can be used to discover partitions and their biological significance can be determined by comparing with clinical correlates and gene annotations. The MATLAB Many diseases, though, are characterized by small numbers of genes that differentiate between different disease states. Giving equal weight to relevant and irrelevant genes will obscure this difference. Figure et al [Clustering is one of the most common methods for discovering hidden structure in micro-array gene expression data. Clustering of samples has been used to discover new disease taxonomies -3. Clustre et al )In this article we propose an iterative algorithm, where we initially do a clustering using all the genes. This clustering (which gives a binary partition of the samples) is used to select genes that differentiate between the two clusters. The clustering is done again, but this time, only in the set of genes that was selected in the previous iteration. This alternation between clustering and feature selection continues until there is no change in the set of genes (and partition) between two iterations. The final gene set is removed, and the process repeated on the remaining genes to find other partitions. The algorithm generates a set of binary partitions, along with corresponding sets of genes which differentiate the clusters present in these partitions.et al [et al [et al [et al [Similar approaches have been used in other algorithms. Ben-Dor et al  use simuet al  use a Nol [et al  and Tangl [et al  present l [et al ; though We use a Minimal Spanning Tree (MST) based algorithm ,14 for cV = {x1, x2..., xN} be a set of points with distances dij = d(xi,xj) defined between all xi and xj. A tree on V is a graph with no loops whose vertices are elements of V and edge lengths are dij. A minimal spanning tree (MST) is a tree that connects all points such that the sum of the length of the edges is a minimum. An MST can be efficiently computed in O(N2) time  using either Prim's [Let r Prim's  or Kruskr Prim's  algorithδ and denoting the sets of nodes in the two trees as V1 and V2, we have the property that there are no pairs of points (x1,x2), x1 ∈ V1, x2∈ V2such that d(xi,xj) <δ. Define the smallest distance between any two points, one in V1 and the other in V2, as the separation between V1 and V2. Then we have the result that the separation is at-least δ.Deletion of any edge from an MST results in two disconnected trees. Assuming the length of the deleted edge to be δ we are assured of a partition where the two clusters have a separation of at-least δ. This means that if we are interested in looking at all binary partitions with large separations between the clusters, it is sufficient to look at partitions obtained by deleting edges of the MST. Instead of looking at all possible binary partitions (which number 2N-1-1) our algorithm looks only at partitions obtained by deleting single edges from the MST (which number N-1).The significance of this result is that by deleting an edge of length et al have used MST for clustering gene expression data [Minimal Spanning Trees were initially proposed for clustering by Zahn . More reion data .S1, S2 of the sample index set S, with each Sk containing Nk samples, denote by μk the mean of the samples in Sk and μ the global mean of all samples. Also denote by  the j-th sample in cluster Sk. Then the Fukuyama-Sugeno (F-S) clustering measure is defined asTo compare the partitions obtained by deleting different edges of the MST, we use the Fukuyama-Sugeno clustering measure . Given aFS(S) are indicative of tight clusters with a large separation between clusters.Small values of We have considered various other clustering measures. The ideal clustering measure should show local minima at each viable partition and have good performance even with a large number of noisy features. We have found the Fukuyama-Sugeno (F-S) measure to give the best performance in these two respects (Supplementary data – Tthresh are selected. The percentile threshold parameter Pthresh∈(0,100) is used to compute Tthresh. Tthresh is the Pthresh/2-th percentile of a random variable distributed according to Student's t-distribution with mean zero and N-2 degrees of freedom (N is the number of samples). Here we use the t-statistic as a heuristic measure of the contribution of each gene to the selected partition; no statistical significance is implied.For a given partition with two clusters, we can ask if a particular gene shows sufficient differential expression between samples belonging to the different clusters. A gene which is very differently expressed in samples belonging to different clusters can be said to be relevant to the partition or to support the partition. There can be many ways of measuring a gene's support for a partition. Here we use the two sample t-statistic with pooled variance. The t-statistic is computed for each gene to compare the mean expression level in the two clusters. Genes with absolute t-statistic greater than a threshold Tthresh/2. This cutoff increases linearly with the number of iterations until it reaches Tthresh. This is done so that we do not lose any useful genes by putting a too-stringent selection criterion before the partition has evolved close to its final form.The condition for selection of a gene becomes stricter with each iteration. In the first iteration we choose genes with absolute t-statistic greater than Initially, an MST is created using all the genes; then each binary partition obtained by deleting an edge from the tree is considered as a putative partition. The partition with the minimum value of the F-S clustering measure is selected. The t-statistic is used to select a subset of genes that discriminate between the clusters in this partition. In the next iteration, clustering is done in this set of selected genes. This process continues until the selected gene subset converges (remains the same between two iterations), resulting in a set of genes and the final partition. Having identified a partition and the associated set of genes, these selected genes are removed from the pool of genes. This prevents the algorithm from detecting the same partition the next time. The whole process repeats in the pool of remaining genes to find other partitions.xs,g}, the maximum number of partitions to be found MaxNp and percentile threshold Pthresh. Pthresh is used to compute Tthresh. The outer loop of the algorithm runs as long as the number of discovered partitions is less than MaxNp. The set of selected genes F is initialized to be the set of all genes Fset and the cutoff t is initialized as Tthresh /2. In the inner loop, an MST is created using the genes in F, and for all partitions obtained by deleting single edges from this MST, the F-S measure is calculated. For the partition P* with the lowest F-S measure, genes are selected from F based on the t-statistic. These selected genes form the new gene set Fnew. If Fnew ≠ F, the cutoff t is increased and another iteration of the inner loop is performed. If Fnew = F, this means that the gene set has remained unchanged between two iterations and the current partition P* along with the current gene set F is output. The number of discovered partitions is increased and another iteration of the outer loop is performed.The inputs to the algorithm are the gene expression matrix {i.e. generated due to noise and not due to any biological difference) by requiring a minimum of 2M (1 - Pthresh/100) genes in support of a partition ; the algorithm is terminated if there are fewer.Since this is an unsupervised method, the partitions picked might be indicative of biological differences that are relevant, irrelevant (like age or sex of patients) or unknown. We control the detection of chance partitions  then/*Not enough genes support partitions*/exit;endF with metric d;Create MST in feature set Delete edges one at a time and calculate F-S measure for each ensuring binary partition;P* with the lowest F-S measure;Find partition tg for all genes g ∈ F for this partition;Compute t-statistic Fnew to the set of genes {g: |tg| >t};Set If Fnew = F AND t = Tthresh then/*Feature set has converged */P* and F;output F from Fset*//*Remove genes in Fset ← Fset \ F;Np = Np + 1;break;elseF ← Fnew;t;Increase endendi.e. the false detection rate).We first tested the algorithm on synthetic data to compare its performance against a hierarchical clustering method at detecting planted partitions. We also estimated the probability of detection of spurious partitions created by noise  and 1 AML sample while the second cluster contains 24 AML samples  and 1 ALL sample  and sevIf the 252 selected genes were truly discriminatory between the ALL and AML samples, then we should be able to separate the two classes in unknown data by unsupervised clustering using these genes. This was verified by clustering the rest of the samples in the dataset (containing 34 samples corresponding to the "testing set" used in ) using tWe obtained two clusters with the first cluster containing 20 ALL and 1 AML samples while the second cluster contained 13 AML and no ALL samples. This almost-complete separation of ALL and AML in the testing data shows that the genes selected by the iterative clustering are truly supportive of the partition discovered in the training data.The biological differences present in the Lymphoma data-set were originally detected using hierarchical clustering  after maet al use the Jaccard index [The results from the iterative clustering algorithm is compared to that obtained by Overabundance Analysis (OA)  Table 44 and CLIrd index  to measuBoth OA and iterative clustering pick out partitions corresponding to the ALL/AML classification, though OA detects it as the fourth partition while iterative clustering detects it as the first partition. There is a small but definite improvement in the Jaccard index for the results obtained for the Lymphoma data by iterative clustering as compared to OA.Compared to CLIFF, iterative clustering picks a partition in the Leukemia data