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.

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.

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.

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.

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.

Evolution of vault RNAs.

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.

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

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.

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.

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.

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.

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

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.

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.

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

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.

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

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.