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1.
Biomarkers. 2018 Nov 2:1-8. doi: 10.1080/1354750X.2018.1539769. [Epub ahead of print]

Specific induction of the unique GPR15 expression in heterogeneous blood lymphocytes by tobacco smoking.

Author information

1
a Department of Environmental Immunology , Helmholtz Centre for Environmental Research GmbH - UFZ , Leipzig , Germany.
2
b Young Investigators Group Bioinformatics and Transcriptomics , Helmholtz Centre for Environmental Research-UFZ , Leipzig , Germany.

Abstract

PURPOSE:

In the peripheral blood, it has been shown that smoking is, to date, the only specific condition leading to an increase in GPR15+ T cells. We, therefore, aimed to characterize GPR15-expressing blood T cells in more detail.

MATERIALS AND METHODS:

The whole transcriptome by RNAseq as a proxy for protein expression was analyzed in GPR15+ and GPR15- T cells. A deep immuno-phenotyping was conducted for the identification of T cell subtypes.

RESULTS:

The expression of GPR15 seemed to be unique, not concomitantly accompanied with the expression of another protein. According to different T cell subtypes, there is no single cell type prominently represented in GPR15+ T cells. The individually different proportions of GPR15+ cells among each GPR15-expressing T cell subtypes in blood were strongly associated with chronic smoking. Indeed, the frequency of GPR15+ T cell subtypes can be effectively used as a highly convincing biomarker for tobacco smoking.

CONCLUSIONS:

While the chronic smoking-induced enrichment of GPR15+ T cells in blood might indicate a systemic inflammation, by the widespread presence in different T cell subtypes, GPR15 could feature a general impact on maintaining the systemic homeostasis to putatively prevent harm from smoking.

KEYWORDS:

GPR15; MAIT; Th17; Treg; smoking

2.
Nat Commun. 2018 Sep 19;9(1):3810. doi: 10.1038/s41467-018-06184-3.

Roquin targets mRNAs in a 3'-UTR-specific manner by different modes of regulation.

Author information

1
Institute for Immunology at the Biomedical Center, Ludwig-Maximilians-Universität München, 82152, Planegg-Martinsried, Germany.
2
Computational and Systems Biology, Biozentrum, University of Basel, 4056, Basel, Switzerland.
3
Research Unit Molecular Immune Regulation, Helmholtz Zentrum München, 81377, München, Germany.
4
Institute of Structural Biology, Helmholtz Zentrum München, 85764, Neuherberg, Germany.
5
Center for Integrated Protein Science Munich at Biomolecular NMR Spectroscopy, Department Chemie, Technische Universität München, 85748, Garching, Germany.
6
Young Investigators Group Bioinformatics and Transcriptomics, Department Molecular Systems Biology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany.
7
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center of Bioinformatics, Leipzig University, Härtelstraße 16-18, 04107, Leipzig, Germany.
8
Division of Biophysics, Institute of Experimental Physics, Faculty of Physics, University of Warsaw, 02-089, Warsaw, Poland.
9
Immunology Unit, Department of Experimental and Health Sciences, Pompeu Fabra University, 08003, Barcelona, Spain.
10
Centre of New Technologies, University of Warsaw, 02-097, Warsaw, Poland.
11
Institute of Biochemistry, Hannover Medical School, 30623, Hannover, Germany.
12
Bioinformatics Unit, Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology-IZI, Leipzig, Germany.
13
Computational and Systems Biology, Biozentrum, University of Basel, 4056, Basel, Switzerland. mihaela.zavolan@unibas.ch.
14
Institute for Immunology at the Biomedical Center, Ludwig-Maximilians-Universität München, 82152, Planegg-Martinsried, Germany. vigo.heissmeyer@med.uni-muenchen.de.
15
Research Unit Molecular Immune Regulation, Helmholtz Zentrum München, 81377, München, Germany. vigo.heissmeyer@med.uni-muenchen.de.

Abstract

The RNA-binding proteins Roquin-1 and Roquin-2 redundantly control gene expression and cell-fate decisions. Here, we show that Roquin not only interacts with stem-loop structures, but also with a linear sequence element present in about half of its targets. Comprehensive analysis of a minimal response element of the Nfkbid 3'-UTR shows that six stem-loop structures cooperate to exert robust and profound post-transcriptional regulation. Only binding of multiple Roquin proteins to several stem-loops exerts full repression, which redundantly involved deadenylation and decapping, but also translational inhibition. Globally, most Roquin targets are regulated by mRNA decay, whereas a small subset, including the Nfat5 mRNA, with more binding sites in their 3'-UTRs, are also subject to translational inhibition. These findings provide insights into how the robustness and magnitude of Roquin-mediated regulation is encoded in complex cis-elements.

PMID:
30232334
PMCID:
PMC6145892
DOI:
10.1038/s41467-018-06184-3
[Indexed for MEDLINE]
Free PMC Article
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3.
Nucleic Acids Res. 2018 May 4;46(8):4256-4270. doi: 10.1093/nar/gky106.

A translational silencing function of MCPIP1/Regnase-1 specified by the target site context.

Author information

1
Institute of Cell Biochemistry, Hannover Medical School, 30625 Hannover, Germany.
2
Institute for Immunology, Biomedical Center of the Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Germany.
3
Young Investigators Group Bioinformatics and Transcriptomics, Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research - UFZ, 04318 Leipzig, Germany.
4
Department of Computer Science, University of Leipzig, 04081 Leipzig, Germany.
5
Research Unit Molecular Immune Regulation, Helmholtz Zentrum München, 81377 München, Germany.

Abstract

The expression of proteins during inflammatory and immune reactions is coordinated by post-transcriptional mechanisms. A particularly strong suppression of protein expression is exerted by a conserved translational silencing element (TSE) identified in the 3' UTR of NFKBIZ mRNA, which is among the targets of the RNA-binding proteins Roquin-1/2 and MCPIP1/Regnase-1. We present evidence that in the context of the TSE MCPIP1, so far known for its endonuclease activity toward mRNAs specified by distinct stem-loop (SL) structures, also suppresses translation. Overexpression of MCPIP1 silenced translation in a TSE-dependent manner and reduced ribosome occupancy of the mRNA. Correspondingly, MCPIP1 depletion alleviated silencing and increased polysomal association of the mRNA. Translationally silenced NFKBIZ or reporter mRNAs were mostly capped, polyadenylated and ribosome associated. Furthermore, MCPIP1 silenced also cap-independent, CrPV-IRES-dependent translation. This suggests that MCPIP1 suppresses a post-initiation step. The TSE is predicted to form five SL structures. SL4 and 5 resemble target structures reported for MCPIP1 and together were sufficient for MCPIP1 binding and mRNA destabilization. Translational silencing, however, required SL1-3 in addition. Thus the NFKBIZ TSE functions as an RNA element in which sequences adjacent to the site of interaction with MCPIP1 and dispensable for accelerated mRNA degradation extend the functional repertoire of MCPIP1 to translational silencing.

4.
Nat Commun. 2018 Jan 19;9(1):299. doi: 10.1038/s41467-017-02582-1.

Binding of NUFIP2 to Roquin promotes recognition and regulation of ICOS mRNA.