that is marginally better (2 misclassified as compared to 3 for CLIFF).We have presented a clustering method that uses a minimal spanning tree to lead the search for partitions of samples that form good clusters. Iteration between minimal spanning tree cluster analysis and feature selection is used to converge onto partitions that form well separated clusters and gene subsets that support these partitions.At the convergence of each set of iterations, the result is a partition of the samples and a set of genes that support them. These genes are removed from the pool of genes before searching for other partitions. This removes genes that obscure other partitions supported by smaller numbers of less differentially expressed genes. Genes that support more than one partition will be selected in favor of the partition for which their support is stronger.Testing on synthetic data shows that the algorithm picks out planted clusters with high accuracy and low false positive rate. Application of the algorithm to breast cancer, leukemia and lymphoma data returns partitions with very well separated clusters, some of which have a strong biological significance. The results are comparable to those obtained by other similar algorithms, and superior to those obtained by standard hierarchical clustering.Pthresh. Values of Pthresh close to 100 will give preference to partitions that are supported by a small number of very highly differentially expressed genes. On the other hand, smaller values of Pthresh will preferentially detect partitions that are supported by a large number of genes differentially expressed to a lesser degree. If the first application of the algorithm returns several partitions that are correlated with each other, then we could suspect that there is one partition that is supported by a large number of genes and run the algorithm again with a smaller value of Pthresh to detect all these genes. We have not been able to specify a single value of Pthresh that works in all cases, although the range of values we used (Pthresh = 99.9-99.95) works well in most situations.The kind of partitions discovered depends very much on the value of The partitions and supporting gene sets detected by the use of this algorithm must be further analyzed using gene annotation and clinical data to determine whether they are biologically relevant and worth further investigation. The significance of the detected partitions must be further investigated by evaluating the clinical correlates of patients in different clusters. Clinical observations made on patients, like survival duration, response to therapy and grade of tumor can be compared among the clusters obtained to see if there are any detected partitions whose clusters are correlated with clinical features.Another, complementary, approach is to analyze the genes that are differentially expressed between two clusters for regulatory relationships with each other or prior known influence on the disease in question. Software tools for searching gene annotations , and expThe results of these two approaches can help the biologist to formulate hypotheses about the significance of the partitions as well as the role of the selected genes in influencing the course of the disease. Examples of this process can be seen in  and 2]..2].xs,g~N (0,0.52) for samples s belonging to cluster 1 and xs,g~N (c,0.52) for samples belonging to cluster 2. The rest of the genes are not differentially expressed and are called noise genes and are distributed according to a normal distribution N (0,0.52). If we have k signal genes, each differentially expressed by c between the two clusters, the Euclidean distance between the cluster-means will be .Synthetic data were created by generating normally distributed expression profiles for each gene. Each planted partition is supported by a fraction of the genes  which were differentially expressed between the two clusters. Each signal gene is differentially expressed to the same extent. The expressions were normally distributed; M = 1000 and M = 10000 with varying fraction of signal genes ε and distance between cluster means Dc. Each set of data was analyzed both by the iterative and the hierarchical clustering method (using average linkage). The iterative clustering method was used to obtain the first partition discovered using the Euclidean distance (Pthresh = 99.95). Hierarchical clustering was used to obtain a tree, and the branch at the highest level was split to produce a partition.Sets of synthetic data were generated for number of genes Dc.The match of these two partitions with the true partition was calculated and the detection accuracy was assigned 1 if the match was greater than 75% and 0 otherwise. A logistic regression analysis was used to model the dependence of the probability of detection on the distance between the clusters Pthresh = 99.9. Each sample is a 1000-dimensional vector drawn from a multivariate normal distribution. Thus any clusters detected can be expected to be spurious clusters formed by chance.To estimate the false detection rate, the algorithm was run on synthetic data containing 10 – 100 samples with Pthresh = 99.9) using (1-correlation coefficient) as the distance measure for the MST. For the BRCA data, we also clustered the data using standard hierarchical clustering using centered correlation as the distance metric [For the micro-array data, the iterative method was used to detect the first 10 partitions,  depends only on the distance between the clusters for both hierarchical and iterative clustering.Click here for file


Bone adapts to mechanical strain by rearranging the trabecular geometry and bone density. The common finite element methods used to simulate this adaptation have inconsistencies regarding material properties at each node and are computationally demanding. Here, a volume-based, non-continuum formulation is proposed as an alternative. Adaptive processes corresponding to various external mechanical loading conditions are simulated for the femur.Bone adaptations were modeled for one-legged stance, abduction and adduction. One-legged stance generally results in higher bone densities than the other two loading cases. The femoral head and neck are the regions where densities change most drastically under different loading conditions while the distal area always contains the lowest densities regardless of the loading conditions. In the proposed formulation, the inconsistency of material densities or strain energy densities, which is a common problem to finite element based approaches, is eliminated. The computational task is alleviated through introduction of the quasi-binary connectivity matrix and linearization operations in the Jacobian matrix and is therefore computationally less demanding.The results demonstrated the viability of the proposed formulation to study bone functional adaptation under mechanical loading. Much research effort has been devoted to understanding the functional adaptation of bone under physiological loading ever since the idea of bone functional adaptation was proposed by Wolff more than one hundred years ago -14. VariThe common finite element approach is to take the element densities as the state variables and define elements with either constant or varying densities, then update the material densities for the next step of computation according to the computed strain energy density ,23,26,27One common problem is the inconsistency of material densities on element boundaries . During Some effort has been made trying to solve this problem. For example, a node-based variant of the finite element method was tried with focus on the densities of the nodes rather than densities of the whole elements ,30. The Other previous work has used Voronoi structures  to studyThe long existing problems and the limitations of assuming a continuum drive this new effort to explore the possibility of a non-continuum formulation of bone functional adaptations through nodal analysis in the hope of eliminating the errors present in the previous approaches. In the proposed non-continuum formulation, neighboring nodes are connected by struts that are defined with invariant material densities with respect to time but strut volumes are defined as state variables indicating different configurations of bone structure. The updating of strut volume will depend on the strain energy density in the strut in the previous step. As a result, there is no conflict either in density or in strain energy density. The shift of state variables from bone densities to bone volumes not only eliminates the errors inherent to the density-based finite element approaches but also transforms the continuum formulation to a non-continuum formulation . The advThe proposed volume-based non-continuum formulation shows computational advantages in modeling bone functional adaptations and has much potential for clinical applications in this field.Fig. 3 is used as the maximum value in the present formulation.The nodal density is a percentage relative to its maximum value (0~100%). It is 'converted' based on volume information (BV/TV) for the purpose of easy visual inspection. As suggested by Zhu , the valIn the combined loading case and one-legged stance, the nodal densities are generally high in the proximal area where a considerable number of nodes reach the highest density due to the relatively high load, while in the large distal area, the densities become lower. In the case of abduction, the external load is the smallest out of the three loading cases and the nodal density in this case seldom reaches the maximum. Although the high densities also appear in the proximal area of femoral head, the densities are lower than those of other loading cases. In the large distal area, the node densities generally range at very low levels. In the case of adduction, the highest densities still appear in the proximal area of femoral head, but in the large diaphyseal area, the densities range at very low levels.In summary, one-legged