Publications by authors named "Friedemann Horn"

25 Publications

  • Page 1 of 1

The noncoding RNA LINC00152 conveys contradicting effects in different glioblastoma cells.

Sci Rep 2021 Sep 16;11(1):18499. Epub 2021 Sep 16.

Institute of Clinical Immunology, University of Leipzig, Leipzig, Germany.

Glioblastoma multiforme (GBM) is an extremely aggressive brain tumor, characterized by its high genetic heterogeneity. In search of novel putative therapeutic RNA targets we investigated the role of the oncogenic long noncoding RNA LINC00152 (CYTOR, and STAiR18) in A172 glioblastoma cells. Here, we are the first to describe, that LINC00152 unexpectedly acts in a tumor suppressive manner in this cell line. SiRNA-based knockdown of LINC00152 enhanced malignant tumor behaviors including proliferation, cell cycle entry, migration, and invasion, contradicting previous studies using U87-MG and LN229 glioblastoma cells. Furthermore, LINC00152 knockdown had no influence on survival of A172 glioblastoma cells. In a genome wide transcription analysis of A172 and U87-MG glioblastoma cells, we identified 70 LINC00152 target genes involved in locomotion, cell migration, and motility in A172 cells, whereas in U87-MG cells only 40 target genes were detected. The LINC00152-regulated genes found in A172 differed from those identified in U87-MG glioblastoma cells, none of them being regulated in both cell lines. These findings underline the strong genetic heterogeneity of glioblastoma and point to a potential, yet unknown risk addressing LINC00152 lncRNA as a prospective therapeutic target in GBM.
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http://dx.doi.org/10.1038/s41598-021-97533-8DOI Listing
September 2021

ProstaTrend-A Multivariable Prognostic RNA Expression Score for Aggressive Prostate Cancer.

Eur Urol 2020 09 4;78(3):452-459. Epub 2020 Jul 4.

Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany; Institute of Clinical Immunology, Faculty of Medicine, University of Leipzig, Leipzig, Germany.

Background: Prostate cancer (PCa) is the most prevalent solid cancer among men in Western Countries. The clinical behavior of localized PCa is highly variable. Some cancers are aggressive leading to death, while others can even be monitored safely. Hence, there is a high clinical need for precise biomarkers for identification of aggressive disease in addition to established clinical parameters.

Objective: To develop an RNA expression-based score for the prediction of PCa prognosis that facilitates clinical decision making.

Design, Setting, And Participants: We assessed 233 tissue specimens of PCa patients with long-term follow-up data from fresh-frozen radical prostatectomies (RPs), from formalin-fixed and paraffin-embedded RP specimens and biopsies by transcriptome-wide next-generation sequencing and customized expression microarrays.

Outcome Measurements And Statistical Analysis: We applied Cox proportional hazard models to the cohorts from different platforms and specimen types. Evidence from these models was combined by fixed-effect meta-analysis to identify genes predictive of the time to death of disease (DoD). Genes were combined by a weighted median approach into a prognostic score called ProstaTrend and transferred for the prediction of biochemical recurrence (BCR) after RP in an independent cohort of The Cancer Genome Atlas (TCGA).

Results And Limitations: ProstaTrend comprising ∼1400 genes was significantly associated with DoD in the training cohort of PCa patients treated by RP (leave-one-out cross-validation, Cox regression: p=2e-09) and with BCR in the TCGA validation cohort (Cox regression: p=3e-06). The prognostic impact persisted after multivariable Cox regression analysis adjusting for Gleason grading group (GG) ≥3 and resection status (p=0.001; DoD, training cohort) and for GG≥3, pathological stage ≥T3, and resection state (p=0.037; BCR, validation cohort).

Conclusions: ProstaTrend is a transcriptome-based score that predicts DoD and BCR in cohorts of PCa patients treated with RP.

Patient Summary: ProstaTrend provides molecular patient risk stratification after radical prostatectomy.
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http://dx.doi.org/10.1016/j.eururo.2020.06.001DOI Listing
September 2020

The Role of lncRNAs TAPIR-1 and -2 as Diagnostic Markers and Potential Therapeutic Targets in Prostate Cancer.

Cancers (Basel) 2020 Apr 30;12(5). Epub 2020 Apr 30.

Institute of Clinical Immunology, Medical Faculty, University of Leipzig, Johannisallee 30, D-04103 Leipzig, Germany.