Author information

1
Institute for Immunology at the Biomedical Center, Ludwig-Maximilians-Universität München, Grosshaderner Strasse 9, 82152, Planegg-Martinsried, Germany.
2
Research Unit Molecular Immune Regulation, Helmholtz Zentrum München, Marchioninistrasse 25, 81377, München, Germany.
3
Group Intracellular Transport and RNA Biology, Institute of Structural Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany.
4
Center for Integrated Protein Science at the Department of Biology, Ludwig-Maximilians-Universität München, Grosshaderner Strasse 2, 82152, Planegg-Martinsried, Germany.
5
Young Investigators Group Bioinformatics and Transcriptomics, Department Molecular Systems Biology, Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318, Leipzig, Germany.
6
Bioinformatics Group, Department of Computer Science; and Interdisciplinary Center of Bioinformatics, Leipzig University, Härtelstraße 16-18, 04107, Leipzig, Germany.
7
Division of Cell Biology, La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA, 92037, USA.
8
The Functional Genomics Center, La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA, 92037, USA.
9
Department of Health Sciences, Universita' del Piemonte Orientale, via Solaroli 17, 28100, Novara, Italy.
10
CBG Department of Clinical Genetics, Erasmus MC, Wytemaweg 80, 3015 CN, Rotterdam, Netherlands.
11
Monoclonal Antibody Core Facility and Research Group, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Marchioninistrasse 25, 81377, München, Germany.
12
Bioinformatic Unit, Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology- IZI, 04103, Leipzig, Germany.
13
Division of Cell Biology, La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA, 92037, USA. soniasharma@lji.org.
14
The Functional Genomics Center, La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA, 92037, USA. soniasharma@lji.org.
15
Group Intracellular Transport and RNA Biology, Institute of Structural Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany. dierk.niessing@uni-ulm.de.
16
Department of Cell Biology at the Biomedical Center, Ludwig-Maximilians-Universität München, Grosshaderner Strasse 9, 82152, Planegg-Martinsried, Germany. dierk.niessing@uni-ulm.de.
17
Institute of Pharmaceutical Biotechnology, Ulm University, James Franck Ring N27, 89081, Ulm, Germany. dierk.niessing@uni-ulm.de.
18
Institute for Immunology at the Biomedical Center, Ludwig-Maximilians-Universität München, Grosshaderner Strasse 9, 82152, Planegg-Martinsried, Germany. vigo.heissmeyer@med.uni-muenchen.de.
19
Research Unit Molecular Immune Regulation, Helmholtz Zentrum München, Marchioninistrasse 25, 81377, München, Germany. vigo.heissmeyer@med.uni-muenchen.de.

Abstract

The ubiquitously expressed RNA-binding proteins Roquin-1 and Roquin-2 are essential for appropriate immune cell function and postnatal survival of mice. Roquin proteins repress target mRNAs by recognizing secondary structures in their 3'-UTRs and by inducing mRNA decay. However, it is unknown if other cellular proteins contribute to target control. To identify cofactors of Roquin, we used RNA interference to screen ~1500 genes involved in RNA-binding or mRNA degradation, and identified NUFIP2 as a cofactor of Roquin-induced mRNA decay. NUFIP2 binds directly and with high affinity to Roquin, which stabilizes NUFIP2 in cells. Post-transcriptional repression of human ICOS by endogenous Roquin proteins requires two neighboring non-canonical stem-loops in the ICOS 3'-UTR. This unconventional cis-element as well as another tandem loop known to confer Roquin-mediated regulation of the Ox40 3'-UTR, are bound cooperatively by Roquin and NUFIP2. NUFIP2 therefore emerges as a cofactor that contributes to mRNA target recognition by Roquin.

PMID:
29352114
PMCID:
PMC5775257
DOI:
10.1038/s41467-017-02582-1
[Indexed for MEDLINE]
Free PMC Article
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Publication type, MeSH terms, Substances, Grant support

5.
Regul Toxicol Pharmacol. 2017 Dec;91 Suppl 1:S36-S45. doi: 10.1016/j.yrtph.2017.11.001. Epub 2017 Nov 4.

A generic Transcriptomics Reporting Framework (TRF) for 'omics data processing and analysis.

Author information

1
Centre for Radiation, Chemical and Environmental Hazards (CRCE), Public Health England (PHE), Harwell Campus, Oxfordshire, UK. Electronic address: tim.gant@phe.gov.uk.
2
Scientific Consultancy - Animal Welfare, Germany.
3
Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, University of Ulster, UK.
4
U.S. Environmental Protection Agency, USA.
5
Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research - UFZ, Germany.
6
Center for Environmental Toxicology, Agency for Prevention, Environment and Energy (Arpae), Emilia-Romagna, Italy.
7
Norwegian Institute for Water Research (NIVA), Norway.
8
BASF SE, Germany.
9
Environmental Health Science and Research Bureau, Health Canada, Canada.
10
National Center for Toxicological Research (NCTR), U.S. Food and Drug Administration (FDA), USA.
11
European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC), Belgium.

Abstract

A generic Transcriptomics Reporting Framework (TRF) is presented that lists parameters that should be reported in 'omics studies used in a regulatory context. The TRF encompasses the processes from transcriptome profiling from data generation to a processed list of differentially expressed genes (DEGs) ready for interpretation. Included within the TRF is a reference baseline analysis (RBA) that encompasses raw data selection; data normalisation; recognition of outliers; and statistical analysis. The TRF itself does not dictate the methodology for data processing, but deals with what should be reported. Its principles are also applicable to sequencing data and other 'omics. In contrast, the RBA specifies a simple data processing and analysis methodology that is designed to provide a comparison point for other approaches and is exemplified here by a case study. By providing transparency on the steps applied during 'omics data processing and analysis, the TRF will increase confidence processing of 'omics data, and regulatory use. Applicability of the TRF is ensured by its simplicity and generality. The TRF can be applied to all types of regulatory 'omics studies, and it can be executed using different commonly available software tools.

KEYWORDS:

Bioinformatics; Differentially expressed genes; Gene expression; Normalisation of ‘omics data; Regulatory toxicology; Reproducibility; Statistical analysis

PMID:
29113939
DOI:
10.1016/j.yrtph.2017.11.001
[Indexed for MEDLINE]
Free full text
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6.
Regul Toxicol Pharmacol. 2017 Dec;91 Suppl 1:S27-S35. doi: 10.1016/j.yrtph.2017.10.007. Epub 2017 Oct 5.

Framework for the quality assurance of 'omics technologies considering GLP requirements.

Author information

1
BASF SE, Germany.
2
Metanomics GmbH, Germany.
3
U.S. Environmental Protection Agency, USA.
4
ExxonMobil Petroleum and Chemical B.V.B.A., Belgium.
5
Imperial College London, United Kingdom.
6
Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research - UFZ, Germany.
7
Center for Environmental Toxicology, Agency for Prevention, Environment and Energy (Arpae), Emilia-Romagna, Italy.
8
European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC), Belgium.
9
Scientific Consultancy - Animal Welfare, Germany.
10
Norwegian Institute for Water Research (NIVA), Norway.
11
Department of Chemical and Product Safety, German Federal Institute of Risk Assessment (BfR), Germany.
12
Environmental Health Science and Research Bureau, Health Canada, Canada.
13
BASF SE, Germany. Electronic address: bennard.ravenzwaay@basf.com.

