A reliable transcriptomic risk-score applicable to formalin-fixed paraffin-embedded biopsies improves outcome prediction in localized prostate cancer.

BACKGROUND: Clinical manifestation of prostate cancer (PCa) is highly variable. Aggressive tumors require radical treatment while clinically non-significant ones may be suitable for active surveillance. We previously developed the prognostic ProstaTrend RNA signature based on transcriptome-wide microarray and RNA-sequencing (RNA-Seq) analyses, primarily of prostatectomy specimens. An RNA-Seq study of formalin-fixed paraffin-embedded (FFPE) tumor biopsies has now allowed us to use this test as a basis for the development of a novel test that is applicable to FFPE biopsies as a tool for early routine PCa diagnostics. METHODS: All patients of the FFPE biopsy cohort were treated by radical prostatectomy and median follow-up for biochemical recurrence (BCR) was 9 years. Based on the transcriptome data of 176 FFPE biopsies, we filtered ProstaTrend for genes susceptible to FFPE-associated degradation via regression analysis. ProstaTrend was additionally restricted to genes with concordant prognostic effects in the RNA-Seq TCGA prostate adenocarcinoma (PRAD) cohort to ensure robust and broad applicability. The prognostic relevance of the refined Transcriptomic Risk Score (TRS) was analyzed by Kaplan-Meier curves and Cox-regression models in our FFPE-biopsy cohort and 9 other public datasets from PCa patients with BCR as primary endpoint. In addition, we developed a prostate single-cell atlas of 41 PCa patients from 5 publicly available studies to analyze gene expression of ProstaTrend genes in different cell compartments. RESULTS: Validation of the TRS using the original ProstaTrend signature in the cohort of FFPE biopsies revealed a relevant impact of FFPE-associated degradation on gene expression and consequently no significant association with prognosis (Cox-regression, p-value > 0.05) in FFPE tissue. However, the TRS based on the new version of the ProstaTrend-ffpe signature, which included 204 genes (of originally 1396 genes), was significantly associated with BCR in the FFPE biopsy cohort (Cox-regression p-value < 0.001) and retained prognostic relevance when adjusted for Gleason Grade Groups. We confirmed a significant association with BCR in 9 independent cohorts including 1109 patients. Comparison of the prognostic performance of the TRS with 17 other prognostically relevant PCa panels revealed that ProstaTrend-ffpe was among the best-ranked panels. We generated a PCa cell atlas to associate ProstaTrend genes with cell lineages or cell types. Tumor-specific luminal cells have a significantly higher TRS than normal luminal cells in all analyzed datasets. In addition, TRS of epithelial and luminal cells was correlated with increased Gleason score in 3 studies. CONCLUSIONS: We developed a prognostic gene-expression signature for PCa that can be applied to FFPE biopsies and may be suitable to support clinical decision-making.

Characterization and evaluation of gene fusions as a measure of genetic instability and disease prognosis in prostate cancer.

BACKGROUND: Prostate cancer (PCa) is one of the most prevalent cancers worldwide. The clinical manifestations and molecular characteristics of PCa are highly variable. Aggressive types require radical treatment, whereas indolent ones may be suitable for active surveillance or organ-preserving focal therapies. Patient stratification by clinical or pathological risk categories still lacks sufficient precision. Incorporating molecular biomarkers, such as transcriptome-wide expression signatures, improves patient stratification but so far excludes chromosomal rearrangements. In this study, we investigated gene fusions in PCa, characterized potential novel candidates, and explored their role as prognostic markers for PCa progression. METHODS: We analyzed 630 patients in four cohorts with varying traits regarding sequencing protocols, sample conservation, and PCa risk group. The datasets included transcriptome-wide expression and matched clinical follow-up data to detect and characterize gene fusions in PCa. With the fusion calling software Arriba, we computationally predicted gene fusions. Following detection, we annotated the gene fusions using published databases for gene fusions in cancer. To relate the occurrence of gene fusions to Gleason Grading Groups and disease prognosis, we performed survival analyses using the Kaplan-Meier estimator, log-rank test, and Cox regression. RESULTS: Our analyses identified two potential novel gene fusions, MBTTPS2,L0XNC01::SMS and AMACR::AMACR. These fusions were detected in all four studied cohorts, providing compelling evidence for the validity of these fusions and their relevance in PCa. We also found that the number of gene fusions detected in a patient sample was significantly associated with the time to biochemical recurrence in two of the four cohorts (log-rank test, p-value < 0.05 for both cohorts). This was also confirmed after adjusting the prognostic model for Gleason Grading Groups (Cox regression, p-values < 0.05). CONCLUSIONS: Our gene fusion characterization workflow revealed two potential novel fusions specific for PCa. We found evidence that the number of gene fusions was associated with the prognosis of PCa. However, as the quantitative correlations were only moderately strong, further validation and assessment of clinical value is required before potential application.

