Henry Wirth

Combined Proteomic and Metabolomic Profiling of Serum Reveals Association of the Complement System with Obesity and Identifies Novel Markers of Body Fat Mass Changes

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.

Molecular characterization of long-term survivors of glioblastoma using genome-and transcriptome-wide profiling

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

Identification of harmless and pathogenic algae of the genus Prototheca by MALDI-MS

The only plants infectious for mammals, green algae from the genus Prototheca, are often overseen or mistaken for yeast in clinical diagnosis. To improve this diagnostical gap, a method was developed for fast and reliable identification of Prototheca. A collection of all currently recognized Prototheca species, most represented by several strains, were submitted to a simple extraction by 70% formic acid and ACN; the extracts were analyzed by means of MALDI‐MS. Most of the peaks were found in the range from 4 to 20 kDa and showed a high reproducibility, not in absolute intensities, but in their peak pattern. The selection of measured peaks is mostly due to the technique of ionization in MALDI‐MS, because proteins in the range up to 200 kDa were detected using gel electrophoresis. Some of the proteins were identified by peptide mass fingerprinting and MS2 analysis and turned out to be ribosomal proteins or other highly abundant proteins such as ubiquitin. For the preparation of a heatmap, the intensities of the peaks were plotted and a cluster analysis was performed. From the peak‐lists, a principal component analysis was conducted and a dendrogram was built. This dendrogram, based on MALDI spectra, was in fairly good agreement with a dendrogram based on sequence information from 18S DNA. As a result, pathogenic and nonpathogenic species from the genus Prototheca can be identified, with possible consequences for clinical diagnostics by MALDI‐typing.

Mining SOM expression portraits: feature selection and integrating concepts of molecular function

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.

Chlorinated Benzenes Cause Concomitantly Oxidative Stress and Induction of Apoptotic Markers in Lung Epithelial Cells (A549) at Nonacute Toxic Concentrations

In industrialized countries, people spend more time indoors and are therefore increasingly exposed to volatile organic compounds that are emitted at working places and from consumer products, paintings, and furniture, with chlorobenzene (CB) and 1,2-dichlorobenzene (DCB) being representatives of the halogenated arenes. To unravel the molecular effects of low concentrations typical for indoor and occupational exposure, we exposed human lung epithelial cells to CB and DCB and analyzed the effects on the proteome level by 2-D DIGE, where 860 protein spots were detected. A set of 25 and 30 proteins were found to be significantly altered due to exposure to environmentally relevant concentrations of 10(-2) g/m(3) of CB or 10(-3) g/m(3) of DCB (2.2 and 0.17 ppm), respectively. The most enriched pathways were cell death signaling, oxidative stress response, protein quality control, and metabolism. The involvement of oxidative stress was validated by ROS measurement. Among the regulated proteins, 28, for example, voltage-dependent anion-selective channel protein 2, PDCD6IP protein, heat shock protein beta-1, proliferating cell nuclear antigen, nucleophosmin, seryl-tRNA synthetase, prohibitin, and protein arginine N-methyltransferase 1, could be correlated with the molecular pathway of cell death signaling. Caspase 3 activation by cleavage was confirmed for both CB and DCB by immunoblotting. Treatment with CB or DCB also caused differential protein phosphorylation, for example, at the proteins HNRNP C1/C2, serine-threonine receptor associated protein, and transaldolase 1. Compared to previous results, where cells were exposed to styrene, for the chlorinated aromatic substances besides oxidative stress, apoptosis was found as the predominant cellular response mechanism.

MALDI-typing of infectious algae of the genus Prototheca using SOM portraits

BACKGROUND MALDI-typing has become a frequently used approach for the identification of microorganisms and recently also of invertebrates. Similarity-comparisons are usually based on single-spectral data. We apply self-organizing maps (SOM) to portray the MS-spectral data with individual resolution and to improve the typing of Prototheca algae by using meta-spectra representing prototypes of groups of similar-behaving single spectra. RESULTS The MALDI-TOF peaklists of more than 300 algae extracts referring to five Prototheca species were transformed into colored mosaic images serving as molecular portraits of the individual samples. The portraits visualize the algae-specific distribution of high- and low-amplitude peaks in two dimensions. Species-specific pattern of MS intensities were readily discernable in terms of unique single spots of high amplitude MS-peaks which collect characteristic fingerprint spectra. The spot patterns allow the visual identification of groups of samples referring to different species, genotypes or isolates. The use of meta-peaks instead of single-peaks reduces the dimension of the data and leads to an increased discriminating power in downstream analysis. CONCLUSIONS We expect that our SOM portray method improves MS-based classifications and feature selection in upcoming applications of MALDI-typing based species identifications especially of closely related species.