Abstract

'Omics technologies are gaining importance to support regulatory toxicity studies. Prerequisites for performing 'omics studies considering GLP principles were discussed at the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) Workshop Applying 'omics technologies in Chemical Risk Assessment. A GLP environment comprises a standard operating procedure system, proper pre-planning and documentation, and inspections of independent quality assurance staff. To prevent uncontrolled data changes, the raw data obtained in the respective 'omics data recording systems have to be specifically defined. Further requirements include transparent and reproducible data processing steps, and safe data storage and archiving procedures. The software for data recording and processing should be validated, and data changes should be traceable or disabled. GLP-compliant quality assurance of 'omics technologies appears feasible for many GLP requirements. However, challenges include (i) defining, storing, and archiving the raw data; (ii) transparent descriptions of data processing steps; (iii) software validation; and (iv) ensuring complete reproducibility of final results with respect to raw data. Nevertheless, 'omics studies can be supported by quality measures (e.g., GLP principles) to ensure quality control, reproducibility and traceability of experiments. This enables regulators to use 'omics data in a fit-for-purpose context, which enhances their applicability for risk assessment.

KEYWORDS:

Data storage; Documentation; Good laboratory practice (GLP); Independent quality assurance; Quality assurance inspection; Raw data definition; Reproducibility; Software validation; Standard operating procedure

PMID:
28987912
DOI:
10.1016/j.yrtph.2017.10.007
[Indexed for MEDLINE]
Free full text
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7.
Regul Toxicol Pharmacol. 2017 Dec;91 Suppl 1:S3-S13. doi: 10.1016/j.yrtph.2017.09.002. Epub 2017 Sep 25.

Applying 'omics technologies in chemicals risk assessment: Report of an ECETOC workshop.

Author information

1
BASF SE, Germany.
2
U.S. Environmental Protection Agency, USA.
3
iMED.Ulisboa and Faculty of Pharmacy, Universidade de Lisboa, Portugal.
4
Procter and Gamble, USA.
5
ExxonMobil Petroleum and Chemical, Belgium.
6
Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, United Kingdom.
7
Centre for Radiation, Chemical and Environmental Hazards (CRCE), Harwell Science and Innovation Campus, Public Health England (PHE), United Kingdom.
8
Syngenta Crop Protection LLC, USA.
9
Albert Einstein College of Medicine, Yeshiva University, USA.
10
European Commission, Joint Research Centre, European Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM), Italy.
11
Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research - UFZ, Germany.
12
Hubesch Consult BVBA, Belgium.
13
Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, The Netherlands.
14
Dow Chemical Company, USA.
15
Japan Organization of Occupational Health and Safety, Japan.
16
European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC), Belgium.
17
ScitoVation, USA.
18
MRC Human Genetics Unit, IGMM, University of Edinburgh, Scotland, United Kingdom.
19
Systox Ltd., United Kingdom.
20
Center for Environmental Toxicology, Agency for Prevention, Environment and Energy (Arpae), Emilia-Romagna, Italy.
21
National Institute for Public Health and the Environment (RIVM), The Netherlands; IRAS Institute for Risk Assessment Sciences, Utrecht University, The Netherlands.
22
Scientific Consultancy - Animal Welfare, Germany.
23
BioMath GmbH, Germany.
24
Institut de Génétique Humain (IGH), Centre National de la Recherche Scientifique - National Centre of Scientific Research (CNRS), France.
25
Sumitomo Chemical Co. Ltd., Japan.
26
Norwegian Institute for Water Research (NIVA), Norway.
27
National Center for Toxicological Research (NCTR), U.S. Food and Drug Administration (FDA), USA.
28
Department of Chemical and Product Safety, German Federal Institute of Risk Assessment (BfR), Germany.
29
Phenome Centre Birmingham, School of Biosciences, University of Birmingham, United Kingdom.
30
Environmental Health Science and Research Bureau, Health Canada, Canada.
31
European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC), Belgium. Electronic address: alan.poole@ecetoc.org.

Abstract

Prevailing knowledge gaps in linking specific molecular changes to apical outcomes and methodological uncertainties in the generation, storage, processing, and interpretation of 'omics data limit the application of 'omics technologies in regulatory toxicology. Against this background, the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) convened a workshop Applying 'omics technologies in chemicals risk assessment that is reported herein. Ahead of the workshop, multi-expert teams drafted frameworks on best practices for (i) a Good-Laboratory Practice-like context for collecting, storing and curating 'omics data; (ii) the processing of 'omics data; and (iii) weight-of-evidence approaches for integrating 'omics data. The workshop participants confirmed the relevance of these Frameworks to facilitate the regulatory applicability and use of 'omics data, and the workshop discussions provided input for their further elaboration. Additionally, the key objective (iv) to establish approaches to connect 'omics perturbations to phenotypic alterations was addressed. Generally, it was considered promising to strive to link gene expression changes and pathway perturbations to the phenotype by mapping them to specific adverse outcome pathways. While further work is necessary before gene expression changes can be used to establish safe levels of substance exposure, the ECETOC workshop provided important incentives towards achieving this goal.

KEYWORDS:

Adverse outcome pathway (AOP); Differentially expressed genes; Gene expression; Good laboratory practice (GLP); Metabolomics; Mode-of-action (MoA); Regulatory toxicology; Transcriptomics; Weight-of-evidence (WoE)

PMID:
28958911
DOI:
10.1016/j.yrtph.2017.09.002
[Indexed for MEDLINE]
Free full text
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8.
Regul Toxicol Pharmacol. 2017 Dec;91 Suppl 1:S14-S26. doi: 10.1016/j.yrtph.2017.09.020. Epub 2017 Sep 18.

The challenge of the application of 'omics technologies in chemicals risk assessment: Background and outlook.

Author information

1
Scientific Consultancy - Animal Welfare, Germany.
2
ExxonMobil Petroleum and Chemical, Belgium.
3
European Commission, Joint Research Centre, European Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM), Italy.
4
Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research - UFZ, Germany.
5
Department of Chemical and Product Safety, German Federal Institute of Risk Assessment (BfR), Germany.
6
BASF SE, Germany.
7
Environmental Health Science and Research Bureau, Health Canada, Canada.
8
European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC), Belgium.
9
National Center for Toxicological Research (NCTR), U.S. Food and Drug Administration (FDA), USA.
10
Centre for Radiation, Chemical and Environmental Hazards (CRCE), Harwell Science and Innovation Campus, Public Health England (PHE), UK. Electronic address: tim.gant@phe.gov.uk.

Abstract

This survey by the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) highlights that 'omics technologies are generally not yet applied to meet standard information requirements during regulatory hazard assessment. While they are used within weight-of-evidence approaches to investigate substances' modes-of-action, consistent approaches for the generation, processing and interpretation of 'omics data are not applied. To date, no 'omics technology has been standardised or validated. Best practices for performing 'omics studies for regulatory purposes (e.g., microarrays for transcriptome profiling) remain to be established. Therefore, three frameworks for (i) establishing a Good-Laboratory Practice-like context for collecting, storing and curating 'omics data; (ii) 'omics data processing; and (iii) quantitative WoE approaches to interpret 'omics data have been developed, that are presented in this journal supplement. Application of the frameworks will enable between-study comparison of results, which will facilitate the regulatory applicability of 'omics data. The frameworks do not constitute prescriptive protocols precluding any other data analysis method, but provide a baseline for analysis that can be applied to all data allowing ready cross-comparison. Data analysis that does not follow the frameworks can be justified and the resulting data can be compared with the Framework-based common analysis output.

KEYWORDS:

Hazard assessment; Omics; Plant protection products; Registration, Evaluation, Authorisation, and Restriction (REACH); Regulatory toxicology; Test method standardisation; Test method validation; Transcriptomics; Weight-of-evidence

PMID:
28927750
DOI:
10.1016/j.yrtph.2017.09.020
[Indexed for MEDLINE]
Free full text
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9.
Sci Rep. 2017 Aug 11;7(1):7976. doi: 10.1038/s41598-017-08348-5.

STAT3-induced long noncoding RNAs in multiple myeloma cells display different properties in cancer.

Binder S1,2,3, Hösler N4,5,6, Riedel D4,5, Zipfel I4,5, Buschmann T5,7, Kämpf C5,8,9,6, Reiche K5,7,8, Burger R10, Gramatzki M10, Hackermüller J5,8,11, Stadler PF5,9,6,12,13,14,15,16,17, Horn F4,5,7,6.

Author information

1
Institute of Clinical Immunology, Faculty of Medicine, University of Leipzig, Leipzig, Germany. stefanie.binder@medizin.uni-leipzig.de.
2
Fraunhofer Institute for Cell Therapy and Immunology, Department of Diagnostics, Leipzig, Germany. stefanie.binder@medizin.uni-leipzig.de.
3
LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany. stefanie.binder@medizin.uni-leipzig.de.
4
Institute of Clinical Immunology, Faculty of Medicine, University of Leipzig, Leipzig, Germany.
5
Fraunhofer Institute for Cell Therapy and Immunology, Department of Diagnostics, Leipzig, Germany.
6
LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.
7
The RIBOLUTION Consortium, Leipzig, Germany.
8
Young Investigators Group Bioinformatics and Transcriptomics, Department Proteomics, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany.
9
Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany.
10
Division of Stem Cell Transplantation and Immunotherapy, Department of Internal Medicine 2, Christian-Albrechts-University, Kiel, Germany.
11
Department of Computer Science, University of Leipzig, Leipzig, Germany.
12
Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany.
13
German Centre for Integrative Biodiversity Research - iDiv, Halle-Jena-Leipzig, Germany.
14
Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany.
15
Department of Theoretical Chemistry, University of Vienna, Vienna, Austria.
16
Center for RNA in Technology and Health, University of Copenhagen, Copenhagen, Denmark.
17
Santa Fe Institute, Santa Fe, USA.

Abstract

Interleukin-6 (IL-6)-activated Signal Transducer and Activator of Transcription 3 (STAT3) facilitates survival in the multiple myeloma cell line INA-6 and therefore represents an oncogenic key player. However, the biological mechanisms are still not fully understood. In previous studies we identified microRNA-21 as a STAT3 target gene with strong anti-apoptotic potential, suggesting that noncoding RNAs have an impact on the pathogenesis of human multiple myeloma. Here, we describe five long noncoding RNAs (lncRNAs) induced by IL-6-activated STAT3, which we named STAiRs. While STAiRs 1, 2 and 6 remain unprocessed in the nucleus and show myeloma-specific expression, STAiRs 15 and 18 are spliced and broadly expressed. Especially STAiR2 and STAiR18 are promising candidates. STAiR2 originates from the first intron of a tumor suppressor gene. Our data support a mutually exclusive expression of either STAiR2 or the functional tumor suppressor in INA-6 cells and thus a contribution of STAiR2 to tumorigenesis. Furthermore, STAiR18 was shown to be overexpressed in every tested tumor entity, indicating its global role in tumor pathogenesis. Taken together, our study reveals a number of STAT3-induced lncRNAs suggesting that the interplay between the coding and noncoding worlds represents a fundamental principle of STAT3-driven cancer development in multiple myeloma and beyond.

10.
J Biotechnol. 2017 Aug 10;255:33-36. doi: 10.1016/j.jbiotec.2017.06.1197. Epub 2017 Jun 22.

The complete genome of the tetrachloroethene-respiring Epsilonproteobacterium Sulfurospirillum halorespirans.

Author information

1
Department of Applied and Ecological Microbiology, Institute of Microbiology, Friedrich Schiller University, 07743 Jena, Germany. Electronic address: tobias.goris@uni-jena.de.
2
Department of Applied and Ecological Microbiology, Institute of Microbiology, Friedrich Schiller University, 07743 Jena, Germany.
3
Department of Applied and Ecological Microbiology, Institute of Microbiology, Friedrich Schiller University, 07743 Jena, Germany; Current address Research Group Medical Systems Biology, Institute for Experimental Medicine, Christian-Albrechts-University, 24105 Kiel, Germany.
4
Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, 04318 Leipzig, Germany.

Abstract

Sulfurospirillum halorespirans is a bacterium that couples the reductive dehalogenation of chlorinated ethenes to growth. This process is called organohalide respiration (OHR), which can be of importance for bioremediation. Here, we report the complete genome of S. halorespirans, the second one of an organohalide-respiring Epsilonproteobacterium after that of Sulfurospirillum multivorans. With both genomes at hand, we were able to ascertain that the genomic region encoding OHR proteins in Epsilonproteobacteria differs from that found in organohalide-respiring bacteria (OHRB) affiliated to other phyla and that the production of a unique cobamide, norpseudo-B12, might not be limited to the model organism S. multivorans. The OHR region is virtually identical in both organisms with differences only in the gene sequence of the key enzyme of OHR, the PCE reductive dehalogenase (PceA), and in regulatory regions. This is of interest, since the availability of natural, closely related variants opens an avenue to study the poorly understood OHRB, which withstand systematic genetic manipulation so far.

KEYWORDS:

Anaerobic respiration; Nitrous oxide reduction; Organohalide respiration; Tetrachloroethene; Thiosulfate oxidation; Vitamin B12

PMID:
28648395
DOI:
10.1016/j.jbiotec.2017.06.1197
[Indexed for MEDLINE]
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11.
J Allergy Clin Immunol. 2018 Feb;141(2):741-753. doi: 10.1016/j.jaci.2017.03.017. Epub 2017 Apr 6.

Maternal phthalate exposure promotes allergic airway inflammation over 2 generations through epigenetic modifications.