Four decades of experience of prosthetic valve endocarditis reflect a high variety of diverse pathogens.

Prosthetic valve endocarditis (PVE) remains a serious condition with a high mortality rate. Precise identification of the PVE-associated pathogen/s and their virulence is essential for successful therapy and patient survival. The commonly described PVE-associated pathogens are staphylococci, streptococci, and enterococci, with Staphylococcus aureus being the most frequently diagnosed species. Furthermore, multi-drug resistance pathogens are increasing in prevalence and continue to pose new challenges mandating a personalized approach. Blood cultures in combination with echocardiography are the most common methods to diagnose PVE, often being the only indication, it exists. In many cases, the diagnostic strategy recommended in the clinical guidelines does not identify the precise microbial agent, and frequently, false-negative blood cultures are reported. Despite the fact that blood culture findings are not always a good indicator of the actual PVE agent in the valve tissue, only a minority of re-operated prostheses are subjected to microbiological diagnostic evaluation. In this review, we focus on the diversity and the complete spectrum of PVE-associated bacterial, fungal, and viral pathogens in blood and prosthetic heart valve, their possible virulence potential, and their challenges in making a microbial diagnosis. We are curious to understand if the unacceptable high mortality of PVE is associated with the high number of negative microbial findings in connection with a possible PVE. Herein, we discuss the possibilities and limits of the diagnostic methods conventionally used and make recommendations for enhanced pathogen identification. We also show possible virulence factors of the most common PVE-associated pathogens and their clinical effects. Based on blood culture, molecular biological diagnostics, and specific valve examination, better derivations for the antibiotic therapy as well as possible preventive intervention can be established in the future.

RNA sequencing of glioblastoma tissue slice cultures reveals the effects of treatment at the transcriptional level.

One of the major challenges in cancer research is finding models that closely resemble tumors within patients. Human tissue slice cultures are a promising approach to provide a model of the patient's tumor biology ex vivo. Recently, it was shown that these slices can be successfully analyzed by whole transcriptome sequencing as well as automated histochemistry, increasing their usability as preclinical model. Glioblastoma multiforme (GBM) is a highly malignant brain tumor with poor prognosis and little is known about its genetic background and heterogeneity regarding therapy success. In this study, tissue from the tumors of 25 patients with primary GBM was processed into slice cultures and treated with standard therapy (irradiation and temozolomide). Total RNA sequencing and automated histochemistry were performed to enable analysis of treatment effects at a transcriptional and histological level. Slice cultures from long-term survivors (overall survival [OS] > 24 months) exhibited more apoptosis than cultures from patients with shorter OS. Proliferation within these slices was slightly increased in contrast to other groups, but not significantly. Among all samples, 58 protein-coding genes were upregulated and 32 downregulated in treated vs. untreated slice cultures. In general, an upregulation of DNA damage-related and cell cycle checkpoint genes as well as enrichment of genotoxicity pathways and p53-dependent signaling was found after treatment. Overall, the current study reproduces knowledge from former studies regarding the feasibility of transcriptomic analyses and automated histology in tissue slice cultures. We further demonstrate that the experimental data merge with the clinical follow-up of the patients, which improves the applicability of our model system.

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

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.

Alzheimer-related genes show accelerated evolution.

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.

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

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.

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

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 (TAPIR-1 and -2). 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 DNA-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 TAPIR-1 significantly reduced tumor growth. These findings point to a crucial role of TAPIR-1 and -2 in PCa.

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

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.

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

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 106 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.

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

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.

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

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.

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

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.

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

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.

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.

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.

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

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.

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

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.

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

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.

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

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.

Bioinformatics for RNomics.

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.