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.

No abstract

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'.

The prognosis of glioblastoma, the most malignant type of glioma, is still poor, with only a minority of patients showing long‐term survival of more than three years after diagnosis. To elucidate the molecular aberrations in glioblastomas of long‐term survivors, we performed genome‐ and/or transcriptome‐wide molecular profiling of glioblastoma samples from 94 patients, including 28 long‐term survivors with >36 months overall survival (OS), 20 short‐term survivors with <12 months OS and 46 patients with intermediate OS. Integrative bioinformatic analyses were used to characterize molecular aberrations in the distinct survival groups considering established molecular markers such as isocitrate dehydrogenase 1 or 2 (IDH1/2) mutations, and O6‐methylguanine DNA methyltransferase (MGMT) promoter methylation. Patients with long‐term survival were younger and more often had IDH1/2‐mutant and MGMT‐methylated tumors. Gene expression profiling revealed over‐representation of a distinct (proneural‐like) expression signature in long‐term survivors that was linked to IDH1/2 mutation. However, IDH1/2‐wildtype glioblastomas from long‐term survivors did not show distinct gene expression profiles and included proneural, classical and mesenchymal glioblastoma subtypes. Genomic imbalances also differed between IDH1/2‐mutant and IDH1/2‐wildtype tumors, but not between survival groups of IDH1/2‐wildtype patients. Thus, our data support an important role for MGMT promoter methylation and IDH1/2 mutation in glioblastoma long‐term survival and corroborate the association of IDH1/2 mutation with distinct genomic and transcriptional profiles. Importantly, however, IDH1/2‐wildtype glioblastomas in our cohort of long‐term survivors lacked distinctive DNA copy number changes and gene expression signatures, indicating that other factors might have been responsible for long survival in this particular subgroup of patients.© 2014 UICC

No abstract

BackgroundSelf organizing maps (SOM) enable the straightforward portraying of high-dimensional data of large sample collections in terms of sample-specific images. The analysis of their texture provides so-called spot-clusters of co-expressed genes which require subsequent significance filtering and functional interpretation. We address feature selection in terms of the gene ranking problem and the interpretation of the obtained spot-related lists using concepts of molecular function.ResultsDifferent expression scores based either on simple fold change-measures or on regularized Student’s t-statistics are applied to spot-related gene lists and compared with special emphasis on the error characteristics of microarray expression data. The spot-clusters are analyzed using different methods of gene set enrichment analysis with the focus on overexpression and/or overrepresentation of predefined sets of genes. Metagene-related overrepresentation of selected gene sets was mapped into the SOM images to assign gene function to different regions. Alternatively we estimated set-related overexpression profiles over all samples studied using a gene set enrichment score. It was also applied to the spot-clusters to generate lists of enriched gene sets. We used the tissue body index data set, a collection of expression data of human tissues as an illustrative example. We found that tissue related spots typically contain enriched populations of gene sets well corresponding to molecular processes in the respective tissues. In addition, we display special sets of housekeeping and of consistently weak and high expressed genes using SOM data filtering.ConclusionsThe presented methods allow the comprehensive downstream analysis of SOM-transformed expression data in terms of cluster-related gene lists and enriched gene sets for functional interpretation. SOM clustering implies the ability to define either new gene sets using selected SOM spots or to verify and/or to amend existing ones.

No abstract