Time-course human urine proteomics in space-flight simulation experiments

BackgroundLong-term space travel simulation experiments enabled to discover different aspects of human metabolism such as the complexity of NaCl salt balance. Detailed proteomics data were collected during the Mars105 isolation experiment enabling a deeper insight into the molecular processes involved.ResultsWe studied the abundance of about two thousand proteins extracted from urine samples of six volunteers collected weekly during a 105-day isolation experiment under controlled dietary conditions including progressive reduction of salt consumption. Machine learning using Self Organizing maps (SOM) in combination with different analysis tools was applied to describe the time trajectories of protein abundance in urine. The method enables a personalized and intuitive view on the physiological state of the volunteers. The abundance of more than one half of the proteins measured clearly changes in the course of the experiment. The trajectory splits roughly into three time ranges, an early (week 1-6), an intermediate (week 7-11) and a late one (week 12-15). Regulatory modes associated with distinct biological processes were identified using previous knowledge by applying enrichment and pathway flow analysis. Early protein activation modes can be related to immune response and inflammatory processes, activation at intermediate times to developmental and proliferative processes and late activations to stress and responses to chemicals.ConclusionsThe protein abundance profiles support previous results about alternative mechanisms of salt storage in an osmotically inactive form. We hypothesize that reduced NaCl consumption of about 6 g/day presumably will reduce or even prevent the activation of inflammatory processes observed in the early time range of isolation. SOM machine learning in combination with analysis methods of class discovery and functional annotation enable the straightforward analysis of complex proteomics data sets generated by means of mass spectrometry.

A Global Genome Segmentation Method for Exploration of Epigenetic Patterns

Current genome-wide ChIP-seq experiments on different epigenetic marks aim at unraveling the interplay between their regulation mechanisms. Published evaluation tools, however, allow testing for predefined hypotheses only. Here, we present a novel method for annotation-independent exploration of epigenetic data and their inter-correlation with other genome-wide features. Our method is based on a combinatorial genome segmentation solely using information on combinations of epigenetic marks. It does not require prior knowledge about the data (e.g. gene positions), but allows integrating the data in a straightforward manner. Thereby, it combines compression, clustering and visualization of the data in a single tool. Our method provides intuitive maps of epigenetic patterns across multiple levels of organization, e.g. of the co-occurrence of different epigenetic marks in different cell types. Thus, it facilitates the formulation of new hypotheses on the principles of epigenetic regulation. We apply our method to histone modification data on trimethylation of histone H3 at lysine 4, 9 and 27 in multi-potent and lineage-primed mouse cells, analyzing their combinatorial modification pattern as well as differentiation-related changes of single modifications. We demonstrate that our method is capable of reproducing recent findings of gene centered approaches, e.g. correlations between CpG-density and the analyzed histone modifications. Moreover, combining the clustered epigenetic data with information on the expression status of associated genes we classify differences in epigenetic status of e.g. house-keeping genes versus differentiation-related genes. Visualizing the distribution of modification states on the chromosomes, we discover strong patterns for chromosome X. For example, exclusively H3K9me3 marked segments are enriched, while poised and active states are rare. Hence, our method also provides new insights into chromosome-specific epigenetic patterns, opening up new questions how “epigenetic computation” is distributed over the genome in space and time.

Reprogramming of Human Huntington Fibroblasts Using mRNA

The derivation of induced pluripotent stem cells (iPS) from human cell sources using transduction based on viral vectors has been reported by several laboratories. Viral vector-induced integration is a potential cause of genetic modification. We have derived iPS cells from human foreskin, adult Huntington fibroblasts, and adult skin fibroblasts of healthy donors using a nonviral and nonintegrating procedure based on mRNA transfer. In vitro transcribed mRNA for 5 factors, oct-4, nanog, klf-4, c-myc, sox-2 as well as for one new factor, hTERT, was used to induce pluripotency. Reprogramming was analyzed by qPCR analysis of pluripotency gene expression, differentiation, gene expression array, and teratoma assays. iPS cells were shown to express pluripotency markers and were able to differentiate towards ecto-, endo-, and mesodermal lineages. This method may represent a safer technology for reprogramming and derivation of iPS cells. Cells produced by this method can more easily be transferred into the clinical setting.

Portraying the expression landscapes of cancer subtypes

Self-organizing maps (SOM) portray molecular phenotypes with individual resolution. We present an analysis pipeline based on SOM machine learning which allows the comprehensive study of large scale clinical data. The potency of the method is demonstrated in selected applications studying the diversity of gene expression in Glioblastoma Multiforme (GBM) and prostate cancer progression. Our method characterizes relationships between the samples, disentangles the expression patterns into well separated groups of co-regulated genes, extracts their functional contexts using enrichment techniques, and enables the detection of contaminations and outliers in the samples. We found that the four GBM subtypes can be divided into two “localized” and two “intermediate” ones. The localized subtypes are characterized by the antagonistic activation of processes related to immune response and cell division, commonly observed also in other cancers. In contrast, each of the “intermediate” subtypes forms a heterogeneous continuum of expression states linking the “localized” subtypes. Both “intermediate” subtypes are characterized by distinct expression patterns related to translational activity and innate immunity as well as nervous tissue and cell function. We show that SOM portraits provide a comprehensive framework for the description of the diversity of expression landscapes using concepts of molecular function.