stance and the combined loading case generally result in higher bone densities than the other two loading cases due to higher mechanical loading. The femoral head and neck are the regions where densities change most drastically under different loading conditions while the distal area always contains the lowest densities regardless of the loading conditions.Starting from a uniform material distribution, the program computes the strain energy density, adjusts the strut configurations, and continues on to the next iteration. Fig. A prediction of bone fracture risk has been proposed using stress range levels  from whiA volume-based non-continuum formulation has been developed that describes the adaptation of bone to various mechanical loading situations. In the finite element approach to bone adaptation simulation, the integration of the entries in the coefficient matrix can be a heavy computing task ,23,26,27As mentioned in the introduction, one common problem is the inconsistency of material densities or strain energy densities on element boundaries . Since aAs described above, previous work -33 addreHip fracture is one typical manifestation of osteoporosis, and the results obtained by the simulation indicate that considerable changes of bone structure take place in the regions of femoral head and neck, where the stress level is normally higher than that of distal regions. The variations in stress level as shown in Fig. In the current literature, the time scale for adaptive processes is not very well defined. This general lack of knowledge poses a problem for any experimental proof of concept – while the numbers of strain repetition can be predefined, they must be done within biologically suitable time frames. If a given strain comes too sudden, the bone may break instead of remodel; if the strain is applied over too long a period, it may not be a sufficient stimulus to activate adaptive processes. The lack of well-defined temporal constraints, however, is common. Kim's approach  is very The simulation model presented here may, beyond theoretical calculations, be applied to look at two clinical questions. Firstly, the simulation can be adjusted so that a realistic density distribution is the starting point, and outcomes following certain loading conditions, such as a predefined number of load cycles can then be predicted. Secondly, the program can integrate the measured bone density of a given patient to estimate the fracture risk based on stress level calculations.By eliminating the common inconsistencies at each node, the formulation presented here shows good numerical performance and successfully predicts reasonable bone structure changes under different loading conditions. It is viable to serve as an alternative method apart from the traditional finite element based approached to study bone adaptations. In conclusion, the volume based non-continuum formulation is a new approach to bone adaptation study and has its own advantages. can be obtained though the resultant of the vectorial material components of the struts, as described by equation (1) and (2).In the volume-based non-continuum formulation used here, the trabecular structure is represented by a connected strut system and each strut can take different sizes according to the mechanical loading requirements, that is, strain energy density. The strut representation is shown in Fig. vi is the volume of the i-th strut in the j-th basic unit, Vj is the volume of the j-th basic unit, Rj is the material orientation of the j-th basic unit,  is the orientation of the i-th strut in the j-th basic unit and N is the number of struts in the j-th basic unit.where For the formulation proposed here, the bone structure is decomposed into and represented by a connected network of struts. These struts are a mathematical abstraction of the physical bone structure, from which the BV/TV ratio can be derived. The thickness of a strut is adapted during the bone adaptation process in mimicry of the physiological processes.The bone mass will vary under mechanical loading. In engineering, the general relationship between varying mass, density and volume is described as:, simply vanishes and the mass variation is realized through volume variation under mechanical loading. Based on the density-based adaptation proposed by Zhu X. et al (39), which is stated as:Since the density here is taken as constant regarding time, the second term on the right hand side, the volume-based adaptation can thus be stated as:βi = Ui / ρik, which is a comparative coefficient describing the comparison of a given mechanical stimulus in each sensor cell with the reference value k, and Ui represents the strain energy density for the I-th sensor unit; N is the number of sensor cells and fi(x) is the spatial influence function; B(t) is a remodeling coefficient; α indicates the remodeling power of strain energy density [where  density .With the whole bone represented by a volume-based strut system, the non-continuum formulation can be noted as follows:A is a connectivity matrix describing the connecting relationship between the struts, α is the linearized Jacobian matrix,  is the nodal displacement vector to be solved for and  is the loading vector derived from the external mechanical load. The generalized conjugate residue method is used to solve this formulation [where mulation ,40.Connectivity matrix A is the matrix to show the relationship between connected struts with the entries of 1, -1 or 0. A strut starts from the node with the index corresponding to the entry 1 and ends at the node with the index corresponding to -1. It is further illustrated in Fig. Finally, the different loading conditions to be applied are shown in Fig. The author(s) declare that they have no competing interests.Zhengyuan Wang developed the formulation and partly prepared the manuscript, Adrian Mondry participated in the adaptation controls and partly prepared the manuscript.


To build the complex body plan of higher organisms, thousands of genes must act in a coordinated fashion, becoming active at the right time and in the right place to define structures like head, thorax, and abdomen, or cell types like skin, muscle, and bone. One of the central questions for developmental biologists is how such specific spatiotemporal expression of genes is achieved.The general mechanism of the control of gene expression is well understood: Special proteins, called transcription factors, bind to short stretches of DNA near a gene. By docking to such binding sites, they activate or repress the transcription of the gene into mRNA (which is then translated into protein). Transcription factors often act in a combinatorial fashion—that is, several different factors have to bind in close proximity to each other to achieve a particular transcriptional outcome. As a consequence, their binding sites form clusters, called regulatory elements or modules.Drosophila. Organized in four tiers and acting in combinatorial fashion, the segmentation genes lay out the anterior-posterior axis of the embryo. In a stepwise refinement of expression patterns, they translate broad, overlapping gradients formed by maternally provided transcription factors into a periodic pattern of 14 discrete stripes that prefigure the 14 segments of the larva. The segmentation gene network has long been one of the prime paradigms for studying transcriptional control, and many researchers have worked over the years to experimentally dissect the regulatory interactions within the hierarchy. For some of the most important genes, the regulatory elements driving their expression and the favored binding sites have been identified. Nevertheless, the picture of transcriptional regulation within the segmentation gene network has remained incomplete.In many contexts, the genes that are activated or repressed encode transcription factors themselves, forming a cascade of transcriptional control events. One such transcriptional control hierarchy is the segmentation gene network in the fruitfly Drosophila, the binding sites are typically short and variable, and the search space is large; on the other hand, the fact that sites cluster—where transcription factors work in concert—aids the task.This is where the research reported by Mark Schroeder et al. comes in: With the sequence of entire genomes available, it's possible to use existing binding site information to computationally search the neighborhood of genes for regulatory elements. The difficulty here is that in higher organisms such as To identify regulatory elements, the researchers developed an algorithm, named Ahab, that models the behavior of multiple transcription factors competing for binding sites and fine-tunes the search by detecting clusters of weak sites. Using this approach, Schroeder et al. identified 52 regulatory elements within the segmentation gene network, 32 of them novel. The authors tested a large number of the newly identified modules experimentally by placing them in front of reporter genes that reveal where the modules drive expression within the developing fly. They showed that almost all modules faithfully reproduce the expression pattern of the endogenous gene. To better understand the way segmentation gene modules function, the researchers then systematically analyzed their predicted binding site composition. They correlated the composition of modules with the expression they produce and with the distribution of the transcription factors that bind to them. They were thus able to glean basic composition rules and to derive the mode of action for most of the factors, that is, whether they act as activators or as repressors.Overall, Schroeder et al. show that a computational search can greatly reduce the experimental effort necessary for finding regulatory elements within the genomic sequence. Their study provides an example of how experimental and quantitative methods can be combined to achieve a more global analysis of the regulatory interactions within a transcriptional network.