In search of new biomarkers suitable for the diagnosis and treatment of prostate cancer, genome-wide transcriptome sequencing was carried out with tissue specimens from 40 prostate cancer (PCa) and 8 benign prostate hyperplasia patients. We identified two intergenic long non-coding transcripts, located in close genomic proximity, which are highly expressed in PCa. Microarray studies on a larger cohort comprising 155 patients showed a profound diagnostic potential of these transcripts (AUC~0.94), which we designated as tumor associated prostate cancer increased lncRNA ( and . To test their therapeutic potential, knockdown experiments with siRNA were carried out. The knockdown caused an increase in the p53/TP53 tumor suppressor protein level followed by downregulation of a large number of cell cycle- and -damage repair key regulators. Furthermore, in radiation therapy resistant tumor cells, the knockdown leads to a renewed sensitization of these cells to radiation treatment. Accordingly, in a preclinical PCa xenograft model in mice, the systemic application of nanoparticles loaded with siRNA targeting significantly reduced tumor growth. These findings point to a crucial role of and in PCa.
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http://dx.doi.org/10.3390/cancers12051122DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7280983PMC
April 2020

Alzheimer-related genes show accelerated evolution.

Mol Psychiatry 2020 Mar 13. Epub 2020 Mar 13.

Paul Flechsig Institute of Brain Research, Liebigstraße 19, 04103, Leipzig, Germany.

Alzheimer's disease (AD) is a neurodegenerative disorder of unknown cause with complex genetic and environmental traits. While AD is extremely prevalent in human elderly, it hardly occurs in non-primate mammals and even non-human-primates develop only an incomplete form of the disease. This specificity of AD to human clearly implies a phylogenetic aspect. Still, the evolutionary dimension of AD pathomechanism remains difficult to prove and has not been established so far. To analyze the evolutionary age and dynamics of AD-associated-genes, we established the AD-associated genome-wide RNA-profile comprising both protein-coding and non-protein-coding transcripts. We than applied a systematic analysis on the conservation of splice-sites as a measure of gene-structure based on multiple alignments across vertebrates of homologs of AD-associated-genes. Here, we show that nearly all AD-associated-genes are evolutionarily old and did not originate later in evolution than not-AD-associated-genes. However, the gene-structures of loci, that exhibit AD-associated changes in their expression, evolve faster than the genome at large. While protein-coding-loci exhibit an enhanced rate of small changes in gene structure, non-coding loci show even much larger changes. The accelerated evolution of AD-associated-genes indicates a more rapid functional adaptation of these genes. In particular AD-associated non-coding-genes play an important, as yet largely unexplored, role in AD. This phylogenetic trait indicates that recent adaptive evolution of human brain is causally involved in basic principles of neurodegeneration. It highlights the necessity for a paradigmatic change of our disease-concepts and to reconsider the appropriateness of current animal-models to develop disease-modifying strategies that can be translated to human.
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http://dx.doi.org/10.1038/s41380-020-0680-1DOI Listing
March 2020

Master and servant: LINC00152 - a STAT3-induced long noncoding RNA regulates STAT3 in a positive feedback in human multiple myeloma.

BMC Med Genomics 2020 02 10;13(1):22. Epub 2020 Feb 10.

Institute of Clinical Immunology, Faculty of Medicine, University of Leipzig, Leipzig, Germany.

Background: The survival of INA-6 human multiple myeloma cells is strictly dependent upon the Interleukin-6-activated transcription factor STAT3. Although transcriptional analyses have revealed many genes regulated by STAT3, to date no protein-coding STAT3 target gene is known to mediate survival in INA-6 cells. Therefore, the aim here was to identify and analyze non-protein-coding STAT3 target genes. In addition to the oncogenic microRNA-21, we previously described five long noncoding RNAs (lncRNAs) induced by STAT3, named STAiRs. Here, we focus on STAT3-induced RNA 18 (STAiR18), an mRNA-like, long ncRNA that is duplicated in the human lineage. One STAiR18 locus is annotated as the already well described LINC00152/CYTOR, however, the other harbors the MIR4435-2HG gene and is, up to now, barely described.

Methods: CAPTURE-RNA-sequencing was used to analyze STAiR18 transcript architecture. To identify the STAiR18 and STAT3 phenotype, siRNA-based knockdowns were performed and microarrays were applied to identify their target genes. RNA-binding partners of STAiR18 were determined by Chromatin-Isolation-by-RNA-Purification (ChIRP) and subsequent sequencing. STAT3 expression in dependence of STAiR18 was investigated by immunoblots, chromatin- and RNA-immunoprecipitations.