Author information

1
Department of Environmental Immunology, UFZ-Helmholtz Centre for Environmental Research Leipzig-Halle, Leipzig, Germany; Department of Dermatology, Venerology and Allergology, Leipzig University Medical Center, Leipzig, Germany; Infections in Hematology/Oncology, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knöll Institute, Jena, Germany.
2
Department of Environmental Immunology, UFZ-Helmholtz Centre for Environmental Research Leipzig-Halle, Leipzig, Germany; Institute of Pharmacy and Molecular Biotechnology, and Bioquant Center, University of Heidelberg, Heidelberg, Germany.
3
Department of Environmental Immunology, UFZ-Helmholtz Centre for Environmental Research Leipzig-Halle, Leipzig, Germany.
4
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
5
Department Molecular Systems Biology, UFZ-Helmholtz Centre for Environmental Research Leipzig-Halle, Leipzig, Germany.
6
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Cell Biology, Harvard Medical School, Boston.
7
Department of Dermatology, Venerology and Allergology, Leipzig University Medical Center, Leipzig, Germany.
8
Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), Heidelberg, Germany.
9
Institute of Clinical Immunology, Medical Faculty, University of Leipzig, Leipzig, Germany.
10
Municipal Hospital "St Georg" Children's Hospital, Leipzig, Germany.
11
Young Investigators Group Bioinformatics and Transcriptomics, Department Molecular Systems Biology, UFZ-Helmholtz Centre for Environmental Research Leipzig-Halle, Leipzig, Germany.
12
Department of Ecological Chemistry, UFZ-Helmholtz Centre for Environmental Research Leipzig-Halle, Leipzig, Germany; Institute for Organic Chemistry, Technical University Bergakademie Freiberg, Freiberg, Germany.
13
Department Molecular Systems Biology, UFZ-Helmholtz Centre for Environmental Research Leipzig-Halle, Leipzig, Germany; Institute of Biochemistry, Faculty of Bioscience, Pharmacy and Psychology, University of Leipzig, Leipzig, Germany; Department of Chemistry and Bioscience, University of Aalborg, Aalborg, Denmark.
14
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany; Institute of Pharmacy and Molecular Biotechnology, and Bioquant Center, University of Heidelberg, Heidelberg, Germany; Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), University of Heidelberg, Heidelberg, Germany.
15
Department of Environmental Immunology, UFZ-Helmholtz Centre for Environmental Research Leipzig-Halle, Leipzig, Germany; Department of Dermatology, Venerology and Allergology, Leipzig University Medical Center, Leipzig, Germany. Electronic address: tobias.polte@ufz.de.

Abstract

BACKGROUND:

Prenatal and early postnatal exposures to environmental factors are considered responsible for the increasing prevalence of allergic diseases. Although there is some evidence for allergy-promoting effects in children because of exposure to plasticizers, such as phthalates, findings of previous studies are inconsistent and lack mechanistic information.

OBJECTIVE:

We investigated the effect of maternal phthalate exposure on asthma development in subsequent generations and their underlying mechanisms, including epigenetic alterations.

METHODS:

Phthalate metabolites were measured within the prospective mother-child cohort Lifestyle and Environmental Factors and Their Influence on Newborns Allergy Risk (LINA) and correlated with asthma development in the children. A murine transgenerational asthma model was used to identify involved pathways.

RESULTS:

In LINA maternal urinary concentrations of mono-n-butyl phthalate, a metabolite of butyl benzyl phthalate (BBP), were associated with an increased asthma risk in the children. Using a murine transgenerational asthma model, we demonstrate a direct effect of BBP on asthma severity in the offspring with a persistently increased airway inflammation up to the F2 generation. This disease-promoting effect was mediated by BBP-induced global DNA hypermethylation in CD4+ T cells of the offspring because treatment with a DNA-demethylating agent alleviated exacerbation of allergic airway inflammation. Thirteen transcriptionally downregulated genes linked to promoter or enhancer hypermethylation were identified. Among these, the GATA-3 repressor zinc finger protein 1 (Zfpm1) emerged as a potential mediator of the enhanced susceptibility for TH2-driven allergic asthma.

CONCLUSION:

These data provide strong evidence that maternal BBP exposure increases the risk for allergic airway inflammation in the offspring by modulating the expression of genes involved in TH2 differentiation through epigenetic alterations.

KEYWORDS:

Airway inflammation; T cells; asthma; epigenetics; phthalates

PMID:
28392331
DOI:
10.1016/j.jaci.2017.03.017
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12.
Stand Genomic Sci. 2017 Feb 2;12:20. doi: 10.1186/s40793-017-0235-5. eCollection 2017.

Complete genome sequence of Pseudoalteromonas phage vB_PspS-H40/1 (formerly H40/1) that infects Pseudoalteromonas sp. strain H40 and is used as biological tracer in hydrological transport studies.

Author information

1
Department of Environmental Microbiology, Helmholtz Centre for Environmental Research - UFZ, 04318 Leipzig, Germany.
2
Department of Isotope Biogeochemistry, ProVis - Centre for Chemical Microscopy, Helmholtz Centre for Environmental Research - UFZ, 04318 Leipzig, Germany.
3
Institute of Anatomy, Histology and Embryology, Faculty of Veterinary Medicine, University of Leipzig, 04103 Leipzig, Germany.
4
Department of Environmental Sciences - Aquatic and Stable Isotope Biogeochemistry, University of Basel, 4056 Basel, Switzerland.
5
Young Investigators Group Bioinformatics & Transcriptomics, Helmholtz Centre for Environmental Research - UFZ, 04318 Leipzig, Germany.
6
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.

Abstract

Pseudoalteromonas phage vB_PspS-H40/1 is a lytic phage that infects Pseudoalteromonas sp. strain H40. Both, the phage and its host were isolated in the 1970s from seawater samples collected from the North Sea near the island of Helgoland, Germany. The phage particle has an icosahedral capsid with a diameter of ~43 to 45 nm and a long non-contractile tail of ~68 nm in length, a typical morphology for members of the Siphoviridae family. The linear dsDNA genome of Pseudoalteromonas phage vB_PspS-H40/1 has a sequence length of 45,306 bp and a GC content of 40.6%. The genome has a modular structure and contains a high proportion of sequence information for hypothetical proteins, typically seen in phage genome sequences. This is the first report of the complete genome sequence of this lytic phage, which has been frequently used since the 1990s as biological tracer in hydrogeological transport studies.

KEYWORDS:

AquaDiva; Bacteriophages as tracers; Genome; Marine phage; Pseudoalteromonas phage; Siphoviridae; Virus

13.
Environ Int. 2017 Feb;99:97-106. doi: 10.1016/j.envint.2016.11.029. Epub 2016 Dec 8.

From the exposome to mechanistic understanding of chemical-induced adverse effects.

Author information

1
Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, 04318 Leipzig, Germany. Electronic address: beate.escher@ufz.de.
2
Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, 04318 Leipzig, Germany.
3
Leipzig University, Rudolf Boehm Institute for Pharmacology & Toxicology, Clinical Pharmacology, Haertelstr. 16-18, 04107 Leipzig, Germany.
4
European Commission Joint Research Centre, Directorate F - Health, Consumers and Reference Materials, Via E. Fermi 2749, 21027 Ispra, VA, Italy.
5
University London, Imperial College, Department Epidemiology & Biostatistics, School of Public Health, St Marys Campus, Norfolk Place, London W2 1PG, England, United Kingdom.
6
Toxicological Center, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk-Antwerp, Belgium.
7
German Environment Agency UBA, Dessau-Roßlau, Germany.
8
Vanderbilt University, School of Medicine, A.B. Hancock Jr. Memorial Laboratory for Cancer Research, Department Biochemistry, Nashville, TN 37232, USA.
9
US Army Engineer Research & Development Center, Vicksburg, MS, USA; Mississippi State University, Starkville, MS, USA.
10
Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA; University of Konstanz, Germany.
11
Maastricht University, Department Toxicogenomics, 6200 MD Maastricht, The Netherlands.
12
Vrije Universiteit, Faculty of Earth & Life Sciences, Institute for Environmental Studies, 1081 HV Amsterdam, The Netherlands.
13
Dept of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
14
King's College London, MRC-PHE Centre for Environment & Health, Analytical & Environmental Sciences Division, London SE1 9NH, England, United Kingdom.
15
Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, 04318 Leipzig, Germany; Technical University Bergakademie Freiberg, Institute for Organic Chemistry, 09596 Freiberg, Germany.
16
Institute Pasteur, Systems Biology Laboratory, Paris, France.
17
US EPA, National Exposure Research Laboratory, Research Triangle Park, NC 27711, USA.
18
US EPA, National Center for Computational Toxicology, Research Triangle Park, NC 27711, USA.

Abstract

The exposome encompasses an individual's exposure to exogenous chemicals, as well as endogenous chemicals that are produced or altered in response to external stressors. While the exposome concept has been established for human health, its principles can be extended to include broader ecological issues. The assessment of exposure is tightly interlinked with hazard assessment. Here, we explore if mechanistic understanding of the causal links between exposure and adverse effects on human health and the environment can be improved by integrating the exposome approach with the adverse outcome pathway (AOP) concept that structures and organizes the sequence of biological events from an initial molecular interaction of a chemical with a biological target to an adverse outcome. Complementing exposome research with the AOP concept may facilitate a mechanistic understanding of stress-induced adverse effects, examine the relative contributions from various components of the exposome, determine the primary risk drivers in complex mixtures, and promote an integrative assessment of chemical risks for both human and environmental health.

KEYWORDS:

AOP; Exposome; Risk assessment; Systems biology; Systems chemistry; Systems toxicology

PMID:
27939949
PMCID:
PMC6116522
DOI:
10.1016/j.envint.2016.11.029
[Indexed for MEDLINE]
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14.
Regul Toxicol Pharmacol. 2016 Dec;82:127-139. doi: 10.1016/j.yrtph.2016.09.018. Epub 2016 Sep 20.

Advancing the use of noncoding RNA in regulatory toxicology: Report of an ECETOC workshop.

Author information

1
Rudolf-Boehm-Institute for Pharmacology and Toxicology, Clinical Pharmacology, Leipzig University, 04107 Leipzig, Germany.
2
BASF SE, 67056 Ludwigshafen, Germany.
3
Centre for Radiation, Chemical and Environmental Hazards, Public Health England (PHE), Harwell Campus, Chilton, Oxfordshire, OX11 0RQ, UK.
4
Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK.
5
Technical University Munich, 85354 Freising-Weihenstephan, Germany.
6
Young Investigators Group Bioinformatics and Transcriptomics, Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research - UFZ, 04318 Leipzig, Germany.
7
European Chemicals Council (Cefic) Long-Range Research Initiative, 1160 Brussels, Belgium; Hubesch Consult BVBA, 1600 Sint-Pieters-Leeuw, Belgium.
8
European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC), 1160 Brussels, Belgium.
9
Biochemistry Centre Regensburg, University of Regensburg, 93053 Regensburg, Germany.
10
Monsanto Company, St. Louis, MO 63167, USA.
11
Dow AgroSciences, Indianapolis, IN 46268, USA.
12
Scientific Consultancy - Animal Welfare, 85579 Neubiberg, Germany.
13
BioMath GmbH, 18055 Rostock, Germany.
14
Institut de Génétique Humain (IGH), Centre National de la Recherche Scientifique - National Centre of Scientific Research (CNRS), 34396 Montpellier Cedex 5, France.
15
Department of Pathology, Institute for RNA Medicine, Beth Israel Deaconess Medical Center - BIDMC Cancer Center Harvard Medical School, Boston, MA 02215, USA.
16
Sumitomo Chemical Europe, 1830 Machelen, Belgium.
17
Neuroscience Amsterdam, VU University Medical Centre, 1081HV 1117, Amsterdam, The Netherlands.
18
Monsanto Company, 1150 Brussels, Belgium.
19
The John Hopkins University School of Medicine, Baltimore, MD 21205, USA.
20
European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC), 1160 Brussels, Belgium. Electronic address: alan.poole@ecetoc.org.

Abstract

The European Centre for the Ecotoxicology and Toxicology of Chemicals (ECETOC) organised a workshop to discuss the state-of-the-art research on noncoding RNAs (ncRNAs) as biomarkers in regulatory toxicology and as analytical and therapeutic agents. There was agreement that ncRNA expression profiling data requires careful evaluation to determine the utility of specific ncRNAs as biomarkers. To advance the use of ncRNA in regulatory toxicology, the following research priorities were identified: (1) Conduct comprehensive literature reviews to identify possibly suitable ncRNAs and areas of toxicology where ncRNA expression profiling could address prevailing scientific deficiencies. (2) Develop consensus on how to conduct ncRNA expression profiling in a toxicological context. (3) Conduct experimental projects, including, e.g., rat (90-day) oral toxicity studies, to evaluate the toxicological relevance of the expression profiles of selected ncRNAs. Thereby, physiological ncRNA expression profiles should be established, including the biological variability of healthy individuals. To substantiate the relevance of key ncRNAs for cell homeostasis or pathogenesis, molecular events should be dose-dependently linked with substance-induced apical effects. Applying a holistic approach, knowledge on ncRNAs, 'omics and epigenetics technologies should be integrated into adverse outcome pathways to improve the understanding of the functional roles of ncRNAs within a regulatory context.

KEYWORDS:

Adverse-outcome-pathway; Biomarker; Gene expression; Noncoding RNA (ncRNA); Phenotypic consequence; Regulatory toxicology

PMID:
27663666
DOI:
10.1016/j.yrtph.2016.09.018
[Indexed for MEDLINE]
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15.
Hum Mol Genet. 2015 Aug 15;24(16):4746-63. doi: 10.1093/hmg/ddv194. Epub 2015 May 27.

Dissecting the genetics of the human transcriptome identifies novel trait-related trans-eQTLs and corroborates the regulatory relevance of non-protein coding loci†.

Author information

1
Institute for Medical Informatics, Statistics and Epidemiology, LIFE - Leipzig Research Center for Civilization Diseases, Cognitive Genetics, Department of Cell Therapy.
2
Department for Computer Science, Analysis Strategies Group, Department of Diagnostics, Young Investigators Group Bioinformatics and Transcriptomics, Department Proteomics, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany and.
3
Institute of Laboratory Medicine, Ludwig-Maximilians-University, Munich, Germany.
4
Institute for Medical Informatics, Statistics and Epidemiology, LIFE - Leipzig Research Center for Civilization Diseases.
5
LIFE - Leipzig Research Center for Civilization Diseases, Department of Internal Medicine/Cardiology, Heart Center.
6
Interdisciplinary Center for Clinical Research, Faculty of Medicine and .
7
LIFE - Leipzig Research Center for Civilization Diseases, Institute of Laboratory Medicine, University of Leipzig, Leipzig, Germany.
8
Department for Computer Science, RNomics Group, Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology- IZI, Leipzig, Germany, Young Investigators Group Bioinformatics and Transcriptomics, Department Proteomics, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany and.
9
Institute for Medical Informatics, Statistics and Epidemiology, LIFE - Leipzig Research Center for Civilization Diseases, markus.scholz@imise.uni-leipzig.de.

Abstract

Genetics of gene expression (eQTLs or expression QTLs) has proved an indispensable tool for understanding biological pathways and pathomechanisms of trait-associated SNPs. However, power of most genome-wide eQTL studies is still limited. We performed a large eQTL study in peripheral blood mononuclear cells of 2112 individuals increasing the power to detect trans-effects genome-wide. Going beyond univariate SNP-transcript associations, we analyse relations of eQTLs to biological pathways, polygenetic effects of expression regulation, trans-clusters and enrichment of co-localized functional elements. We found eQTLs for about 85% of analysed genes, and 18% of genes were trans-regulated. Local eSNPs were enriched up to a distance of 5 Mb to the transcript challenging typically implemented ranges of cis-regulations. Pathway enrichment within regulated genes of GWAS-related eSNPs supported functional relevance of identified eQTLs. We demonstrate that nearest genes of GWAS-SNPs might frequently be misleading functional candidates. We identified novel trans-clusters of potential functional relevance for GWAS-SNPs of several phenotypes including obesity-related traits, HDL-cholesterol levels and haematological phenotypes. We used chromatin immunoprecipitation data for demonstrating biological effects. Yet, we show for strongly heritable transcripts that still little trans-chromosomal heritability is explained by all identified trans-eSNPs; however, our data suggest that most cis-heritability of these transcripts seems explained. Dissection of co-localized functional elements indicated a prominent role of SNPs in loci of pseudogenes and non-coding RNAs for the regulation of coding genes. In summary, our study substantially increases the catalogue of human eQTLs and improves our understanding of the complex genetic regulation of gene expression, pathways and disease-related processes.

PMID:
26019233
PMCID:
PMC4512630
DOI:
10.1093/hmg/ddv194
[Indexed for MEDLINE]
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16.
PLoS One. 2014 Sep 29;9(9):e106076. doi: 10.1371/journal.pone.0106076. eCollection 2014.

Long non-coding RNAs differentially expressed between normal versus primary breast tumor tissues disclose converse changes to breast cancer-related protein-coding genes.

Author information

1
Young Investigators Group Bioinformatics and Transcriptomics, Department Proteomics, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany, and Department for Computer Science, University of Leipzig, Leipzig, Germany; RNomics Group, Department Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology - IZI, Leipzig, Germany.
2
RNomics Group, Department Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology - IZI, Leipzig, Germany; LIFE Interdisciplinary Research Cluster, University of Leipzig, Leipzig, Germany.
3
Clinical Molecular Biology (EpiGen), Akershus University Hospital, Lørenskog, Norway; K.G. Jebsen Center for Breast Cancer Research, Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.
4
K.G. Jebsen Center for Breast Cancer Research, Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway.
5
K.G. Jebsen Center for Breast Cancer Research, Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Oncology, Oslo University Hospital Radiumhospitalet, Oslo, Norway.
6
Department of Breast and Endocrine Surgery, Oslo University Hospital, Ullevål, Norway.
7
Clinical Molecular Biology (EpiGen), Akershus University Hospital, Lørenskog, Norway; K.G. Jebsen Center for Breast Cancer Research, Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway.
8
RNomics Group, Department Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology - IZI, Leipzig, Germany; Institute for Clinical Immunology, University of Leipzig, Leipzig, Germany.
9
Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Oslo, Norway; Department of Pediatric Research, Women and Children's Division, Oslo University Hospital Rikshospitalet, Oslo, Norway.

Abstract

Breast cancer, the second leading cause of cancer death in women, is a highly heterogeneous disease, characterized by distinct genomic and transcriptomic profiles. Transcriptome analyses prevalently assessed protein-coding genes; however, the majority of the mammalian genome is expressed in numerous non-coding transcripts. Emerging evidence supports that many of these non-coding RNAs are specifically expressed during development, tumorigenesis, and metastasis. The focus of this study was to investigate the expression features and molecular characteristics of long non-coding RNAs (lncRNAs) in breast cancer. We investigated 26 breast tumor and 5 normal tissue samples utilizing a custom expression microarray enclosing probes for mRNAs as well as novel and previously identified lncRNAs. We identified more than 19,000 unique regions significantly differentially expressed between normal versus breast tumor tissue, half of these regions were non-coding without any evidence for functional open reading frames or sequence similarity to known proteins. The identified non-coding regions were primarily located in introns (53%) or in the intergenic space (33%), frequently orientated in antisense-direction of protein-coding genes (14%), and commonly distributed at promoter-, transcription factor binding-, or enhancer-sites. Analyzing the most diverse mRNA breast cancer subtypes Basal-like versus Luminal A and B resulted in 3,025 significantly differentially expressed unique loci, including 682 (23%) for non-coding transcripts. A notable number of differentially expressed protein-coding genes displayed non-synonymous expression changes compared to their nearest differentially expressed lncRNA, including an antisense lncRNA strongly anticorrelated to the mRNA coding for histone deacetylase 3 (HDAC3), which was investigated in more detail. Previously identified chromatin-associated lncRNAs (CARs) were predominantly downregulated in breast tumor samples, including CARs located in the protein-coding genes for CALD1, FTX, and HNRNPH1. In conclusion, a number of differentially expressed lncRNAs have been identified with relation to cancer-related protein-coding genes.

PMID:
25264628
PMCID:
PMC4180073
DOI:
10.1371/journal.pone.0106076
[Indexed for MEDLINE]
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17.
J Biotechnol. 2014 Nov 10;189:154-6. doi: 10.1016/j.jbiotec.2014.09.012. Epub 2014 Sep 28.

CEM-designer: design of custom expression microarrays in the post-ENCODE Era.

Author information

1
Bioinformatics Group, Department for Computer Science, University of Leipzig, Leipzig, Germany; RNomics Group, Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology - IZI, Leipzig, Germany.
2
Bioinformatics Group, Department for Computer Science, University of Leipzig, Leipzig, Germany.
3
Young Investigators Group, Bioinformatics and Transcriptomics, Department Proteomics, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany; Bioinformatics Group, Department for Computer Science, University of Leipzig, Leipzig, Germany; RNomics Group, Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology - IZI, Leipzig, Germany. Electronic address: joerg.hackermueller@ufz.de.
4
Young Investigators Group, Bioinformatics and Transcriptomics, Department Proteomics, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany; Bioinformatics Group, Department for Computer Science, University of Leipzig, Leipzig, Germany; RNomics Group, Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology - IZI, Leipzig, Germany. Electronic address: kristin.reiche@ufz.de.

Abstract

Microarrays are widely used in gene expression studies, and custom expression microarrays are popular to monitor expression changes of a customer-defined set of genes. However, the complexity of transcriptomes uncovered recently make custom expression microarray design a non-trivial task. Pervasive transcription and alternative processing of transcripts generate a wealth of interweaved transcripts that requires well-considered probe design strategies and is largely neglected in existing approaches. We developed the web server CEM-Designer that facilitates microarray platform independent design of custom expression microarrays for complex transcriptomes. CEM-Designer covers (i) the collection and generation of a set of unique target sequences from different sources and (ii) the selection of a set of sensitive and specific probes that optimally represents the target sequences. Probe design itself is left to third party software to ensure that probes meet provider-specific constraints. CEM-Designer is available at http://designpipeline.bioinf.uni-leipzig.de.

KEYWORDS:

Custom expression microarray; Microarray design; Non-coding RNA; Pervasive transcription; Transcriptomics

PMID:
25262644
DOI:
10.1016/j.jbiotec.2014.09.012
[Indexed for MEDLINE]
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18.
Mediators Inflamm. 2014;2014:182549. doi: 10.1155/2014/182549. Epub 2014 Feb 20.

Generation of IL-8 and IL-9 producing CD4⁺ T cells is affected by Th17 polarizing conditions and AHR ligands.

Author information

1
UFZ-Helmholtz Centre for Environmental Research Leipzig, Department of Environmental Immunology, Permoserstrasse, 04318 Leipzig, Germany.
2
Junior Research Group Immune Pathogenesis of New Allergies, Leipzig Research Centre for Civilization Diseases, University of Leipzig, Johannisallee, 04103 Leipzig, Germany.
3
Institute for Laboratory Medicine and Pathobiochemistry, Philipps University of Marburg, Hans-Meerwein Strasse, 35043 Marburg, Germany.
4
Universitätsklinikum, Klinik für Dermatologie, Venerologie und Allergologie, Philipp-Rosenthal Strasse, 04103 Leipzig, Germany.
5
Young Investigators Group Bioinformatics and Transcriptomics, Department of Proteomics, UFZ-Helmholtz Centre for Environmental Research, Permoserstrasse, 04318 Leipzig, Germany.

Abstract

The T helper cell subsets Th1, Th2, Th17, and Treg play an important role in immune cell homeostasis, in host defense, and in immunological disorders. Recently, much attention has been paid to Th17 cells which seem to play an important role in the early phase of the adoptive immune response and autoimmune disease. When generating Th17 cells under in vitro conditions the amount of IL-17A producing cells hardly exceeds 20% while the nature of the remaining T cells is poorly characterized. As engagement of the aryl hydrocarbon receptor (AHR) has also been postulated to modulate the differentiation of T helper cells into Th17 cells with regard to the IL-17A expression we ask how far do Th17 polarizing conditions in combination with ligand induced AHR activation have an effect on the production of other T helper cell cytokines. We found that a high proportion of T helper cells cultured under Th17 polarizing conditions are IL-8 and IL-9 single producing cells and that AHR activation results in an upregulation of IL-8 and a downregulation of IL-9 production. Thus, we have identified IL-8 and IL-9 producing T helper cells which are subject to regulation by the engagement of the AHR.

PMID:
24692846
PMCID:
PMC3945483
DOI:
10.1155/2014/182549
[Indexed for MEDLINE]
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19.
J Exp Zool B Mol Dev Evol. 2014 May;322(3):177-89. doi: 10.1002/jez.b.22560. Epub 2014 Feb 12.

A first glimpse at the genome of the Baikalian amphipod Eulimnogammarus verrucosus.

Author information

1
Department of Bioanalytical Ecotoxicology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany; Interdisciplinary Center for Bioinformatics, University Leipzig, Leipzig, Germany.

Abstract

Eulimnogammarus verrucosus is an amphipod endemic to the unique ecosystem of Lake Baikal and serves as an emerging model in ecotoxicological studies. We report here on a survey sequencing of its genome as a first step to establish sequence resources for this species. From a single lane of paired-end sequencing data, we estimated the genome size as nearly 10 Gb and we obtained an overview of the repeat content. At least two-thirds of the genome are non-unique DNA, and a third of the genomic DNA is composed of just five families of repetitive elements, including low-complexity sequences. Attempts to use off-the-shelf assembly tools failed on the available low-coverage data both before and after removal of highly repetitive components. Using a seed-based approach we nevertheless assembled short contigs covering 33 pre-microRNAs and the homeodomain-containing exon of nine Hox genes. The absence of clear evidence for paralogs implies that a genome duplication did not contribute to the large genome size. We furthermore report the assembly of the mitochondrial genome using a new, guided "crystallization" procedure. The initial results presented here set the stage for a more complete sequencing and analysis of this large genome.

PMID:
24677529
DOI:
10.1002/jez.b.22560
[Indexed for MEDLINE]
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20.
Genome Biol. 2014 Mar 4;15(3):R48. doi: 10.1186/gb-2014-15-3-r48.

Cell cycle, oncogenic and tumor suppressor pathways regulate numerous long and macro non-protein-coding RNAs.

Abstract

BACKGROUND:

The genome is pervasively transcribed but most transcripts do not code for proteins, constituting non-protein-coding RNAs. Despite increasing numbers of functional reports of individual long non-coding RNAs (lncRNAs), assessing the extent of functionality among the non-coding transcriptional output of mammalian cells remains intricate. In the protein-coding world, transcripts differentially expressed in the context of processes essential for the survival of multicellular organisms have been instrumental in the discovery of functionally relevant proteins and their deregulation is frequently associated with diseases. We therefore systematically identified lncRNAs expressed differentially in response to oncologically relevant processes and cell-cycle, p53 and STAT3 pathways, using tiling arrays.

RESULTS:

We found that up to 80% of the pathway-triggered transcriptional responses are non-coding. Among these we identified very large macroRNAs with pathway-specific expression patterns and demonstrated that these are likely continuous transcripts. MacroRNAs contain elements conserved in mammals and sauropsids, which in part exhibit conserved RNA secondary structure. Comparing evolutionary rates of a macroRNA to adjacent protein-coding genes suggests a local action of the transcript. Finally, in different grades of astrocytoma, a tumor disease unrelated to the initially used cell lines, macroRNAs are differentially expressed.

CONCLUSIONS:

It has been shown previously that the majority of expressed non-ribosomal transcripts are non-coding. We now conclude that differential expression triggered by signaling pathways gives rise to a similar abundance of non-coding content. It is thus unlikely that the prevalence of non-coding transcripts in the cell is a trivial consequence of leaky or random transcription events.

PMID:
24594072
PMCID:
PMC4054595
DOI:
10.1186/gb-2014-15-3-r48
[Indexed for MEDLINE]
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