The etiology of ulcerative colitis (UC) is to be understood. The basic pathological feature of UC is intestinal chronic inflammation. Superantigen, such as Staphylococcus enterotoxin B (SEB), is reported to compromise intestinal barrier function by increasing epithelial permeability and initiate inflammation in the intestinal mucosa. Inasmuch as anatomic position of the sinus, chronic sinusitis-derived SEB may follow the secretion and to be swallowed down to the gastrointestinal tract and induce lesions to the intestinal mucosa.Sinus wash fluid (SWF, containing SEB) was collected from a group of patients with both chronic sinusitis (CS) and UC. A group of mice were sensitized to ovalbumin (OVA) in the presence of SWF. The sensitized mice were challenged with the specific antigen OVA. The inflammatory status of the colonic tissue was determined with histology, serology and electron microscopy. Using horseradish peroxidase (HRP) as a tracer, another group of mice was stimulated with SWF for 2 hours. The HRP activity was detected in the colonic tissue with enzymatic approaches and electron microscopy.2 tissue); (3) increased MPO activity (12.9 ± 3.2 U/g tissue) and inflammatory scores (1.8 ± 0.3); (4) mucosal surface ulcers; (5) edema in the lamina propria; (6) bacterial translocation and abscess formation in the subepithelial region.Epithelial hyperpermeability in colonic epithelium was induced by stimulating with SWF. The HRP activity in the colonic mucosa was almost 11 times more in the SWF treated group (3.2 ± 0.6 μg/g tissue) than the control group (0.3 ± 0.1 μg/g tissue). Mice were sensitized using a mixture of SWF and OVA (serum OVA-specific IgE was detected with a highest titer as 1:64). Challenge with OVA induced extensive inflammation in the colonic mucosa by showing (1) marked degranulation in mast cells (MC, 46.3 ± 4.5%) and eosinophils (Eo, 55.7 ± 4.2%); (2) inflammatory cell infiltration (MC = 145.2 ± 11.4; Eo = 215.8 ± 12.5; mononuclear cell = 258.4 ± 15.3/mmIntroducing Sinusitis-derived SEB-containing SWF to the gastrointestinal tract compromised colonic mucosal barrier function increasing epithelial permeability to luminal macromolecular protein in mice. The SWF facilitated colonic mucosal sensitization to luminal antigen. Multiple challenging the sensitized colonic mucosa with specific antigen OVA induced inflammation, induced a condition similar to human ulcerative colitis. Ulcerative colitis (UC) is a disease characterized by inflammation and ulcers in the mucosa of the large intestine with unknown etiology. The inflammation usually occurs in the rectum and lower part of the colon, but it may affect the entire colon. The inflammation speeds up colonic motility and causes diarrhea. Ulcers form in places where the inflammation has killed the cells lining the colon; the ulcers bleed and produce pus. Theories about what causes ulcerative colitis abound, but none have been proven. The most popular theory is that the body's immune system reacts to a virus or a bacterium causing ongoing inflammation in the intestinal mucosa; others include genetic predisposition, autoimmune disorders and impaired immune regulation -3.over the past 15 years, more than 2000 patients with chronic sinusitis (CS) including some patients with both CS and UC visited our clinic and were treated with different remedies, including medical treatment and functional sinus endoscopic surgery (FESS). Apart from improvement of chronic sinusitis, those patients with both CS and UC showed great improvement of UC as well (data not shown) that couldn't be explained with the specific treatment alone. Therefore, we postulated that there might be an association between CS and UC in these patients. Microbial infection is the most common cause of CS. The microbial products, such as lipopolysaccharide (LPS), staphylococcus aureus enterotoxin B (SEB) ,5 can beStaphylococcus aureus (S. aureus) [S. aureus enterotoxins [Staphylococcus enterotoxin B is an extracellular toxin produced by certain strains of  aureus) . Many carotoxins . In addirotoxins  and haverotoxins . SEB is rotoxins . SEB is rotoxins . SEB canrotoxins  that mayS. aureus, and a quantity of SEB was detected in the nasal cavity of the patients with allergic rhinitis [The nasal cavity and sinus are primary sites of colonization by rhinitis ,16. BaseSinus-wash fluids (SWF) were collected from 32 patients with both CS and UC . We diagnosed CS in the patients as an inflammation of the sinus mucosa with a persistent mucoid or mucopurulent nasal discharge for longer than 3 months that resisted antimicrobial therapy and antral irrigation. Diagnosis was made on the basis of clinical history, rhinoscopic findings, and computed tomographic scan of paranasal sinuses. CS was confirmed by computed tomographic examination that showed diffuse mucosal thickening in the ethmoid or/and maxillary sinuses bilaterally with scores higher than 12 by the Lund-Mackay staging system . Twenty-The diagnosis of UC was based on clinical history, colonoscopy and histology. The history included persistent bloody diarrhea, rectal urgency, or tenesmus. Examinations and sigmoidoscopy and biopsy were performed to confirm the presence of colitis and to exclude the presence of infectious etiologies.Every patient underwent maxillary sinus puncturing and washing. The SWF was collected prior to other procedures. Five ml saline was injected into the sinus cavities and re-collected and stored at -70°C for further use. SEB content in SWF was evaluated with ELISA (All the reagents used in this study were purchased from Sigma unless otherwise mentioned). None of the subjects had recent upper respiratory acute infections. This study was approved by the Ethical Committee of the First Hospital of Shanxi Medical University.For the purpose of verifying our hypothesis of an association between sinus pathology and colitis, an animal model of ulcerative colitis was developed. Mice were sensitized to a model food antigen, ovalbumin (OVA) with or without the presence of SEB-containing SWF. Animal experiments were approved by Animal Use and Care Committee at Shanxi Medical University. Male Balb/c mice (10 week old) were purchased from Beijing Animal Research Institute and maintained in the animal center at Shanxi Medical University. All mice were housed according to guidelines of the animal center. Water was available continuously through automatic ports, and a commercial mouse diet was provided ad libitum.2O2 (pH 7.6) for 30 min, and postfixed with 2% osmium tetroxide for 60 minutes, followed by staining en bloc with 4% uranyl acetate for 30 minutes. Tissue samples were dehydrated through a graded series of ethanol, cleared in propylene oxide, and embedded in Epoxy embedding medium. Thin sections were prepared and stained with 4% uranyl acetate for 5 min and subsequently with 2.5% lead citrate for 2 min and examined at 80 kV with a JEM 1200 electron microscope. HRP containing endosomes were photographed randomly for further analysis.A group of mice was introduced 0.2 ml SWF (containing SEB 50 μg and 10 mg horseradish peroxidase, HRP. SWF was concentrated with the method of ammonium sulfate precipitation for higher content of SEB) via intragastric gavage under a light anesthetization. Mice were killed by cervical dislocation 2 hours later . Control groups were designed as: a naïve control group, treated with HRP 10 mg in 0.2 ml PBS via intragastric gavage; an inactivated-SWF control group, the SWF was pretreated with anti-SEB (100 μg in 0.2 ml) for 30 min, then introduced to the mice in gavage with HRP. Colon was removed; a piece of colon (3 cm) was opened; the contents were collected and dissolved in 1 ml PBS for HRP assay; the colon tissue was snap frozen and stored at -70°C for HRP assay; another piece of colon (2 × 2 × 4 mm) was fixed with 2.5% glutaraldehyde for 2 hours; then rinsed in sodium cacodylate buffer, incubated in 3,3'-diaminobenzidine tetrahydrochlorine and H2EDTA, 1 mM EGTA, 1% Triton, 2.5 mM sodium pyrophosphate, 1 mM β-glycerophosphate, 1 mM Na3VO4 (sodium ortho-vanadate), 1 μg/ml leupeptin) and homogenized on ice; the homogenates and colon content were centrifuged at 1,500 g for 20 minutes at 4°C respectively, the supernatants were collected for HRP assay. HRP amount in colon content and colon tissue was determined by assaying enzyme activity in the collected supernatant according to the previous report [2O2 and 0.025 ml of a solution of o-dianisidine di-HCl (10 mg/ml). The optical density was determined at 460 nm with a spectrophotometer.Frozen colon tissue was weighed and immersed into lysis buffer (20 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1 mM Nas report . BrieflyTen mice were used for each group. A group of mice was sensitized by means of intragastric gavage with 50 μg OVA in 0.2 ml alum-SWF (the adjuvant, prepared with SWF instead of distilled water that contains 50 μg SEB). Another group was also treated with the same protocol, but the SWF in the adjuvant was pretreated with an anti-SEB antibody (100 μg in 0.2 ml). Control groups were designed as: mice were only exposed to OVA without the presence of SWF, or were only exposed to SWF without OVA; naïve controls were treated with 0.2 ml saline. Each mouse was injected with 50 ng pertussis toxin peritoneally.From the 14th day after sensitization, all mice were challenged with 50 mg OVA in 0.3 