Results: As identified by CAPTURE-RNA sequencing, a complex splice pattern originates from both STAiR18 loci, generating different transcripts. Knockdown of the most abundant STAiR18 isoforms dramatically decreased INA-6 cell vitality, suggesting a functional role in myeloma cells. Additionally, STAiR18 and STAT3 knockdowns yielded overlapping changes of transcription patterns in INA-6 cells, suggesting a close functional interplay between the two factors. Moreover, Chromatin isolation by RNA purification (ChIRP), followed by genome-wide RNA sequencing showed that STAiR18 associates specifically with the STAT3 primary transcript. Furthermore, the knockdown of STAiR18 reduced STAT3 levels on both the RNA and protein levels, suggesting a positive feedback between both molecules. Furthermore, STAiR18 knockdown changes the histone methylation status of the STAT3 locus, which explains the positive feedback and indicates that STAiR18 is an epigenetic modulator.

Conclusion: Hence, STAiR18 is an important regulator of myeloma cell survival and is strongly associated with the oncogenic function of STAT3. The close functional interplay between STAT3 and STAiR18 suggests a novel principle of regulatory interactions between long ncRNAs and signaling pathways.
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http://dx.doi.org/10.1186/s12920-020-0692-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7011539PMC
February 2020

Deep sequencing and automated histochemistry of human tissue slice cultures improve their usability as preclinical model for cancer research.

Sci Rep 2019 12 27;9(1):19961. Epub 2019 Dec 27.

Institute of Anatomy, University of Leipzig, Faculty of Medicine, Leipzig, Germany.

Cancer research requires models closely resembling the tumor in the patient. Human tissue cultures can overcome interspecies limitations of animal models or the loss of tissue architecture in in vitro models. However, analysis of tissue slices is often limited to histology. Here, we demonstrate that slices are also suitable for whole transcriptome sequencing and present a method for automated histochemistry of whole slices. Tumor and peritumoral tissue from a patient with glioblastoma was processed to slice cultures, which were treated with standard therapy including temozolomide and X-irradiation. Then, RNA sequencing and automated histochemistry were performed. RNA sequencing was successfully accomplished with a sequencing depth of 243 to 368 x 10 reads per sample. Comparing tumor and peritumoral tissue, we identified 1888 genes significantly downregulated and 2382 genes upregulated in tumor. Treatment significantly downregulated 2017 genes, whereas 1399 genes were upregulated. Pathway analysis revealed changes in the expression profile of treated glioblastoma tissue pointing towards downregulated proliferation. This was confirmed by automated analysis of whole tissue slices stained for Ki67. In conclusion, we demonstrate that RNA sequencing of tissue slices is possible and that histochemical analysis of whole tissue slices can be automated which increases the usability of this preclinical model.
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http://dx.doi.org/10.1038/s41598-019-56509-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6934722PMC
December 2019

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

Sci Rep 2017 08 11;7(1):7976. Epub 2017 Aug 11.

Institute of Clinical Immunology, Faculty of Medicine, University of Leipzig, Leipzig, Germany.

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.
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http://dx.doi.org/10.1038/s41598-017-08348-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5554185PMC
August 2017

New insights into valve-related intramural and intracellular bacterial diversity in infective endocarditis.

PLoS One 2017 14;12(4):e0175569. Epub 2017 Apr 14.

Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany.

Aims: In infective endocarditis (IE), a severe inflammatory disease of the endocardium with an unchanged incidence and mortality rate over the past decades, only 1% of the cases have been described as polymicrobial infections based on microbiological approaches. The aim of this study was to identify potential biodiversity of bacterial species from infected native and prosthetic valves. Furthermore, we compared the ultrastructural micro-environments to detect the localization and distribution patterns of pathogens in IE.

Material And Methods: Using next-generation sequencing (NGS) of 16S rDNA, which allows analysis of the entire bacterial community within a single sample, we investigated the biodiversity of infectious bacterial species from resected native and prosthetic valves in a clinical cohort of 8 IE patients. Furthermore, we investigated the ultrastructural infected valve micro-environment by focused ion beam scanning electron microscopy (FIB-SEM).

Results: Biodiversity was detected in 7 of 8 resected heart valves. This comprised 13 bacterial genera and 16 species. In addition to 11 pathogens already described as being IE related, 5 bacterial species were identified as having a novel association. In contrast, valve and blood culture-based diagnosis revealed only 4 species from 3 bacterial genera and did not show any relevant antibiotic resistance. The antibiotics chosen on this basis for treatment, however, did not cover the bacterial spectra identified by our amplicon sequencing analysis in 4 of 8 cases. In addition to intramural distribution patterns of infective bacteria, intracellular localization with evidence of bacterial immune escape mechanisms was identified.

