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Characterization of Naturally Occurring Minor Histocompatibility Peptides Including H-4 and H-Y MHC Class I-RESTRICTED CTLs are essential for immunity to intracellular pathogens [1]. In addition, CTLs, are also involved in [...]

Limit of T Cell Tolerance to Self Proteins by Peptide Presentation SELF TOLERANCE IN THE IMMUNE system is established by elimination of self-reactive T lymphocytes during thymic differentiation [1]. The [...]

Novel wide array blood biomarkers of multisystem dysregulation, compared to conventional clinical and blood biomarkers, are potentially able to provide more accurate prognostic information of long-term mortality risks. We identified biomarker signatures of all-cause and disease-specific mortality from a comprehensive range of analytes related to six major physiological functions: cytokine, chemokine, and growth factors; glucose metabolism regulators and adipokines; adhesion molecules; acute phase response; pathogen-specific antibodies; and bone remodeling. A total of 144 elderly were prospectively followed up on mortality for median 136 months. Plasma levels of 93 biomarkers measured by enzyme-linked immunosorbent assay, Luminex, FlowCytomix, DNA quantification, and recombinant DNA technology at baseline were compared among deceased and surviving elderly and in a referent group of 72 healthy young adults. The elderly, and especially deceased elderly, exhibited differential profiles of the composite index of each physiological function from young adults. In Cox regression, we identified and validated in an independent cohort of 357 elderly the specific and common biomarkers predicting all-cause, cardiovascular disease-related, neoplasm-related, and respiratory disease-related mortalities after controlling age, sex, and major comorbidities. These biomarkers had 74.3% correct classification for deceased elderly from surviving elderly. We reported for the first time, stem cell growth factor-β and gastric inhibitory polypeptide as specific biomarkers of mortality risk. [ABSTRACT FROM AUTHOR]

Background: Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death and there is a huge unmet clinical need to identify disease biomarkers in peripheral blood. Compared to gene level differential expression approaches to identify gene signatures, network analyses provide a biologically intuitive approach which leverages the co-expression patterns in the transcriptome to identify modules of co-expressed genes.Methods: A weighted gene co-expression network analysis (WGCNA) was applied to peripheral blood transcriptome from 238 COPD subjects to discover co-expressed gene modules. We then determined the relationship between these modules and forced expiratory volume in 1 s (FEV1). In a second, independent cohort of 381 subjects, we determined the preservation of these modules and their relationship with FEV1. For those modules that were significantly related to FEV1, we determined the biological processes as well as the blood cell-specific gene expression that were over-represented using additional external datasets.Results: Using WGCNA, we identified 17 modules of co-expressed genes in the discovery cohort. Three of these modules were significantly correlated with FEV1 (FDR < 0.1). In the replication cohort, these modules were highly preserved and their FEV1 associations were reproducible (P < 0.05). Two of the three modules were negatively related to FEV1 and were enriched in IL8 and IL10 pathways and correlated with neutrophil-specific gene expression. The positively related module, on the other hand, was enriched in DNA transcription and translation and was strongly correlated to CD4+, CD8+ T cell-specific gene expression.Conclusions: Network based approaches are promising tools to identify potential biomarkers for COPD.Trial Registration: The ECLIPSE study was funded by GlaxoSmithKline, under ClinicalTrials.gov identifier NCT00292552 and GSK No. SCO104960. [ABSTRACT FROM AUTHOR]