ml saline by means of intragastric gavage 3 times, 48 h apart. Diarrhea was determined by visually monitoring mice for up to 1 hour following intragastric challenge. Mice demonstrating profuse liquid stool were recorded as diarrhea-positive animals. Two days after the last challenge, mice were sacrificed by decapitation; blood samples were collected, serum was separated and stored at -70°C for further analysis. Colon was removed, one piece was fixed with 4% paraformaldehyde for histology; one piece was snap frozen for MPO analysis; one piece was fixed with Carnoy solution for mast cell count; one piece was fixed with 2% glutaraldehyde for electron microscopy.2O2 in 1 minute at 25°C.Myeloperoxidase (MPO) activity was determined according to the method of Bradley et al . Tissue 2 of mucosa. All sections were coded to avoid observer bias. The degree of inflammation on microscopic tissue sections was scored as follows: : 0, no leukocyte infiltration; 1, low level of leukocyte infiltration; 2, moderate level of leukocyte infiltration; 3, high vascular density and thickening of the colon wall; and 4, transmural leukocyte infiltration, loss of goblet cells, high vascular density, and thickening of the colon wall. Grading was done in a blinded manner [The morphology of the epithelium, villi, and subepithelial layer of colon mucosa were assessed, and the numbers of eosinophils and mononuclear cells were counted in 10 randomly selected fields (magnification, ×400) for each mouse (100/group). To determine mast cell numbers, tissues were fixed in Carnoy's fixative and paraffin sections were stained with 0.5% toluidine blue. Mast cells were counted in 10 fields for each mouse (100/group). Cell numbers were expressed per mmd manner .Colon tissue was processed with routine procedures and observed with an electron microscope. Activation of mast cell and eosinophil in colonic mucosa was determined by the phenomenon of degranulation .Passive cutaneous anaphylaxis (PCA) was employed to determine serum OVA-specific IgE level. The serum was diluted in phosphate-buffered saline (PBS) from 1:4 to 1:128. The mice were injected intradermally with 50 μl serum into each of 4 dorsal shaved skin sites. The sites were outlined with a water-insoluble red marker. Forty-eight hours later each mouse received an injection of 0.1 ml OVA (1 mg/ml) containing 4% Evans blue via the tail vein. The results were recorded thirty minutes after the challenge; the diameter of the blue spot on the injection sites larger than 6 mm was recorded as positive reaction.t test was used to compare the difference between groups. p < 0.05 was accepted as significant criteria.Data were expressed as mean ± SD; Student The content of SEB was significantly higher in the SWF of patients with both UC and CS (from 30.5 to 565.6, with an average of 154.5 ± 81.7 pg/ml) than in the nasal wash fluids (from 0 to 18.6, with an average of 8.5 ± 4.3 pg/ml) of healthy control subjects.Horseradish peroxisase activity was detected in colonic content in both control mice (group of HRP-only and group of anti-SEB treated SWF) and SWF treated mice Fig . The HRPPassive cutaneous anaphylaxis (PCA) is the gold standard method to measure allergen-specific IgE antibody levels in mouse models of allergy. The present study demonstrated that only the group treated with both SWF and OVA showed positive results with the highest titer 1:64. No specific IgE was detected in the serum of mice in control groups.Activation of mast cells and eosinophils was observed as degranulation of the granules in the cytoplasm. Piecemeal type degranulation was observed more often than the anaphylactic type in the mast cells in the colonic mucosa of the sensitized mice with EM. Crystal core degranulation and matrix degranulation were observed in eosinophils of the sensitized mice. Granules were categorized intact or degranulated for each mast cell or eosinophil. Each type granule was numerated with EM. After challenge with specific antigen OVA, degranulation was observed in both mast cells and eosinophils of the sensitized mice. The ratio of degranulation was much higher in the sensitized mice compared with controls Fig .Challenge with specific antigen OVA resulted in inflammatory cell infiltration in the lamina propria and subepithelial regions. The infiltrate consisted of mainly mast cells, eosinophils and mononuclear cells. Compared with control, the sensitized group showed significantly more inflammatory cell infiltration, most of them might be activated immune cells Fig . CapillaInflammatory status in the colonic mucosa was assessed by colonic tissue MPO measurement, inflammatory cell infiltration and mucosal surface condition observation. MPO is a critical parameter that indicates activation of neutrophils. We observed a significant increase in MPO and inflammatory score in colonic mucosa after challenge with OVA in the mice treated with SWF and OVA Fig .Electron microscopy revealed ultrapathology in the sensitized and OVA-challenged colonic mucosa that included: (i) epithelial cell destruction Fig ; (ii) baFollowing the protocol of challenge with specific antigen OVA, the sensitized mice developed diarrhea 15–30 minutes after each challenge that lasted up to 1 hour. The number of diarrhea episodes (3 to 7 times) increased in parallel with the number of OVA challenges. No diarrhea was noted in control mice although those mice also received the same number of OVA challenges. Diarrhea was also noted by direct observation of the colon and cecum; the liquid stool observed following OVA challenge-induced diarrhea in the sensitized mice contrasts with the solid pellets seen in the distal colon of other mice in control groups.Our knowledge about the etiology of ulcerative colitis is still limited. Although some theories about its origins have been advanced, such as genetic predisposition, autoimmune disorders, infection, and so on ,24, the S. aureus in sinuses is frequently encountered [Rhinosinuses are empty cavities lined with mucosa. The anatomic feature, only having a small ostium, makes them very easily to be blocked and subsequently infected. Infection with ountered ,5. Thus,There are many toxic substances in the secretions from chronic sinusitis. SEB is one that has been well characterized. The unique feature of SEB is that it can down regulate intestinal barrier function ,13, actiIntestinal epithelial cells form a barrier between the luminal contents and the subepithelial region. The barrier restricts substances to be absorbed. It only allows some small molecules such as water to pass it freely. Antigens are macromolecular proteins that are not allowed to be absorbed before being digested to small peptides or amino acids under normal physiological conditions. But in reality, intact antigens do pass the intestinal barrier to reach lamina propria to induce inappropriate immune reactions under certain circumstance. How antigens cross the intestinal epithelial barrier is still a mystery. By introducing both SWF and HRP to mouse gastrointestinal tract, intact HRP in the colonic tissue was increased nearly 11 times compared to control. The results implicate that superantigen SEB is one of the factors that facilitate antigens to be absorbed without destroying their antigenicity. Lu et al also reported that SEB significantly increased colonic mucosal permeability in a mouse study .We have begun to appreciate that food allergy plays a role in the inflammation of intestinal mucosa . The resIn chronic allergic diseases such as asthma, during continuous antigen exposure, eosinophils are primed by IL-5 and attracted by chemokines, infiltrating the local tissue . We alsoMarked mast cell hyperplasia in the colonic tissue was observed in this study. In general, mast cell numbers in tissues are relatively constant, even though mast cell hyperplasia is observed in both the inflammatory and in repair/remodeling stage of various inflammatory disorders . The funStriking epithelial damage of colonic mucosa of the sensitized mice after challenge with OVA was observed in this study. This phenomenon has been well-documented in airway allergy . AlthougIn summary, we reported a murine model of ulcerative colitis in this paper that was induced with sinusitis-derived SEB and OVA sensitization followed by repeat challenges with specific antigen OVA. The histopathology of the colonic mucosa included inflammatory cell infiltration, activation of mast cell/eosinophil, epithelial barrier damage, micro-ulcer formation on the surface of colonic mucosa, bacteria translocation and abscesses formation in the subepithelial region.UC, ulcerative colitis; CS, chronic sinusitis; FESS: functional endoscopic sinus surgery; OVA, ovalbumin; SEB, Staphylococcus enterotoxin B; MPO, myeloperoxidase; HRP, horseradish peroxidase.The author(s) declare that they have no competing interests.PCY was involved in study design, histology, EM observation, data analysis and manuscript preparation; CSW and AZY were involved in SWF collection and animal model.The pre-publication history for this paper can be accessed here:


