Unique immunologic patterns in fibromyalgia.

BACKGROUND: Fibromyalgia (FM) is a clinical syndrome characterized by chronic pain and allodynia. The diagnosis of FM has been one of exclusion as a test to confirm the diagnosis is lacking. Recent data highlight the role of the immune system in FM. Aberrant expressions of immune mediators, such as cytokines, have been linked to the pathogenesis and traits of FM. We therefore determined whether cytokine production by immune cells is altered in FM patients by comparing the cellular responses to mitogenic activators of stimulated blood mononuclear cells of a large number of patients with FM to those of healthy matched individuals. METHODS: Plasma and peripheral blood mononuclear cells (PBMC) were collected from 110 patients with the clinical diagnosis of FM and 91 healthy donors. Parallel samples of PBMC were cultured overnight in medium alone or in the presence of mitogenic activators; PHA or PMA in combination with ionomycin. The cytokine concentrations of IFN-γ, IL-5, IL-6, IL-8, IL-10, MIP-1ß , MCP-1, and MIP1-α in plasma as well as in cultured supernatants were determined using a multiplex immunoassay using bead array technology. RESULTS: Cytokine levels of stimulated PBMC cultures of healthy control subjects were significantly increased as compared to matched non-stimulated PBMC cultures. In contrast, the concentrations of most cytokines were lower in stimulated samples from patients with FM compared to controls. The decreases of cytokine concentrations in patients samples ranged from 1.5-fold for MIP-1ß to 10.2-fold for IL-6 in PHA challenges. In PMA challenges, we observed 1.8 to 4-fold decreases in the concentrations of cytokines in patient samples. CONCLUSION: The cytokine responses to mitogenic activators of PBMC isolated from patients with FM were significantly lower than those of healthy individuals, implying that cell-mediated immunity is impaired in FM patients. This novel cytokine assay reveals unique and valuable immunologic traits, which, when combined with clinical patterns, can offer a diagnostic methodology in FM.

Flynet: a genomic resource for Drosophila melanogaster transcriptional regulatory networks.

MOTIVATION: The highly coordinated expression of thousands of genes in an organism is regulated by the concerted action of transcription factors, chromatin proteins and epigenetic mechanisms. High-throughput experimental data for genome wide in vivo protein-DNA interactions and epigenetic marks are becoming available from large projects, such as the model organism ENCyclopedia Of DNA Elements (modENCODE) and from individual labs. Dissemination and visualization of these datasets in an explorable form is an important challenge. RESULTS: To support research on Drosophila melanogaster transcription regulation and make the genome wide in vivo protein-DNA interactions data available to the scientific community as a whole, we have developed a system called Flynet. Currently, Flynet contains 101 datasets for 38 transcription factors and chromatin regulator proteins in different experimental conditions. These factors exhibit different types of binding profiles ranging from sharp localized peaks to broad binding regions. The protein-DNA interaction data in Flynet was obtained from the analysis of chromatin immunoprecipitation experiments on one color and two color genomic tiling arrays as well as chromatin immunoprecipitation followed by massively parallel sequencing. A web-based interface, integrated with an AJAX based genome browser, has been built for queries and presenting analysis results. Flynet also makes available the cis-regulatory modules reported in literature, known and de novo identified sequence motifs across the genome, and other resources to study gene regulation. AVAILABILITY: Flynet is available at https://www.cistrack.org/flynet/.

Building a meta-predictor for MHC class II-binding peptides.

Prediction of class II major histocompatibility complex (MHC)-peptide binding is a challenging task due to variable length of binding peptides. Different computational methods have been developed; however, each has its own strength and weakness. In order to provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. In this chapter, the procedure of building such a meta-predictor based on Naïve Bayesian approach is introduced. The system is designed in such a way that results obtained from any number of individual predictors can be easily incorporated. This meta-predictor is expected to give users more confidence in the prediction.

Prediction of MHC class II binders using the ant colony search strategy.

OBJECTIVE: Predictions of the binding ability of antigen peptides to major histocompatibility complex (MHC) class II molecules are important in vaccine development. The variable length of each binding peptide complicates this prediction. METHODOLOGY: Motivated by the search properties of the ant colony system (ACS), a method for the identification of an alignment for a given set of short protein peptides has been developed. This alignment is further used for the derivation of a position specific scoring matrix. The distinguishing feature of this method is the use of the collective optimized search strategy of ants for the selection of the alignment. RESULTS: The performance of the new model has been evaluated with several benchmark datasets. It achieves better or comparable results as compared to the performance of existing methods. CONCLUSION: The experiments demonstrate that the predictive performance of the scoring matrix embodies several promising characteristics.

Galaxy High Throughput Genotyping Pipeline for GeneTitan.

Latest genotyping solutions allow for rapid testing of more than two million markers in one experiment. Fully automated instruments such as Affymetrix GeneTitan enable processing of large numbers of samples in a truly high-throughput manner. In concert with solutions like Axiom, fully customizable array plates can now utilize automated workflows that can leverage multi-channel instrumentation like the GeneTitan. With the growing size of raw data output, the serial computational architecture of the software, typically distributed by the vendors on turnkey desktop solutions for quality control and genotype calling, becomes legacy rather than an advantage. Advanced software techniques provide power, flexibility, and can be deployed in an HPC environment, but become technically inconvenient for biologists to use. Here we present a pipeline that uses Galaxy as a mechanism to lower the barrier for complex analysis, and increase efficiency by leveraging high-throughput computing.

Oligo- and polymetastatic progression in lung metastasis(es) patients is associated with specific microRNAs.

RATIONALE: Strategies to stage and treat cancer rely on a presumption of either localized or widespread metastatic disease. An intermediate state of metastasis termed oligometastasis(es) characterized by limited progression has been proposed. Oligometastases are amenable to treatment by surgical resection or radiotherapy. METHODS: We analyzed microRNA expression patterns from lung metastasis samples of patients with ≤ 5 initial metastases resected with curative intent. RESULTS: Patients were stratified into subgroups based on their rate of metastatic progression. We prioritized microRNAs between patients with the highest and lowest rates of recurrence. We designated these as high rate of progression (HRP) and low rate of progression (LRP); the latter group included patients with no recurrences. The prioritized microRNAs distinguished HRP from LRP and were associated with rate of metastatic progression and survival in an independent validation dataset. CONCLUSION: Oligo- and poly- metastasis are distinct entities at the clinical and molecular level.

A probabilistic meta-predictor for the MHC class II binding peptides.

Several computational methods for the prediction of major histocompatibility complex (MHC) class II binding peptides embodying different strengths and weaknesses have been developed. To provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. The construction of a meta-predictor of this type based on a probabilistic approach is introduced in this paper. The design permits the easy incorporation of results obtained from any number of individual predictors. It is demonstrated that this integrated method outperforms six state-of-the-art individual predictors based on computational studies using MHC class II peptides from 13 HLA alleles and three mouse MHC alleles obtained from the Immune Epitope Database and Analysis Resource. It is concluded that this integrative approach provides a clearly enhanced reliability of prediction. Moreover, this computational framework can be directly extended to MHC class I binding predictions.

A meta-predictor for MHC class II binding peptides based on Naïve Bayesian approach.

Prediction of class II MHC-peptide binding is a challenging task due to variable length of binding peptides. Different computational methods have been developed; however, each has its own strength and weakness. In order to provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. In this paper, we introduce a procedure of building such a meta-predictor based on Naïve Bayesian approach. The system is designed in such a way that results obtained from any number of individual predictors can be easily incorporated. This meta-predictor is expected to give users more confidence in the prediction.