The human gastric pathogen Helicobacter pylori spontaneously switches lipopolysaccharide (LPS) Lewis (Le) antigens on and off (phase-variable expression), but the biological significance of this is unclear. Here, we report that Le+ H. pylori variants are able to bind to the C-type lectin DC-SIGN and present on gastric dendritic cells (DCs), and demonstrate that this interaction blocks T helper cell (Th)1 development. In contrast, Le− variants escape binding to DCs and induce a strong Th1 cell response. In addition, in gastric biopsies challenged ex vivo with Le+ variants that bind DC-SIGN, interleukin 6 production is decreased, indicative of increased immune suppression. Our data indicate a role for LPS phase variation and Le antigen expression by H. pylori in suppressing immune responses through DC-SIGN.

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Obesity is associated with multiple adverse health effects and a high risk of developing metabolic and cardiovascular diseases. Therefore, there is a great need to identify circulating parameters that link changes in body fat mass with obesity. This study combines proteomic and metabolomic approaches to identify circulating molecules that discriminate healthy lean from healthy obese individuals in an exploratory study design. To correct for variations in physical activity, study participants performed a one hour exercise bout to exhaustion. Subsequently, circulating factors differing between lean and obese individuals, independent of physical activity, were identified. The DIGE approach yielded 126 differentially abundant spots representing 39 unique proteins. Differential abundance of proteins was confirmed by ELISA for antithrombin-III, clusterin, complement C3 and complement C3b, pigment epithelium-derived factor (PEDF), retinol binding protein 4 (RBP4), serum amyloid P (SAP), and vitamin-D binding protein (VDBP). Targeted serum metabolomics of 163 metabolites identified 12 metabolites significantly related to obesity. Among those, glycine (GLY), glutamine (GLN), and glycero-phosphatidylcholine 42:0 (PCaa 42:0) serum concentrations were higher, whereas PCaa 32:0, PCaa 32:1, and PCaa 40:5 were decreased in obese compared to lean individuals. The integrated bioinformatic evaluation of proteome and metabolome data yielded an improved group separation score of 2.65 in contrast to 2.02 and 2.16 for the single-type use of proteomic or metabolomics data, respectively. The identified circulating parameters were further investigated in an extended set of 30 volunteers and in the context of two intervention studies. Those included 14 obese patients who had undergone sleeve gastrectomy and 12 patients on a hypocaloric diet. For determining the long-term adaptation process the samples were taken six months after the treatment. In multivariate regression analyses, SAP, CLU, RBP4, PEDF, GLN, and C18:2 showed the strongest correlation to changes in body fat mass. The combined serum proteomic and metabolomic profiling reveals a link between the complement system and obesity and identifies both novel (C3b, CLU, VDBP, and all metabolites) and confirms previously discovered markers (PEDF, RBP4, C3, ATIII, and SAP) of body fat mass changes.

BackgroundParallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs). SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand.ResultsThe method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories (adipose, endocrine, homeostasis, digestion, exocrine, epithelium, sexual reproduction, muscle, immune system and nervous tissues). SOM mapping reduces the dimension of expression data from ten of thousands of genes to a few thousand metagenes, each representing a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of genes related to specific molecular processes in the respective tissue. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering are better represented and provide better signal-to-noise ratios if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues broadly into three clusters containing nervous, immune system and the remaining tissues.ConclusionsThe SOM technique provides a more intuitive and informative global view of the behavior of a few well-defined modules of correlated and differentially expressed genes than the separate discovery of the expression levels of hundreds or thousands of individual genes. The program is available as R-package 'oposSOM'.