CpG-oligonucleotides (CpG-ODN), which induce signaling through Toll-like receptor 9 (TLR9), are currently under investigation as adjuvants in therapy against infections and cancer. CpG-ODN function as Th-1 adjuvants and are able to activate dendritic cells. In humans TLR9 has been described to be strongly expressed in B-lymphocytes, monocytes, plasmacytoid dendritic cells and at low levels in human respiratory cells. We determined whether a direct interaction of bacterial DNA with the tumor cells themselves is possible and investigated the expression and function of TLR9 in human malignant solid tumors and cell lines. TLR9 expression by malignant tumor cells, would affect treatment approaches using CpG-ODN on the one hand, and, on the other hand, provide additional novel information about the role of tumor cells in tumor-immunology.in situ hybridization, RT-PCR and DNA-sequencing. Apoptosis and chemokine expression was detected by FACS analysis and the Bio-Plex system.The expression of TLR9 in HOPE-fixed non-small lung cancer, non-malignant tissue and tumor cell lines was assessed using immunohistochemistry, confocal microscopy, We found high TLR9 signal intensities in the cytoplasm of tumor cells in the majority of lung cancer specimens as well as in all tested tumor cell lines. In contrast to this non-malignant lung tissues showed only sporadically weak expression. Stimulation of HeLa and A549 cells with CpG-ODN induced secretion of monocyte chemoattractant protein-1 and reduction of spontaneous and tumor necrosis factor-alpha induced apoptosis.Here we show that TLR9 is expressed in a selection of human lung cancer tissues and various tumor cell lines. The expression of functionally active TLR9 in human malignant tumors might affect treatment approaches using CpG-ODN and shows that malignant cells can be regarded as active players in tumor-immunology. Toll gene, the expression of one of it's relatives we are reporting here concerning human malignant tumors, originally was characterized for its role in specifying dorsoventral polarity of the Drosophila embryo[Toll are also present in plants, mammalian toll-like genes are products of an ancient evolutionary process beginning before the separation of animals and plants [Drosophila thus far nine toll-like genes were identified, ten different human toll-like genes are currently described. In contrast to Drosophila, the mechanisms taking place in mammalian embryogenesis concerning TLR are widely unknown. The discovery of immune function for Toll in Drosophila led to a new understanding of innate immunity mechanisms.The la embryo. Since hd plants . Within Human TLR recognize pathogen-derived products, also termed pathogen-associated molecular patterns (PAMP) . These aDifferent studies show an immunostimulatory capacity of bacterial components which can mediate anti-tumor activity. The first reported use of such a therapy for a nonbacterial disease took place 1890, evaluating the anti-tumor activity of living streptococci directly injected into tumor masses . ShimadaIn order to characterize possible interactions between malignant cells and CpG-ODN, we investigated whether TLR9 is present in malignant tumors. A variety of malignant solid tumors and cell lines were tested for TLR9 expression; in addition, we examined direct effects of CpG-ODN upon apoptosis and chemokine production of tumor cells.Samples of human tumors and tumor-free tissues were obtained from lobectomies because of lung cancer. Tumor-free tissues were taken at least 5 cm away from the tumor-border. The specimens were fixed and paraffin-embedded using the HOPE-technique . SectionFor increased comparability of the staining intensities in malignant and non malignant cells we additionally performed IHC on tumor-bearing and tumor free lung tissues which have been assembled on one slide by use of a mechanical tissue arrayer device (MTA1, Alphametrix, Germany).2 polystyrene flasks with Dulbecco's modified Eagle's medium DMEM (Sigma) with 10 % heat-inactivated fetal calf serum (PAA Laboratories), 100 μg/ml penicillin G, 100 μg/ml streptomycin and 2 mM L-glutamine (Sigma), maintained under 5 % CO2 by routine passage every 3 days. Cells were seeded in 35-mm dishes (Nunc).A549 cells and HeLa cells were grown in 25 cmFor IHC cells were cytocentrifuged and treated by the HOPE-technique , the celTotal RNA was extracted from lung tissues according to the manufacturer's recommendations (RNeasy, Qiagen). After destroying residual DNA with DNase (Invitrogen), cDNA was synthesized by reverse transcription . PCR wasHybridization, detection of signals and controls were carried out as previously described (concentration of probe 2 ng/μl, hybridization temperature 46°C) ,22.Primary antibody (mouse anti-human TLR9, clone 26C593, Imgenex) was applied in a dilution of 1/100 in PBS for 16 h at 4°C. Negative controls comprised omission of the primary antibody. Detection was performed by horseradish-peroxidase labeled streptavidine-biotin technique (LSAB2, Dako) .A549, HeLa, BEAS 2b, U937, and NCI-H727 cell lines were used. RT-PCR was performed like described above using TLR9 specific primers  spanning an amplicon of 260 bp. Mononuclear cells were included as positive control as well as RT-PCR targeting glyceraldehyde-3-phosphate dehydrogenase (GAPDH)  (not shown). PCR-products were separated along with a molecular weight marker (MW8, Roche) using 2 % agarose gels Fig. .A549-cells were seeded in 35-mm glass bottom dishes (MatTek Corp.) overnight. Cells were transfected with GFP-huTLR9 using Polyfect (Qiagen) according to the manufacturer's instructions or incubated in medium.Cells were washed in tris-buffered-saline, containing 0.2 % Tween 20 (TTBS), fixed with 4 % paraformaldehyde in phosphate-buffered-saline (PBS) for 10 min on ice, and permeabilized with 0.25 % Triton-X100 (Roche) in PBS for 10 min. Cells were washed with TTBS, blocked with 10 % bovine-serum-albumine (BSA) in TBS for 20 min, and incubated with primary antibody (clone 26C593, Imgenex) or isotype (Mouse IgG1, Jackson ImmunoResearch Laboratories) 1:150 in TBS 10 % BSA for 30 min. Cells were washed with TTBS, incubated for 30 min with Alexa-568/goat-anti-MouseIgG1 (Molecular Probes Inc.) 1:500 in TBS containing 10 % BSA, and washed with TTBS. Counterstaining was achieved using TOTO-3 1:500 in TBS containing 10 % BSA. Cells were washed with TTBS, fixed again as above, mounted and analyzed using a confocal laser microscope. The GFP-TLR9 plasmid was kindly provided by Terje Espevik, Trondheim, Norway.For CpG-ODN stimulation the M362 sequence was used in a concentration of 1 μM; as control M383 was used as described by Hartmann et al.  (MWG-BioAnnexin-V FITC apoptosis kit I and PE-conjugated active caspase-3 apoptosis kit I were used according to the manufacturer's instructions (BD Pharmingen). TLR9 antibody and isotype control (eBioscience, clone: eB72-1665) were stained after fixation and permeabilization using Intraprep (Beckmann Coulter) according to the manufacturer's instructions. Flowcytometric data  collected from 10,000 cells are reported as percentages of positive cells (Becton Dickinson).Cell culture supernatant (50 μl per sample) was analyzed using the Bio-Plex system and a Luminex 100TM analyzer (BioRad) according to manufacturer's instructions.2 for 24 h, and either stimulated or not stimulated with 1 μM of CpG-ODN (M362 sequence). These blocks from adjacent locations of the same lung-tumors were fixed using the HOPE-technique and paraffin embedded. RT-PCR was carried out like described above using primers targeting human MCP-1  resulting in a 149 bp PCR-product and GAPDH  resulting in a 257 bp PCR-product. PCR products were separated on 2 % agarose gels along with a molecular weight marker (pBR322-Msp1) and the results displayed in figure Tissue blocks from lung cancer specimens (edge length approximately 0.5 cm) were cultivated in RPMI 1640 at 37°C and 5 % COin situ hybridization (ISH). The generation of TLR9-signals was achieved within 10 minutes, whereas unspecific signals were not detected in the control preparations. We found high signal intensities for TLR9 transcripts in the cytoplasm of tumor cells in the majority of lung cancer specimens. Immunohistochemistry (IHC) revealed strong TLR9 protein expression within tumor cells of tissues and cell lines. In contrast normal lung tissues sporadically showed weak expression of TLR9 mainly in cells revealing morphological characteristics of alveolar macrophages and alveolar epithelial cells as displayed in figure To investigate the expression of TLR9 in human lung tumors and lung tumor cell lines we used the recently described HOPE-fixation method. HOPE-fixed  specimenA cytoplasmic localization of TLR9 was confirmed by confocal microscopy fig. . This fiThe expression of TLR9 in tumor cells and cell lines rises up the question, whether this receptor is functional active in these cells. As shown in figure Here we investigated, whether CpG-ODN can modulate their own receptor. We found no differences in TLR9 expression with and without CpG-ODN stimulation. However, in the presence of TNF-α/CHX the expression of TLR9 was strongly reduced, whereas CpG-ODN stimulation counteracted this downregulation Fig.  and 3b.In order to obtain further information about the functional activity of TLR9 in tumors we studied cytokine release upon CpG-ODN stimulation. The measurement of cytokines from stimulated HeLa and A549 cells revealed a significantly enhanced release of monocyte chemoattractant protein-1 (MCP-1) after 24 h of stimulation in response to CpG-ODN or TNF-α Fig. . The proex vivo experiments. We conclude the expression of a functionally active TLR9 in human malignant tumors.By application of a novel fixation technique we specify for the first time the expression of TLR9 protein and mRNA in a selection of human non small cell lung cancer tissues as well as cell lines. Stimulation of the TLR-9 expressing cell lines A549 and HeLa with CpG-ODN showed a marked antiapoptotic effect. In addition, there was substantially enhanced release of MCP-1 from the cell lines upon CpG-ODN stimulation which was also shown in Spaetzle. Such a ligand could play a role for the activation of human TLR9. Whether the expression of TLR9 in human malignant cells takes advantage of TLR9-function in embryogenesis therefore remains unclear.The presence of molecules involved in ontogenesis e.g. the carcinoembryonic antigen (CEA) is frequently observed in malignant tumors suggesting a kind of "shift-back" towards earlier developmental stages . The sigOn the other hand TLR9 in malignant cells could have similar functions as in cells of the innate and adaptive immune system. In humans TLR9 has been described to be mainly expressed in B-lymphocytes, monocytes and plasmacytoid dendritic cells . In addiThe CpG-ODN sequence M362 used in our study is known to potently activate TLR9-expressing immune cells in humans including plasmacytoid dendritic cells and B cells as shown by Hartmann et al.  