Conclusion: The high frequency of polymicrobial infections, pathogen diversity, and intracellular persistence of common IE-causing bacteria may provide clues to help explain the persistent and devastating mortality rate observed for IE. Improved bacterial diagnosis by 16S rDNA NGS that increases the ability to tailor antibiotic therapy may result in improved outcomes.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0175569PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391965PMC
April 2017

PIPINO: A Software Package to Facilitate the Identification of Protein-Protein Interactions from Affinity Purification Mass Spectrometry Data.

Biomed Res Int 2016 7;2016:2891918. Epub 2016 Feb 7.

Department of Applied Computer Sciences & Biosciences, University of Applied Sciences Mittweida, 09648 Mittweida, Germany.

The functionality of most proteins is regulated by protein-protein interactions. Hence, the comprehensive characterization of the interactome is the next milestone on the path to understand the biochemistry of the cell. A powerful method to detect protein-protein interactions is a combination of coimmunoprecipitation or affinity purification with quantitative mass spectrometry. Nevertheless, both methods tend to precipitate a high number of background proteins due to nonspecific interactions. To address this challenge the software Protein-Protein-Interaction-Optimizer (PIPINO) was developed to perform an automated data analysis, to facilitate the selection of bona fide binding partners, and to compare the dynamic of interaction networks. In this study we investigated the STAT1 interaction network and its activation dependent dynamics. Stable isotope labeling by amino acids in cell culture (SILAC) was applied to analyze the STAT1 interactome after streptavidin pull-down of biotagged STAT1 from human embryonic kidney 293T cells with and without activation. Starting from more than 2,000 captured proteins 30 potential STAT1 interaction partners were extracted. Interestingly, more than 50% of these were already reported or predicted to bind STAT1. Furthermore, 16 proteins were found to affect the binding behavior depending on STAT1 phosphorylation such as STAT3 or the importin subunits alpha 1 and alpha 6.
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http://dx.doi.org/10.1155/2016/2891918DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4761381PMC
November 2016

Monitoring Disease Progression and Therapeutic Response in a Disseminated Tumor Model for Non-Hodgkin Lymphoma by Bioluminescence Imaging.

Mol Imaging 2015 ;14:400-13

Xenograft tumor models are widely studied in cancer research. Our aim was to establish and apply a model for aggressive CD20-positive B-cell non-Hodgkin lymphomas, enabling us to monitor tumor growth and shrinkage in a noninvasive manner. By stably transfecting a luciferase expression vector, we created two bioluminescent human non-Hodgkin lymphoma cell lines, Jeko1(luci) and OCI-Ly3(luci), that are CD20 positive, a prerequisite to studying rituximab, a chimeric anti-CD20 antibody. To investigate the therapy response in vivo, we established a disseminated xenograft tumor model injecting these cell lines in NOD/SCID mice. We observed a close correlation of bioluminescence intensity and tumor burden, allowing us to monitor therapy response in the living animal. Cyclophosphamide reduced tumor burden in mice injected with either cell line in a dose-dependent manner. Rituximab alone was effective in OCI-Ly3(luci)-injected mice and acted additively in combination with cyclophosphamide. In contrast, it improved the therapeutic outcome of Jeko1(luci)-injected mice only in combination with cyclophosphamide. We conclude that well-established bioluminescence imaging is a valuable tool in disseminated xenograft tumor models. Our model can be translated to other cell lines and used to examine new therapeutic agents and schedules.
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March 2016

The role of HPV RNA transcription, immune response-related gene expression and disruptive TP53 mutations in diagnostic and prognostic profiling of head and neck cancer.

Int J Cancer 2015 Dec 6;137(12):2846-57. Epub 2015 Jul 6.

LIFE-Leipzig Research Center for Civilization Diseases, University of Leipzig, 04103, Leipzig, Germany.

