Search

Background and Purpose— Circulating microRNAs (miRNAs) are emerging biomarkers for stroke because of their high stability in the bloodstream and association with pathophysiologic conditions. However, the circulating whole-genome miRNAs (miRNome) has not been characterized comprehensively in the acute phase of stroke. Methods— We profiled the circulating miRNome in mouse models of acute ischemic and hemorrhagic stroke by next-generation sequencing. Stroke models were compared with sham-operated and naive mice to identify deregulated circulating miRNAs. Top-ranked miRNAs were validated and further characterized by quantitative reverse transcription polymerase chain reaction. Results— We discovered 24 circulating miRNAs with an altered abundance in the circulation 3 hours after ischemia, whereas the circulating miRNome was not altered after intracerebral hemorrhage compared with sham-operated mice. Among the upregulated miRNAs in ischemia, the top-listed miR-1264/1298/448 cluster was strongly dependent on reperfusion in different ischemia models. A time course experiment revealed that the miR-1264/1298/448 cluster peaked in the circulation around 3 hours after reperfusion and gradually decreased thereafter. Conclusions— Alteration of the miRNome in the circulation is associated with cerebral ischemia/reperfusion, but not hemorrhage, suggesting a potential to serve as biomarkers for reperfusion in the acute phase. The pathophysiological role of reperfusion-inducible miR-1264/1298/448 cluster, which is located on chromosome X within the introns of the serotonin receptor HTR2C, requires further investigation.

Die Universität Potsdam hat in diesem Jahr groß gefeiert: Sie ist 25 Jahre alt geworden. Zahlreiche Veranstaltungen und Publikationen zum Jubeljahr beleuchten, wie sich die Universität seit ihrer Gründung am 15. Juli 1991 entwickelt hat. Auch die Redaktion von Portal Alumni will dieses Vierteljahrhundert in den Blick nehmen und zwar aus der Perspektive der Studierenden und Alumni. Wir haben uns gefragt: Wie hat sich das Studium an der Universität Potsdam in der Vergangenheit geändert? Was ist den Studierenden heute wichtig, was waren wichtige Themen vor zehn oder zwanzig Jahren? Welche Erinnerungen haben Alumni an ihre Hochschulzeit? In diesem Heft lassen wir Alumni zu Wort kommen, die zu unterschiedlichen Zeiten an der Universität studiert haben und die für uns einen Blick zurück werfen auf ihre Generation. Mit dabei ist etwa Stefan Uhlmann, der sein Studium kurz vor dem Wendeherbst aufgenommen hatte. Sein erstes Semester war noch geprägt von sozialer Sicherheit: Der Staat zahlte den monatlichen studentischen Unterhalt von 200 Mark, die Universität stellte den Wohnheimplatz. Selbst der Arbeitsplatz war nach den Gesetzen der Planwirtschaft sicher. Wenige Monate später war alles anders: Die Inhalte aller Studiengänge wurden auf den Prüfstand gestellt, alle Rahmenbedingungen neu gestaltet, die gesamte Gesellschaft und damit auch der Arbeitsmarkt waren im Umbruch. Zwischen der Generation von Stefan Uhlmann und der der jungen Bachelorabsolventin Friederike Bath liegen 25 Jahre Universitätsgeschichte. Eine Zeit, in der sich Lehre und Studium gewandelt haben. Der Einführung von einer Studienordnung nach bundesdeutschem Recht folgte in der Jahrtausendwende die Bologna-Reform, die nicht nur das Studieren verändert hat. Sie hat auch einen Wertewandel mit sich gebracht, den der Sozialisationswissenschaftler Wilfried Schubarth in dieser Ausgabe beschreibt., Portal alumni : das Ehemaligen-Magazin der Universität Potsdam, 13/2016