B cells These mechanisms are both reflected in the results we obtained in our study after CpG-ODN stimulation of malignant cells:in vivo showed anti-tumor effects. However, this anti-tumor effect appears to be effective indirectly and is related to enhanced NK cell activity. In a murine model of lymphoma the immunostimulatory effect of CpG-ODN was demonstrated to be responsible for the observed anti-tumor effects [et al. have shown that CpG-ODN in vivo induced rejection of neuroblastoma xenografts [in vivo requires further investigation.Firstly, stimulation of the A549 and HeLa cells with CpG-ODN showed an antiapoptotic effect. This was demonstrated for spontaneous as well as induced apoptosis with TNF-α and CHX after 24 h. Our observation is consistent with previous evidence in other cell lines. Yi et al. demonstrated antiapoptotic effects of CpG-ODN in a mouse B lymphoma cell line , and sim effects . Carpentnografts . In contnografts . To whatin vitro in this study could further enhance this scenario.Secondly, tumor cell lines (A549 and HeLa) stimulated with CpG-ODN showed strong secretion of the CC chemokine MCP-1. Furthermore a similar effect was observed in the investigated tumor tissues. Immunostimulatory properties together with anti-tumor activity of bacterial DNA were initially reported for a DNA fraction derived from mycobacteria by Tokunaga and coworkers . It is kRegarding TLR9 expression in nonmalignant lung tissue our data confirm the findings of low TLR9 expression in respiratory cells of Platz et al. , who havin vitro and ex vivo experiments, our data provide evidence for the second as well as the third possibility; the sum effect of these two counteracting mechanisms in an in vivo setting can not be estimated from these experiments and could even differ from tumor entity to tumor entity.Biological explanations for the TLR9 expression in malignant cells require further investigations. Three possibilities are conceivable: Either this could represent a bystander phenomenon, a side effect of a pathway functional to a different purpose. Secondly the upregulation of TLR9 could be beneficial to the tumor, promoting tumor cell survival. Thirdly, it even might help immune control strategies of the organisms an element of a pathway directing defense mechanisms against malignantly transforming cells. While the first possibility seems unlikely in the light of our findings of a functionality of the receptor in various In conclusion, we showed in a selection of samples that human malignant tumors express functionally active TLR9 and respond to CpG treatment with prolonged survival and chemokine release. This might influence the effects of CpG-ODN based anti-tumor therapies. Broad screening approaches will be worthwhile to further substantiate these initial results.While recent strategies in tumor-immunology mainly target a strengthening of the host-defense, we provide evidence that the malignant cells themselves can be regarded active players in the complex struggle between tumor and host. In any case CpG-ODN based anti-tumor therapies should be reconsidered in the light of our findings since CpG-ODN products are currently in Phase I/II clinical trials both as a monotherapy and as part of multi-drug regimens.DD carried out the flow cytometry and cytokine assays and was involved in the design and coordination of the study and drafting the manuscript. DA and AJU carried out the confocal microscopy, RT-PCR with cell lines and were involved in drafting the manuscript. JG was involved in immunohistochemistry of cell lines and the design of the study. DB conducted the surgical part of the study. EV conducted the pathological part of the study and was involved in the design of the study. KD and PZ conducted the clinical part of the study and were involved in the design and coordination of the study. TG performed the immunohistochemistry, in situ hybridization and RT-PCR with tissues and conceived of the study. All authors read and approved the final manuscript.


SV40 DNA replication system is a very useful tool to understand the mechanism of replication, which is a tightly regulated process. Many environmental and cellular factors can induce cell cycle arrest or apoptosis by inhibiting DNA replication. In the course of our search for bioactive metabolites from the marine sponges, psammaplin A was found to have some anticancer properties, the possible mechanism of which was studied.in vitro. The activities of topoisomerase I and polymerase α-primase were measured by the relaxation of superhelical plasmid DNA and the incorporation of [3H]dTTP to the template respectively. The ssDNA binding activity of RPA was assessed by Gel Mobility Shift Assay (GMSA).Cell viability was determined by Cell Counting Kit-8 (CCK-8) to count living RAW264.7 cells by combining 2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfophenyl)-2H-tetrazolium (WST-8) and 1-methoxy-phenazine methosulfate (1-methoxy-PMS). The effect of psammaplin A on DNA replication was carried out in SV40 DNA replication system in vitro, in which polymerase α-primase is one of its main targets.We have found that psammaplin A delivers significant cytotoxic activity against the RAW264.7 cell line. It was also found that psammaplin A could substantially inhibit SV40 DNA replication Taken together, we suggest that psammaplin A-induced cytotoxicity may correlate with its inhibition on DNA replication. Psammaplin A has the potential to be developed as an anticancer drug. DNA replication in eukaryotic cells is a tightly regulated process . The regMuch of what is currently known about the mechanism of DNA replication in eukaryotic cells has come from studying SV40 and related viruses. SV40 virus can use the host replication machinery for its own DNA replication together with the virally encoded SV40 T-antigen. SV40 T-Ag is a multifunctional regulatory protein with numerous biochemical activities, and it has been classified as a member of superfamily III helicase and can unwind dsDNA and RNA . All othPsammaplysilla sponge [in vitro antibacterial activity against both Staphylococcus aureus (SA) and methicillin-resistant Staphylococcus aureus (MRSA), which was inferred to be the result of induced bacterial DNA synthesis arrest by psammaplin A through inhibiting DNA gyrase [Psammaplin A is a symmetrical bromotyrosine-derived disulfide dimer that was originally isolated in 1987 from the a sponge . Early sA gyrase . Given tA gyrase , farnesyA gyrase , leucineA gyrase , and latA gyrase . Among tin vitro SV40 DNA replication system. According to our result that psammaplin A can induce eukaryotic DNA replication arrest through inhibiting some important replication proteins, we suggest that psammaplin A-induced cytotoxicity may correlate with its inhibition on DNA replication, and one of the main target molecules could be DNA polymerase α-primase.We have reported that psammaplin A displayed significant cytotoxicity against human lung (A549), ovarian (SK-OV-3), skin (SK-MEL-2), CNS (XF498), and colon HCT15) cancer cell lines [ cancer c+), SV40 T-Ag, topoisomerase I (topo I), human DNA polymerase α-primase (pol α-primase), replication protein A (RPA), and HeLa extract were prepared as described previously [Psammaplin A sample was a gift from a Dr. Jung's lab of Pusan National University. SV40 origin-containing circular duplex DNA  and Fetal Calf Serum (FCS) was from Gibco-BRL (Gaithersburg, MD, USA). Cell Counting Kit-8(CCK-8) was purchased from Dojin Laboratories (Kumamoto, Japan). The mouse macrophage cell line RAW264.7 was purchased from Korean Cell Line Bank (Seoul, Korea).5 cells/well. After incubation with the indicated concentrations of psammaplin A for 12 hr, 10 μl of kit reagent was added and incubated for a further 3 hr. Cell viability was obtained by scanning with a microplate reader at 450 nm.Cell viability was determined by CCK-8 to count living cells by combining WST-8 and 1-Methoxy PMS . Briefly2, 0.5 mM DTT, 4 mM ATP, 200 μM UTP, GTP, and CTP, 100 μM dATP, dGTP, and dCTP, 25 μM [3H]dTTP (300 