Stratification of head and neck squamous cell carcinomas (HNSCC) based on HPV16 DNA and RNA status, gene expression patterns, and mutated candidate genes may facilitate patient treatment decision. We characterize head and neck squamous cell carcinomas (HNSCC) with different HPV16 DNA and RNA (E6*I) status from 290 consecutively recruited patients by gene expression profiling and targeted sequencing of 50 genes. We show that tumors with transcriptionally inactive HPV16 (DNA+ RNA-) are similar to HPV-negative (DNA-) tumors regarding gene expression and frequency of TP53 mutations (47%, 8/17 and 43%, 72/167, respectively). We also find that an immune response-related gene expression cluster is associated with lymph node metastasis, independent of HPV16 status and that disruptive TP53 mutations are associated with lymph node metastasis in HPV16 DNA- tumors. We validate each of these associations in another large data set. Four gene expression clusters which we identify differ moderately but significantly in overall survival. Our findings underscore the importance of measuring the HPV16 RNA (E6*I) and TP53-mutation status for patient stratification and identify associations of an immune response-related gene expression cluster and TP53 mutations with lymph node metastasis in HNSCC.
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http://dx.doi.org/10.1002/ijc.29649DOI Listing
December 2015

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

PLoS One 2014 29;9(9):e106076. Epub 2014 Sep 29.

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.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0106076PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4180073PMC
June 2015

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

Genome Biol 2014 Mar 4;15(3):R48. Epub 2014 Mar 4.

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.
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http://dx.doi.org/10.1186/gb-2014-15-3-r48DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4054595PMC
March 2014

Massive transcriptional perturbation in subgroups of diffuse large B-cell lymphomas.

PLoS One 2013 4;8(11):e76287. Epub 2013 Nov 4.

Institute of Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany.

Based on the assumption that molecular mechanisms involved in cancerogenesis are characterized by groups of coordinately expressed genes, we developed and validated a novel method for analyzing transcriptional data called Correlated Gene Set Analysis (CGSA). Using 50 extracted gene sets we identified three different profiles of tumors in a cohort of 364 Diffuse large B-cell (DLBCL) and related mature aggressive B-cell lymphomas other than Burkitt lymphoma. The first profile had high level of expression of genes related to proliferation whereas the second profile exhibited a stromal and immune response phenotype. These two profiles were characterized by a large scale gene activation affecting genes which were recently shown to be epigenetically regulated, and which were enriched in oxidative phosphorylation, energy metabolism and nucleoside biosynthesis. The third and novel profile showed only low global gene activation similar to that found in normal B cells but not cell lines. Our study indicates novel levels of complexity of DLBCL with low or high large scale gene activation related to metabolism and biosynthesis and, within the group of highly activated DLBCLs, differential behavior leading to either a proliferative or a stromal and immune response phenotype.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0076287PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3817189PMC
August 2014

Analysis of the STAT3 interactome using in-situ biotinylation and SILAC.

J Proteomics 2013 Dec 4;94:370-86. Epub 2013 Sep 4.

Institute of Clinical Immunology, Medical Faculty, University of Leipzig, Johannisallee 30, 04103 Leipzig, Germany; Fraunhofer Institute for Cell Therapy and Immunology, Perlickstrasse 1, 04103 Leipzig, Germany.

Unlabelled: Signal transducer and activator of transcription 3 (STAT3) is activated by a variety of cytokines and growth factors. To generate a comprehensive data set of proteins interacting specifically with STAT3, we applied stable isotope labeling with amino acids in cell culture (SILAC). For high-affinity pull-down using streptavidin, we fused STAT3 with a short peptide tag allowing biotinylation in situ (bio-tag), which did not affect STAT3 functions. By this approach, 3642 coprecipitated proteins were detected in human embryonic kidney-293 cells. Filtering using statistical and functional criteria finally extracted 136 proteins as putative interaction partners of STAT3. Both, a physical interaction network analysis and the enrichment of known and predicted interaction partners suggested that our filtering criteria successfully enriched true STAT3 interactors. Our approach identified numerous novel interactors, including ones previously predicted to associate with STAT3. By reciprocal coprecipitation, we were able to verify the physical association between STAT3 and selected interactors, including the novel interaction with TOX4, a member of the TOX high mobility group box family. Applying the same method, we next investigated the activation-dependency of the STAT3 interactome. Again, we identified both known and novel interactions. Thus, our approach allows to study protein-protein interaction effectively and comprehensively.

Biological Significance: The location, activity, function, degradation, and synthesis of proteins are significantly regulated by interactions of proteins with other proteins, biopolymers and small molecules. Thus, the comprehensive characterization of interactions of proteins in a given proteome is the next milestone on the path to understanding the biochemistry of the cell. In order to generate a comprehensive interactome dataset of proteins specifically interacting with a selected bait protein, we fused our bait protein STAT3 with a short peptide tag allowing biotinylation in situ (bio-tag). This bio-tag allows an affinity pull-down using streptavidin but affected neither the activation of STAT3 by tyrosine phosphorylation nor its transactivating potential. We combined SILAC for accurate relative protein quantification, subcellular fractionation to increase the coverage of interacting proteins, high-affinity pull-down and a stringent filtering method to successfully analyze the interactome of STAT3. With our approach we confirmed several already known and identified numerous novel STAT3 interactors. The approach applied provides a rapid and effective method, which is broadly applicable for studying protein-protein interactions and their dependency on post-translational modifications.
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http://dx.doi.org/10.1016/j.jprot.2013.08.021DOI Listing
December 2013

Bioinformatics for RNomics.

Methods Mol Biol 2011 ;719:299-330

Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany.

Rapid improvements in high-throughput experimental technologies make it nowadays possible to study the expression, as well as changes in expression, of whole transcriptomes under different environmental conditions in a detailed view. We describe current approaches to identify genome-wide functional RNA transcripts (experimentally as well as computationally), and focus on computational methods that may be utilized to disclose their function. While genome databases offer a wealth of information about known and putative functions for protein-coding genes, functional information for novel non-coding RNA genes is almost nonexistent. This is mainly explained by the lack of established software tools to efficiently reveal the function and evolutionary origin of non-coding RNA genes. Here, we describe in detail computational approaches one may follow to annotate and classify an RNA transcript.
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http://dx.doi.org/10.1007/978-1-61779-027-0_14DOI Listing
June 2011

Evolution of vault RNAs.

Mol Biol Evol 2009 Sep 2;26(9):1975-91. Epub 2009 Jun 2.

Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany.

Vault RNAs (vtRNAs) are small, about 100 nt long, polymerase III transcripts contained in the vault particles of eukaryotic cells. Presumably due to their enigmatic function, they have received little attention compared with most other noncoding RNA (ncRNA) families. Their poor sequence conservation makes homology search a complex and tedious task even within vertebrates. Here we report on a systematic and comprehensive analysis of this rapidly evolving class of ncRNAs in deuterostomes, providing a comprehensive collection of computationally predicted vtRNA genes. We find that all previously described vtRNAs are located at a conserved genomic locus linked to the protocadherin gene cluster, an association that is conserved throughout gnathostomes. Lineage-specific expansions to small vtRNA gene clusters are frequently observed in this region. A second vtRNA locus is syntenically conserved across eutherian mammals. The vtRNAs at the two eutherian loci exhibit substantial differences in their promoter structures, explaining their differential expression patterns in several human cancer cell lines. In teleosts, expression of several paralogous vtRNA genes, most but not all located at the syntenically conserved protocadherin locus, was verified by reverse transcriptase-polymerase chain reaction.
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http://dx.doi.org/10.1093/molbev/msp112DOI Listing
September 2009

High-dimensional data analysis: selection of variables, data compression and graphics--application to gene expression.

Biom J 2009 Apr;51(2):235-51

Interdisciplinary Centre for Bioinformatics (IZBI), University of Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany.

The paper presents effective and mathematically exact procedures for selection of variables which are applicable in cases with a very high dimension as, for example, in gene expression analysis. Choosing sets of variables is an important method to increase the power of the statistical conclusions and to facilitate the biological interpretation. For the construction of sets, each single variable is considered as the centre of potential sets of variables. Testing for significance is carried out by means of the Westfall-Young principle based on resampling or by the parametric method of spherical tests. The particular requirements for statistical stability are taken into account; each kind of overfitting is avoided. Thus, high power is attained and the familywise type I error can be kept in spite of the large dimension. To obtain graphical representations by heat maps and curves, a specific data compression technique is applied. Gene expression data from B-cell lymphoma patients serve for the demonstration of the procedures.
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http://dx.doi.org/10.1002/bimj.200800207DOI Listing
April 2009

Co-activator SRC-1 is dispensable for transcriptional control by STAT3.

Biochem J 2009 Apr 28;420(1):123-32. Epub 2009 Apr 28.

Institute of Clinical Immunology and Transfusion Medicine, University of Leipzig, Johannisallee 30, 04103 Leipzig, Germany.

SRC (steroid receptor co-activator)-1 has been reported to interact with and to be an essential co-activator for several members of the STAT (signal transducer and activator of transcription) family, including STAT3, the major signal transducer of IL (interleukin)-6. We addressed the question of whether SRC-1 is crucial for IL-6- and STAT3-mediated physiological responses such as myeloma cell survival and acute-phase protein induction. In fact, silencing of SRC-1 by RNA interference rapidly induced apoptosis in IL-6-dependent INA-6 human myeloma cells, comparable with what was observed upon silencing of STAT3. Using chromatin immunoprecipitation at STAT3 target regions of various genes, however, we observed constitutive binding of SRC-1 that decreased when INA-6 cells were treated with IL-6. The same held true for STAT3 target genes analysed in HepG2 human hepatocellular carcinoma cells. SRC-1-knockdown studies demonstrated that STAT3-controlled promoters require neither SRC-1 nor the other p160 family members SRC-2 or SRC-3 in HepG2 cells. Furthermore, microarray expression profiling demonstrated that the responsiveness of IL-6 target genes is not affected by SRC-1 silencing. In contrast, co-activators of the CBP [CREB (cAMP-response element-binding protein)-binding protein]/p300 family proved functionally important for the transactivation potential of STAT3 and bound inducibly to STAT3 target regions. This recruitment did not depend on the presence of SRC-1. Altogether, this suggests that functional impairment of STAT3 is not involved in the induction of myeloma cell apoptosis by SRC-1 silencing. We therefore conclude that STAT3 transactivates its target genes by the recruitment of CBP/p300 co-activators and that this process generally does not require the contribution of SRC-1.
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http://dx.doi.org/10.1042/BJ20081989DOI Listing
April 2009

Interleukin-6 dependent survival of multiple myeloma cells involves the Stat3-mediated induction of microRNA-21 through a highly conserved enhancer.

Blood 2007 Aug 11;110(4):1330-3. Epub 2007 May 11.

Institute of Clinical Immunology and Transfusion Medicine, Medical Faculty, University of Leipzig, Leipzig, Germany.

Signal transducer and activator of transcription 3 (Stat3) is implicated in the pathogenesis of many malignancies and essential for IL-6-dependent survival and growth of multiple myeloma cells. Here, we demonstrate that the gene encoding oncogenic microRNA-21 (miR-21) is controlled by an upstream enhancer containing 2 Stat3 binding sites strictly conserved since the first observed evolutionary appearance of miR-21 and Stat3. MiR-21 induction by IL-6 was strictly Stat3 dependent. Ectopically raising miR-21 expression in myeloma cells in the absence of IL-6 significantly reduced their apoptosis levels. These data provide strong evidence that miR-21 induction contributes to the oncogenic potential of Stat3.
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http://dx.doi.org/10.1182/blood-2007-03-081133DOI Listing
August 2007

Clusterin is protective in pancreatitis through anti-apoptotic and anti-inflammatory properties.

Biochem Biophys Res Commun 2007 May 6;356(2):431-7. Epub 2007 Mar 6.

Medizinische Klinik und Poliklinik 2, Universitätsklinikum Leipzig AöR, Ph.-Rosenthal-Str. 27, 04103 Leipzig, Germany.

Clusterin is overexpressed in pancreas during the acute phase of pancreatitis. We intended to clarify the role of clusterin expression in stressed exocrine pancreas. We performed in vitro experiments in transfected AR4-2J cells with modified expression levels of clusterin and in vivo studies in clusterin-deficient mice. AR4-2J cells were exposed to agents mimicking cell-stress during pancreatitis (cerulein, hydrogen peroxide, staurosporine or lysophosphatidylcholine). Clusterin-overexpressing AR4-2J cells showed higher viability after cell stress and accordingly reduced rates of apoptosis and lessened caspase-3 activation. Blockage of endogenous clusterin expression reduced viability and enhanced apoptosis. Presence of clusterin reduced NF-kappaB activation and expression of the NF-kappaB target genes TNF-alpha and MOB-1 under cell stress. Clusterin-deficient mice showed a more severe course of acute experimental pancreatitis with enhanced rates of apoptosis and inflammatory cell infiltration. We concluded that clusterin was protective during inflammation of exocrine pancreas because of its anti-apoptotic and anti-inflammatory functions.
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http://dx.doi.org/10.1016/j.bbrc.2007.02.148DOI Listing
May 2007

Signal transducer and activator of transcription STAT3 plays a major role in gp130-mediated acute phase protein gene activation.

Acta Biochim Pol 2003 ;50(3):595-601

Department of Biochemistry, Universitätsklinikum, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany.

