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Items: 16

1.
BMC Genomics. 2014;15 Suppl 12:S2. doi: 10.1186/1471-2164-15-S12-S2. Epub 2014 Dec 19.

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

Abstract

BACKGROUND:

Long-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.

RESULTS:

We 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.

CONCLUSIONS:

The 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.

PMID:
25563515
PMCID:
PMC4303941
DOI:
10.1186/1471-2164-15-S12-S2
[Indexed for MEDLINE]
Free PMC Article
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2.
J Integr Bioinform. 2014 Oct 15;11(1):246. doi: 10.2390/biecoll-jib-2014-246.

Profiling of genetic switches using boolean implications in expression data.

Author information

1
Interdisciplinary Centre for Bioinformatics, University of Leipzig, Härtelstr. 16 - 18, 04107 Leipzig, Germany.

Abstract

Correlation analysis assuming coexpression of the genes is a widely used method for gene expression analysis in molecular biology. Yet growing extent, quality and dimensionality of the molecular biological data permits emerging, more sophisticated approaches like Boolean implications. We present an approach which is a combination of the SOM (self organizing maps) machine learning method and Boolean implication analysis to identify relations between genes, metagenes and similarly behaving metagene groups (spots). Our method provides a way to assign Boolean states to genes/metagenes/spots and offers a functional view over significantly variant elements of gene expression data on these three different levels. While being able to cover relations between weakly correlated entities Boolean implication method also decomposes these relations into six implication classes. Our method allows one to validate or identify potential relationships between genes and functional modules of interest and to assess their switching behaviour. Furthermore the output of the method renders it possible to construct and study the network of genes. By providing logical implications as updating rules for the network it can also serve to aid modelling approaches.

PMID:
25318120
DOI:
10.2390/biecoll-jib-2014-246
[Indexed for MEDLINE]
3.
Cell Stem Cell. 2014 Sep 4;15(3):376-391. doi: 10.1016/j.stem.2014.06.005. Epub 2014 Jul 17.

A systems biology approach for defining the molecular framework of the hematopoietic stem cell niche.

Author information

1
INSERM U972, University Paris 11, Hôpital Paul Brousse, 94807 Villejuif, France. Electronic address: pierre.charbord@inserm.fr.
2
Department of Cell and Developmental Biology, University of California, San Diego, La Jolla, CA 92093-0380, USA.
3
Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany.
4
Genomic Platform, Institut Cochin, INSERM U567, 75014 Paris, France.
5
Sorbonne Universités, UPMC Paris 06, IBPS, UMR 7622, Laboratoire de Biologie du Développement, 75005 Paris; CNRS, INSERM U1156, IBPS, UMR 7622, Laboratoire de Biologie du Développement, 75005 Paris, France.
6
UMR967 INSERM, LSHL/IRCM, CEA, University Paris 7, 92260 Fontenay-aux-Roses, France.
7
Department of Cell Biology, Erasmus Stem Cell Institute, Erasmus Medical Center, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands.
8
Sorbonne Universités, UPMC Paris 06, IBPS, UMR 7622, Laboratoire de Biologie du Développement, 75005 Paris; CNRS, INSERM U1156, IBPS, UMR 7622, Laboratoire de Biologie du Développement, 75005 Paris, France. Electronic address: charles.durand@upmc.fr.

Abstract

Despite progress in identifying the cellular composition of hematopoietic stem/progenitor cell (HSPC) niches, little is known about the molecular requirements of HSPC support. To address this issue, we used a panel of six recognized HSPC-supportive stromal lines and less-supportive counterparts originating from embryonic and adult hematopoietic sites. Through comprehensive transcriptomic meta-analyses, we identified 481 mRNAs and 17 microRNAs organized in a modular network implicated in paracrine signaling. Further inclusion of 18 additional cell strains demonstrated that this mRNA subset was predictive of HSPC support. Our gene set contains most known HSPC regulators as well as a number of unexpected ones, such as Pax9 and Ccdc80, as validated by functional studies in zebrafish embryos. In sum, our approach has identified the core molecular network required for HSPC support. These cues, along with a searchable web resource, will inform ongoing efforts to instruct HSPC ex vivo amplification and formation from pluripotent precursors.

PMID:
25042701
DOI:
10.1016/j.stem.2014.06.005
[Indexed for MEDLINE]
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4.
Int J Cancer. 2014 Oct 15;135(8):1822-31. doi: 10.1002/ijc.28836. Epub 2014 Mar 28.

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

Author information

1
Department of Neuropathology, Heinrich Heine University, Düsseldorf, and German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.

Abstract

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 O(6) -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.

KEYWORDS:

IDH1; MGMT; array-based comparative genomic hybridization; gene expression profiles; glioblastoma; integrative bioinformatics; long-term survival

PMID:
24615357
DOI:
10.1002/ijc.28836
[Indexed for MEDLINE]
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5.
Methods Mol Biol. 2014;1107:279-302. doi: 10.1007/978-1-62703-748-8_17.

MicroRNA expression landscapes in stem cells, tissues, and cancer.

Author information

1
Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany.

Abstract

MicroRNAs play critical roles in the regulation of gene expression with two major functions: marking mRNA for degradation in a sequence-specific manner or repressing translation. Publicly available data sets on miRNA and mRNA expression in embryonal and induced stem cells, human tissues, and solid tumors are analyzed in this case study using self-organizing maps (SOMs) to characterize miRNA expression landscapes in the context of cell fate commitment, tissue-specific differentiation, and its dysfunction in cancer. The SOM portraits of the individual samples clearly reveal groups of miRNA specifically overexpressed without the need of additional pairwise comparisons between the different systems. Sets of miRNA differentially over- and underexpressed in different systems have been detected in this study. The individual portraits of the expression landscapes enable a very intuitive, image-based perception which clearly promotes the discovery of qualitative relationships between the systems studied. We see perspectives for broad applications of this method in standard analysis to many kinds of high-throughput data of single miRNA and especially combined miRNA/mRNA data sets.

PMID:
24272444
DOI:
10.1007/978-1-62703-748-8_17
[Indexed for MEDLINE]
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6.
Methods Mol Biol. 2014;1107:257-78. doi: 10.1007/978-1-62703-748-8_16.

Analysis of microRNA expression using machine learning.

Author information

1
Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany.

Abstract

The systematic analysis of miRNA expression and its potential mRNA targets constitutes a basal objective in miRNA research in addition to miRNA gene detection and miRNA target prediction. In this chapter we address methodical issues of miRNA expression analysis using self-organizing maps (SOM), a neural network machine learning algorithm with strong visualization and second-level analysis capabilities widely used to categorize large-scale, high-dimensional data. We shortly review selected experimental and theoretical aspects of miRNA expression analysis. Then, the protocol of our SOM method is outlined with special emphasis on miRNA/mRNA coexpression. The method allows extracting differentially expressed RNA transcripts, their functional context, and also characterization of global properties of expression states and profiles. In addition to the separate study of miRNA and mRNA expression landscapes, we propose the combined analysis of both entities using a covariance SOM.

PMID:
24272443
DOI:
10.1007/978-1-62703-748-8_16
[Indexed for MEDLINE]
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7.
PLoS One. 2013 Jun 24;8(6):e66636. doi: 10.1371/journal.pone.0066636. Print 2013.

Molecular fingerprint of high fat diet induced urinary bladder metabolic dysfunction in a rat model.

Author information

1
Department of Cardiac Surgery, University of Leipzig, Heart Center Leipzig, Leipzig, Germany.

Abstract

AIMS/HYPOTHESIS:

Diabetic voiding dysfunction has been reported in epidemiological dimension of individuals with diabetes mellitus. Animal models might provide new insights into the molecular mechanisms of this dysfunction to facilitate early diagnosis and to identify new drug targets for therapeutic interventions.

METHODS:

Thirty male Sprague-Dawley rats received either chow or high-fat diet for eleven weeks. Proteomic alterations were comparatively monitored in both groups to discover a molecular fingerprinting of the urinary bladder remodelling/dysfunction. Results were validated by ELISA, Western blotting and immunohistology.

RESULTS:

In the proteome analysis 383 proteins were identified and canonical pathway analysis revealed a significant up-regulation of acute phase reaction, hypoxia, glycolysis, β-oxidation, and proteins related to mitochondrial dysfunction in high-fat diet rats. In contrast, calcium signalling, cytoskeletal proteins, calpain, 14-3-3η and eNOS signalling were down-regulated in this group. Interestingly, we found increased ubiquitin proteasome activity in the high-fat diet group that might explain the significant down-regulation of eNOS, 14-3-3η and calpain.

CONCLUSIONS/INTERPRETATION:

Thus, high-fat diet is sufficient to induce significant remodelling of the urinary bladder and alterations of the molecular fingerprint. Our findings give new insights into obesity related bladder dysfunction and identified proteins that may indicate novel pathophysiological mechanisms and therefore constitute new drug targets.

PMID:
23826106
PMCID:
PMC3691244
DOI:
10.1371/journal.pone.0066636
[Indexed for MEDLINE]
Free PMC Article
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8.
Biology (Basel). 2013 Dec 2;2(4):1411-37. doi: 10.3390/biology2041411.

Portraying the Expression Landscapes of B-CellLymphoma-Intuitive Detection of Outlier Samples and of Molecular Subtypes.

Author information

1
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, Leipzig 04107, Germany. hopp@izbi.uni-leipzig.de.
2
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, Leipzig 04107, Germany. lembcke@izbi.uni-leipzig.de.
3
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, Leipzig 04107, Germany. binder@izbi.uni-leipzig.de.
4
Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16-18, Leipzig 04107, Germany. wirth@izbi.uni-leipzig.de.

Abstract

We present an analytic framework based on Self-Organizing Map (SOM) machine learning to study large scale patient data sets. The potency of the approach is demonstrated in a case study using gene expression data of more than 200 mature aggressive B-cell lymphoma patients. The method portrays each sample with individual resolution, characterizes the subtypes, disentangles the expression patterns into distinct modules, extracts their functional context using enrichment techniques and enables investigation of the similarity relations between the samples. The method also allows to detect and to correct outliers caused by contaminations. Based on our analysis, we propose a refined classification of B-cell Lymphoma into four molecular subtypes which are characterized by differential functional and clinical characteristics.

9.
PLoS One. 2012;7(10):e46811. doi: 10.1371/journal.pone.0046811. Epub 2012 Oct 12.

A global genome segmentation method for exploration of epigenetic patterns.

Author information

1
Junior Professorship for Computational EvoDevo, Institute of Computer Science, University of Leipzig, Leipzig, Germany.

Abstract

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.

PMID:
23077526
PMCID:
PMC3470578
DOI:
10.1371/journal.pone.0046811
[Indexed for MEDLINE]
Free PMC Article
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10.
BioData Min. 2012 Oct 8;5(1):18. doi: 10.1186/1756-0381-5-18.

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

Author information

1
Interdisciplinary Centre for Bioinformatics of Leipzig University, Härtelstr, 16-18, D-4107, Leipzig, Germany. wirth@izbi.uni-leipzig.de.

Abstract

BACKGROUND:

Self 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.

RESULTS:

Different 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.

CONCLUSIONS:

The 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.

11.
J Microbiol Methods. 2012 Jan;88(1):83-97. doi: 10.1016/j.mimet.2011.10.013. Epub 2011 Oct 26.

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

Author information

1
Interdisciplinary Centre for Bioinformatics of Leipzig University, D-4107, Härtelstr. 16-18, Leipzig, Germany. wirth@izbi.uni-leipzig.de

Abstract

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.

PMID:
22062088
DOI:
10.1016/j.mimet.2011.10.013
[Indexed for MEDLINE]
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12.
J Proteome Res. 2011 Oct 7;10(10):4769-88. doi: 10.1021/pr2005555. Epub 2011 Aug 29.

Combined proteomic and metabolomic profiling of serum reveals association of the complement system with obesity and identifies novel markers of body fat mass changes.

Author information

1
IFB Adiposity Diseases, Leipzig University Medical Centre, Leipzig, Germany.

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.

PMID:
21823675
DOI:
10.1021/pr2005555
[Indexed for MEDLINE]
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13.
BMC Bioinformatics. 2011 Jul 27;12:306. doi: 10.1186/1471-2105-12-306.

Expression cartography of human tissues using self organizing maps.

Author information

1
Interdisciplinary Centre for Bioinformatics of Leipzig University, D-4107 Leipzig, Härtelstr. 16-18, Germany. henry.wirth@ufz.de

Abstract

BACKGROUND:

Parallel 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.

RESULTS:

The 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.

CONCLUSIONS:

The 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'.

PMID:
21794127
PMCID:
PMC3161046
DOI:
10.1186/1471-2105-12-306
[Indexed for MEDLINE]
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14.
J Proteome Res. 2011 Feb 4;10(2):363-78. doi: 10.1021/pr1005718. Epub 2010 Dec 21.

Chlorinated benzenes cause concomitantly oxidative stress and induction of apoptotic markers in lung epithelial cells (A549) at nonacute toxic concentrations.

Author information

1
Department of Proteomics, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany.

Abstract

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.

PMID:
21171652
DOI:
10.1021/pr1005718
[Indexed for MEDLINE]
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15.
J Biotechnol. 2010 Sep 1;149(3):98-114. doi: 10.1016/j.jbiotec.2010.02.002. Epub 2010 Feb 13.

Gene expression density profiles characterize modes of genomic regulation: theory and experiment.

Author information

1
Interdisciplinary Centre for Bioinformatics of Leipzig University, D-4107 Leipzig, Haertelstr. 16-18, Germany. binder@izbi.uni-leipzig.de

Abstract

Our study addresses modes of genomic regulation and their characterization using the distribution of expression values. A simple model of transcriptional regulation is introduced to characterize the response of the global expression pattern to the changing properties of basal regulatory building blocks. Random genomes are generated which express and bind transcription factors according to the appearance of short motifs of coding and binding sequences. Regulation of transcriptional activity is described using a thermodynamic model. Our model predicts single-peaked distributions of expression values the flanks of which decay according to power laws. The characteristic exponent is inversely related to the product of the connectivity of the network times the regulatory strength of bound transcription factors. Such 'expression spectra' were calculated and analyzed for different model genomes. Information on structural properties and on the interactions of regulatory elements is used to build up a framework of basic characteristics of expression spectra. We analyze examples addressing different biological issues. Peak position and width of the experimental expression spectra vary with the biological context. We demonstrate that the study of the global expression pattern provides valuable information about transcriptional regulation which complements conventional searches for differentially expressed single genes.

PMID:
20156493
DOI:
10.1016/j.jbiotec.2010.02.002
[Indexed for MEDLINE]
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16.
Proteomics Clin Appl. 2009 Jul;3(7):774-84. doi: 10.1002/prca.200780138.

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

Author information

1
Department of Proteomics, UFZ - Helmholtz-Centre for Environmental Research, Leipzig, Germany. Martin.vonbergen@ufz.de.

Abstract

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 MS(2) 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.

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