cpm/pmol), 0.6 μg of SV40 T-Ag, 0.23 μg of pUC-ori+, HeLa extracts, and psammaplin A as indicated. The reactions ran at 37°C for 2 hr, after which the acid-insoluble radioactivity was measured [The reactions were carried out as described previously . In briemeasured .2, 0.5 mM DTT, 0.5 mM EDTA, bovine serum albumin (30 μg/ml), pUC118 (20 μg/ml), topo I (1 unit), and various amount of the psammaplin A. After 30 min at 30°C, the reactions were stopped by the addition of 5 μl of 5% NaDodSO4/25% (wt/vol) Ficoll 400 (Pharmacia) containing 0.25 mg of bromophenol blue per ml. The samples were then loaded onto the agarose gel (0.8%) for electrophoresis followed by photography.Topo I was measured by the relaxation of superhelical plasmid DNA . The 20 2, 0.5 mM DTT, 10% glycerol, 50 fmol of 5'-32P-labeled oligo(dT)50 (2200 cpm/fmol), plus the indicated amount of RPA, and was incubated for 15 min at room temperature. The complex was electrophoretically separated on a 5% polyacrylamide gel in 0.5 × TBE (89 mM Tris borate, 2 mM EDTA) at 15 V/cm. The gel was then dried and exposed to X-ray film.The assay was performed according to the published procedures . The rea2, 0.5 mM DTT, 6 μg of bovine serum albumin, 4 mM ATP, 33 μM of [3H]dTTP (500 cpm/pmol), 0.1 μg of poly (dA)4500: oligo (dT)25 (20:1), DNA pol α-primase, and psammaplin A as indicated. After incubation at 37°C for 30 min, acid-insoluble radioactivity was determined [DNA pol α-primase activities were assayed as described previously  with thetermined .Values are presented as mean ± SD. Data was initially analyzed by one-way analysis of variance (ANOVA) and comparison of groups was made using Turkey test (SPSS software).in vitro antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA). Psammaplin A is rather interesting owing to its two identical domains which are linked through a disulfide bridge. Macrophage cells are one of the key players in the early innate immune response, and they release inflammatory chemicals known as cytokines when they are activated. This sort of inflammation is not always a good thing, and overactive macrophage cells have been implicated in a number of human diseases, including arthritis and sepsis. When we studied the effect of psammaplin A on the viability of macrophage cell line RAW264.7, a reduced cell count was observed in the psammaplin A-treated cells and this decrease in the number of living cells also showed good dose-dependent  that is required for replication. The former mechanism is unlikely, because we have directly checked the effect of psammaplin A on DNA and didn't find any detectable damages to the template (data not shown). In order to find what proteins in DNA replication were affected by psammaplin A, we checked topo I activity at first, which plays key roles in DNA replication, transcription, and recombination by forming transient DNA single-strand breaks and acting as DNA strand transferase. In addition, the topoisomerase is now considered to be an important cancer chemotherapeutic target. The inhibitory effect of psammaplin A on the catalytic activity of topo I was shown in Fig Nicolaou and his colleagues reported that the DTT present in many enzyme assays could reduce the disulfide bond of psammaplin A to the corresponding free thiol . In thei50, which appeared as two distinct bands in the polyacrylamide gel. The ssDNA binding activity of RPA was inhibited by psammaplin A in a concentration-dependent manner, and 500 μM of psammaplin A totally inhibited the ssDNA-binding activity of RPA.In replication, RPA mediates unwinding of SV40 origin-containing DNA in the presence of SV40 T-Ag and topo I. It interacts with SV40 T-Ag and the DNA pol α-primase complex, which is necessary for the initiation of SV40 DNA replication ,9. Here,As described above, DNA pol α-primase complex is necessary for the initiation of SV40 DNA replication. To further investigate the inhibitory effect of psammaplin A in replication, we tested psammaplin A for inhibition of pol α-primase activity to see whether it's inhibitory effect on DNA replication correlate with pol α-primase activity. As shown in Fig in vitro system attempting to find out the target process and molecules of psammaplin A and have a glimpse on cell cycle regulation.Psammaplin A has exhibited inhibition on general bacterium, some actinomycetes and fungi, and it also has cytotoxicity toward several cancer cell lines. In our research, we found that psammaplin A deliver significant cytotoxic activity against macrophage cell line RAW264.7. Which process is mostly affected by psammaplin A in cell cycle? What is the mechanism of the inhibition? Enlightened by the inhibitory effects of psammaplin A on bacterial DNA synthesis, bacterial DNA gyrase and eukaryotic topo I, we investigated the effect of psammaplin A on DNA replication using SV40 replication in vitro. In order to clarify the inhibition mechanism, further work were carried out. In SV40 DNA replication, three factors, SV40 T-Ag, RPA, and pol α-primase complex, are essential for initiation process. In the presence of topo I, SV40 T-Ag will continue to unwind the DNA to form a highly unwound DNA [In our study, we found that psammaplin A inhibited SV40 DNA replication ound DNA . DNA synound DNA . We haveound DNA . Psammapin vitro DNA replication system includes a large number of proteins, while in topo I assay, RPA binding assay and pol α-primase assay, purified proteins were used. Many of the proteins in the crude extracts can affect each other by physical or functional interactions. For example, in the process of DNA replication initiation, RPA interacts with T-Ag and DNA pol α-primase, and it is believed that RPA can both stabilize the unwound DNA and stimulate DNA pol α. The universal protein-protein interactions in crude extracts make its working environment quite different from that of purified protein assay system. Second, even for the same protein, for example, pol α-primase, the concentration in the cell extract and in the purified pol α-primase assay is not comparable before any quantification of the replication proteins in the cell extract. Third, due to the active disulfide moiety in the structure of psammaplin A, it is possible that psammaplin A could interact with some particular cellular targets in the crude extract, which may lead to covalent modification of the biological targets and psammaplin A itself. Therefore, the free available concentration of psammaplin A in the crude extract might be different from that in purified protein assay system.It is puzzling that the DNA pol α-primase, RPA and topo I were readily inhibited by low concentration of psammaplin A whereas the SV40 DNA replication assay still showed about more than 80% DNA replication in the presence of 125 μM psammaplin A. In our opinion, at least three points could account for this discrepancy. First, the cell extract we have used to support in vitro replication, the significant inhibition of psammaplin A on the viability of macrophage RAW264.7 cells occurred at a relatively low concentration. There is the possibility that DNA replication might not be the single or primary event that affected by psammaplin A. It should be evident that the event of DNA replication in a living cell is more complicated than that in the crude extract system because of many existing cell cycle signals. So, it is still vague whether the ability of psammaplin A to inhibit cellular viability is correlated with its ability to inhibit DNA replication. In order to make it clear, it is necessary to check the effects of psammaplin A on other macromolecular synthesis (RNA synthesis and protein synthesis). Although the results in this paper do not clearly define the mechanism of cytotoxicity of psammaplin A, they have convincingly shown that psammaplin A possesses the abilities to inhibit DNA replication and some important replication proteins.Different from the effect of psammaplin A on SV40 DNA in vitro assay conditions in this study, we can find that all these assays were performed in similar conditions (for example: pH 7.5~7.7, 0.5 mM DTT and Mg2+ ion environment). Given the mildness and tolerance of these reaction mixtures, we guess that psammaplin A is stable in the assays. Of course there is the second possibility that psammaplin A was reduced in the reaction, but the reduction product had nearly the same inhibition effect on topo I as the original compound. Further investigation aimed at this question need to be carried out.Because of the disulfide bridge linking two identical subunits in the structure of psammaplin A, in this study, we also paid attention to the potential reduction effect of DTT present in the topo I assay. Different from the report of Nicolaou , we didnBased on our results, we suggest that the cytotoxicity of psammaplin A might be related to the inhibitory effect it has on the fundamental cellular process-DNA replication, and one of the main target molecules of psammaplin A could be pol α-primase.The authors declare that they have no competing interests.YHJ and DKK conceived and designed the experiments and wrote the manuscript. YHJ performed all of the experiments. JHJ provided psammaplin A sample. EYA, SHR, JSP, HJY, SY, BJL and DSL participated in the conception, supervision, coordination and guidance of the study and manuscript preparation. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here: