Inhibition of macrophage migration inhibitory factor decreases proliferation and cytokine expression in bladder cancer cells

BACKGROUND: The importance of various inflammatory cytokines in maintaining tumor cell growth and viability is well established. Increased expression of the proinflammatory cytokine macrophage migration inhibitory factor (MIF) has previously been associated with various types of adenocarcinoma. METHODS: MIF IHC was used to localize MIF in human bladder tissue. ELISA and Western blot analysis determined the synthesis and secretion of MIF by human bladder transitional cell carcinoma cells. The effects of MIF inhibitors (high molecular weight hyaluronate (HA), anti-MIF antibody or MIF anti-sense) on cell growth and cytokine expression were analyzed. RESULTS: Human bladder cancer cells (HT-1376) secrete detectable amounts of MIF protein. Treatment with HA, anti-MIF antibody and MIF anti-sense reduced HT-1376 cell proliferation, MIF protein secretion, MIF gene expression and secreted inflammatory cytokines. Our evidence suggests MIF interacts with the invariant chain, CD74 and the major cell surface receptor for HA, CD44. CONCLUSIONS: This study is the first to report MIF expression in the human bladder and these findings support a role for MIF in tumor cell proliferation. Since MIF participates in the inflammatory response and bladder cancer is associated with chronic inflammatory conditions, these new findings suggest that neutralizing bladder tumor MIF may serve as a novel therapeutic treatment for bladder carcinoma

Risk perception of arsenic exposure from rice intake in a UK population

In the UK, consumption of rice and rice-based products is on the rise but, notwithstanding public expressed concerns about such products as an exposure route for arsenic (e.g. BBC News report, 2017“Should I worry about arsenic in my rice?”) there are few, if any published data on public perceptions of risks associated with exposure to arsenic in rice. We therefore aimed to determine the risk perception of arsenic exposure from rice intake and factors that are associated with arsenic knowledge and whether or not this knowledge had an influence on rice consumption and cooking practices. A questionnaire, targeting participation of rice-eating ethnic minorities in Greater Manchester, UK, was administered to 184 participants. A multivariate generalized linear model was used to determine the factors associated with rice consumption behaviour, cooking practices, and risk perception. We show for the first time that the general population did not associate arsenic, which they perceive as toxic to health, with rice consumption. More than half of the participants knew about arsenic as a hazardous substance but less than ten percent knew that rice consumption could be an important route of arsenic exposure. Knowledge of arsenic was significantly lower in Asian/Asian British:Pakistanis (Pakistani) (OR: 0.006; 95% CI:0.00-0.03) and Asian/Asian British:Bangladeshis (Bangladeshi) (OR: 0.064; 95% CI:0.01-0.25) compared to White:English/Welsh/Scottish/Northern Irish/British (White British). Moreover, Bangladeshis consumed three times more rice (OR: 2.92; 95% CI:1.73-4.93) compared to White British. Overall higher rice consumption was not associated with higher knowledge of the nutritional value of rice. Rinsing rice before cooking, an effective arsenic removal technique, was practised by 93% of the participants, however the most popular cooking method was the use of adequate water (rice to water ratio of 1:2) but not excess water (rice to water ratio of > 1:4), the latter being more effective in removing arsenic. Better education, higher weekly expenditure on food and prior knowledge of arsenic hazard were all significant factors positively influencing a change in behaviour to reduce arsenic exposure from rice intake

Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model

<p>Abstract</p> <p>Background</p> <p>The binding of peptide fragments of extracellular peptides to class II MHC is a crucial event in the adaptive immune response. Each MHC allotype generally binds a distinct subset of peptides and the enormous number of possible peptide epitopes prevents their complete experimental characterization. Computational methods can utilize the limited experimental data to predict the binding affinities of peptides to class II MHC.</p> <p>Results</p> <p>We have developed the Regularized Thermodynamic Average, or RTA, method for predicting the affinities of peptides binding to class II MHC. RTA accounts for all possible peptide binding conformations using a thermodynamic average and includes a parameter constraint for regularization to improve accuracy on novel data. RTA was shown to achieve higher accuracy, as measured by AUC, than SMM-align on the same data for all 17 MHC allotypes examined. RTA also gave the highest accuracy on all but three allotypes when compared with results from 9 different prediction methods applied to the same data. In addition, the method correctly predicted the peptide binding register of 17 out of 18 peptide-MHC complexes. Finally, we found that suboptimal peptide binding registers, which are often ignored in other prediction methods, made significant contributions of at least 50% of the total binding energy for approximately 20% of the peptides.</p> <p>Conclusions</p> <p>The RTA method accurately predicts peptide binding affinities to class II MHC and accounts for multiple peptide binding registers while reducing overfitting through regularization. The method has potential applications in vaccine design and in understanding autoimmune disorders. A web server implementing the RTA prediction method is available at <url>http://bordnerlab.org/RTA/</url>.</p

MultiRTA: A simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes

abstract: Background The binding of peptide fragments of antigens to class II MHC is a crucial step in initiating a helper T cell immune response. The identification of such peptide epitopes has potential applications in vaccine design and in better understanding autoimmune diseases and allergies. However, comprehensive experimental determination of peptide-MHC binding affinities is infeasible due to MHC diversity and the large number of possible peptide sequences. Computational methods trained on the limited experimental binding data can address this challenge. We present the MultiRTA method, an extension of our previous single-type RTA prediction method, which allows the prediction of peptide binding affinities for multiple MHC allotypes not used to train the model. Thus predictions can be made for many MHC allotypes for which experimental binding data is unavailable. Results We fit MultiRTA models for both HLA-DR and HLA-DP using large experimental binding data sets. The performance in predicting binding affinities for novel MHC allotypes, not in the training set, was tested in two different ways. First, we performed leave-one-allele-out cross-validation, in which predictions are made for one allotype using a model fit to binding data for the remaining MHC allotypes. Comparison of the HLA-DR results with those of two other prediction methods applied to the same data sets showed that MultiRTA achieved performance comparable to NetMHCIIpan and better than the earlier TEPITOPE method. We also directly tested model transferability by making leave-one-allele-out predictions for additional experimentally characterized sets of overlapping peptide epitopes binding to multiple MHC allotypes. In addition, we determined the applicability of prediction methods like MultiRTA to other MHC allotypes by examining the degree of MHC variation accounted for in the training set. An examination of predictions for the promiscuous binding CLIP peptide revealed variations in binding affinity among alleles as well as potentially distinct binding registers for HLA-DR and HLA-DP. Finally, we analyzed the optimal MultiRTA parameters to discover the most important peptide residues for promiscuous and allele-specific binding to HLA-DR and HLA-DP allotypes. Conclusions The MultiRTA method yields competitive performance but with a significantly simpler and physically interpretable model compared with previous prediction methods. A MultiRTA prediction webserver is available at http://bordnerlab.org/MultiRTA.The electronic version of this article is the complete one and can be found online at: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-48

Promiscuous Binding of Invariant Chain-Derived CLIP Peptide to Distinct HLA-I Molecules Revealed in Leukemic Cells

Antigen presentation by HLA class I (HLA-I) and HLA class II (HLA-II) complexes is achieved by proteins that are specific for their respective processing pathway. The invariant chain (Ii)-derived peptide CLIP is required for HLA-II-mediated antigen presentation by stabilizing HLA-II molecules before antigen loading through transient and promiscuous binding to different HLA-II peptide grooves. Here, we demonstrate alternative binding of CLIP to surface HLA-I molecules on leukemic cells. In HLA-II-negative AML cells, we found plasma membrane display of the CLIP peptide. Silencing Ii in AML cells resulted in reduced HLA-I cell surface display, which indicated a direct role of CLIP in the HLA-I antigen presentation pathway. In HLA-I-specific peptide eluates from B-LCLs, five Ii-derived peptides were identified, of which two were from the CLIP region. In vitro peptide binding assays strikingly revealed that the eluted CLIP peptide RMATPLLMQALPM efficiently bound to four distinct HLA-I supertypes (-A2, -B7, -A3, -B40). Furthermore, shorter length variants of this CLIP peptide also bound to these four supertypes, although in silico algorithms only predicted binding to HLA-A2 or -B7. Immunization of HLA-A2 transgenic mice with these peptides did not induce CTL responses. Together these data show a remarkable promiscuity of CLIP for binding to a wide variety of HLA-I molecules. The found participation of CLIP in the HLA-I antigen presentation pathway could reflect an aberrant mechanism in leukemic cells, but might also lead to elucidation of novel processing pathways or immune escape mechanisms

Association between heavy metal and metalloid levels in topsoil and cancer mortality in Spain

Spatio-temporal cancer mortality studies in Spain have revealed patterns for some tumours which display a distribution that is similar across the sexes and persists over time. Such characteristics would be common to tumours that shared risk factors, including the geochemical composition of the soil. The aim of this study was to assess the possible association between heavy metal and metalloid levels in topsoil (upper soil horizon) and cancer mortality in mainland Spain. Ecological cancer mortality study at a municipal level, covering 861,440 cancer deaths (27 different tumour locations) in 7917 Spanish mainland towns, from 1999 to 2008. The elements included in this analysis were Al, As, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn. Topsoil levels (partial extraction) were determined by ICP-MS at 13,317 sampling points. For the analysis, the data on the topsoil composition have been transformed by the centred logratio (clr-transformation). Principal factor analysis was performed to obtain independent latent factors for the transformed variables. To estimate the effect of heavy metal levels in topsoil composition on mortality, we fitted Besag, York and Mollié models, which included each town's factor scores as the explanatory variable. Integrated Nested Laplace Approximation (INLA) was used as a tool for Bayesian inference. All results were adjusted for sociodemographic variables. The results showed an association between trace contents of heavy metals and metalloids in topsoil and mortality due to tumours of the digestive system in mainland Spain. This association was observed in both sexes, something that would support the hypothesis that the incorporation of heavy metals into the trophic chain might be playing a role in the aetiology of some types of cancer. Topsoil composition and the presence of potentially toxic elements in trace concentrations might be an additional component in the aetiology of some types of cancer, and go some way to determine the ensuing geographic differences in mortality in Spain. The results support the interest of inclusion of heavy metal levels in topsoil as a hypothesis in analytical epidemiological studies using biological markers of exposure to heavy metals and metalloids.The study was partially supported by research grants from the Carlos III Institute of Health (PI4CIII/50) and Spanish Health Research Fund (FIS PI11/00871 and FIS CP11/00112). Mortality data were supplied by the Spanish National Statistics Institute in accordance with a specific confidentiality protocol.S