Interleukin-6 is a potent inducer of acute-phase response gene transcription. The intracellular signal transduction mechanisms by which this and other biological effects of the cytokine are achieved include activation of the JAK-STAT signaling pathway. More specifically, activation of the signal transducers and activators of transcription STAT1, 3, and 5 in response to IL-6 has been described. We examined the relative potency of these three STAT factors for the activation of acute-phase gene promoters in HepG2 cells in a reporter gene-based assay, where specific STAT factors could be activated via recombinant receptor constructs bearing different STAT-recruiting modules. These experiments indicate that amongst the STAT factors known to be activated by IL-6 STAT3 is the most potent activator of acute-phase gene transcription.
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http://dx.doi.org/035003595DOI Listing
August 2004

Analysis of Stat3 (signal transducer and activator of transcription 3) dimerization by fluorescence resonance energy transfer in living cells.

Biochem J 2004 Jan;377(Pt 2):289-97

Institute of Clinical Immunology and Transfusion Medicine, University Hospital, Leipzig Johannisallee 30, D-04103 Leipzig, Germany.

Signal transducer and activator of transcription 3 (Stat3) dimerization is commonly thought to be triggered by its tyrosine phosphorylation in response to interleukin-6 (IL-6) or other cytokines. Accumulating evidence from in vitro studies, however, suggests that cytoplasmic Stat3 may be associated with high-molecular-mass protein complexes and/or dimerize prior to its activation. To directly study Stat3 dimerization and subcellular localization upon cytokine stimulation, we used live-cell fluorescence spectroscopy and imaging microscopy combined with fluorescence resonance energy transfer (FRET). Stat3 fusion proteins with spectral variants of green fluorescent protein (GFP), cyan fluorescent protein (CFP) and yellow fluorescent protein (YFP) were constructed and expressed in human hepatoma cells (HepG2) and human embryonic kidney cells (HEK-293). Like wild-type Stat3, the fusion proteins redistributed from a preferentially cytoplasmic to nuclear localization upon IL-6 stimulation and supported IL-6-dependent target gene expression. FRET studies in cells co-expressing Stat3-CFP and Stat3-YFP demonstrated that Stat3 dimers exist in the absence of tyrosine phosphorylation. IL-6 induced a 2-fold increase of this basal FRET signal, indicating that tyrosine phosphorylation either increases the dimer/monomer ratio of Stat3 or induces a conformational change of the dimer yielding a higher FRET efficiency. Studies using a mutated Stat3 with a non-functional src-homology 2 (SH2) domain showed that the SH2 domain is essential for dimer formation of phosphorylated as well as non-phosphorylated Stat3. Furthermore, our data show that visualization of normalized FRET signals allow insights into the spatiotemporal dynamics of Stat3 signal transduction.
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http://dx.doi.org/10.1042/BJ20030708DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1223859PMC
January 2004

Interleukin-6-dependent gene expression profiles in multiple myeloma INA-6 cells reveal a Bcl-2 family-independent survival pathway closely associated with Stat3 activation.

Blood 2004 Jan 11;103(1):242-51. Epub 2003 Sep 11.

Institute of Clinical Immunology and Transfusion Medicine, University Hospital Leipzig, Johannisallee 30, 04103 Leipzig, Germany.

Interleukin 6 (IL-6) is a growth and survival factor for multiple myeloma cells. As we report here, the IL-6-dependent human myeloma cell line INA-6 responds with a remarkably rapid and complete apoptosis to cytokine withdrawal. Among the antiapoptotic members of the B-cell lymphoma-2 (Bcl-2) family of apoptosis regulators, only myeloid cell factor-1 (Mcl-1) was slightly induced by IL-6. Overexpression studies demonstrated, however, that IL-6 does not exert its survival effect primarily through this pathway. The IL-6 signal transduction pathways required for survival and the target genes controlled by them were analyzed by using mutated receptor chimeras. The activation of signal transducer and activator of transcription 3 (Stat3) turned out to be obligatory for the survival of INA-6 cells. The same held true for survival and growth of XG-1 myeloma cells. Gene expression profiling of INA-6 cells by using oligonucleotide microarrays revealed many novel IL-6 target genes, among them several genes coding for transcriptional regulators involved in B-lymphocyte differentiation as well as for growth factors and receptors potentially implicated in autocrine or paracrine growth control. Regulation of most IL-6 target genes required the activation of Stat3, underscoring its central role for IL-6 signal transduction. Taken together, our data provide evidence for the existence of an as yet unknown Stat3-dependent survival pathway in myeloma cells.
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http://dx.doi.org/10.1182/blood-2003-04-1048DOI Listing
January 2004
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