Polarimetric synthetic aperture radar (PolSAR) data are used extensively for terrain classification applying SAR features from various target decompositions and certain textural features. However, one source of information has so far been neglected from PolSAR classification: Color. It is a common practice to visualize PolSAR data by color coding methods and thus, it is possible to extract powerful color features from such pseudocolor images so as to provide additional data for a superior terrain classification. In this paper, we first review previous attempts for PolSAR classifications using various feature combinations and then we introduce and perform in-depth investigation of the application of color features over the Pauli color-coded images besides SAR and texture features. We run an extensive set of comparative evaluations using 24 different feature set combinations over three images of the Flevoland- and the San Francisco Bay region from the RADARSAT-2 and the AIRSAR systems operating in C- and L-bands, respectively. We then consider support vector machines and random forests classifier topologies to test and evaluate the role of color features over the classification performance. The classification results show that the additional color features introduce a new level of discrimination and provide noteworthy improvement in classification performance (compared with the traditionally employed PolSAR and texture features) within the application of land use and land cover classification.

Fully and partially polarimetric SAR data in combination with textural features have been used extensively for terrain classification. However, there is another type of visual feature that has so far been neglected from polarimetric SAR classification: Color. It is a common practice to visualize polarimetric SAR data by color coding methods and thus it is possible to extract powerful color features from such pseudo color images so as to gather additional crucial information for an improved terrain classification. In this paper, we investigate the application of several individual visual features over different pseudo color generated images along with the traditional SAR and texture features for a novel supervised classification application of dual- and single-polarized SAR data. We then draw the focus on evaluating the effects of the applied pseudo coloring methods on the classification performance. An extensive set of experiments show that individual visual features or their combination with traditional SAR features introduce a new level of discrimination and provide noteworthy improvement of classification accuracies within the application of land use and land cover classification for dual- and single-pol image data.

The genes encoding microRNAs of the human miR-200 family map to fragile chromosomal regions and are frequently downregulated upon tumor progression. Although having been reported to regulate epithelial-to-mesenchymal transition and transforming growth factor-beta-driven cell invasion, the role of the miR-200 family in EGF-driven breast cancer cell invasion, viability, apoptosis and cell cycle progression is still unknown. In particular, there is no study comparing the roles of the two clusters of this miRNA family. In this study, we show for the first time that miR-200 family members differentially regulate EGF-driven invasion, viability, apoptosis and cell cycle progression of breast cancer cells. We showed that, all miR-200 family members regulate EGF-driven invasion, with the miR-200bc/429 cluster showing stronger effects than the miR-200a/141 cluster. Furthermore, expression of the miR-200a/141 cluster results in G1 arrest supported by increased p27/Kip1 and decreased cyclin dependent kinase 6 expression. In contrast, expression of the 200bc/429 cluster decreases G1 population and increases G2/M phase, in line with the observed reduction of p27/Kip1 and upregulation of the inhibitory phosphorylation of Cdc25C, respectively. To test the hypothesis that phenotypical differences observed between the two clusters are caused by differential targeting spectrums, we performed genome-wide microarray profiling in combination with gain-of-function studies. This identified phospholipase C gamma 1 (PLCG1), which was downregulated only by the miR-200bc/429 cluster, as a potential candidate contributing to these phenotypical differences. Luciferase reporter assays validated PLCG1 as a direct functional target of miR-200bc/429 cluster, but not of miR-200a/141 cluster. Finally, loss of PLCG1 in part mimicked the effect of miR-200bc/429 overexpression in viability, apoptosis and EGF-driven cell invasion of breast cancer cells. Our results suggest that the miR-200 family has a tumor-suppressor function by negatively regulating EGF-driven cell invasion, viability and cell cycle progression in breast cancer.

// Umar Raza 1 , Ozge Saatci 1 , Stefan Uhlmann 2 , Suhail A Ansari 1 , Erol Eyupoglu 1 , Emre Yurdusev 1 , Merve Mutlu 1 , Pelin Gulizar Ersan 1 , Mustafa Kadri Altundag 3 , Jitao David Zhang 4 , Hayriye Tatli Dogan 5 , Gulnur Guler 5 , Ozgur Sahin 1 1 Department of Molecular Biology and Genetics, Faculty of Science, Bilkent University, 06800 Ankara, Turkey 2 Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel, Switzerland 3 Department of Medical Oncology, Hacettepe University Cancer Institute, 06410 Ankara, Turkey 4 Baumlihofstrasse 429, 4125 Riehen, Switzerland 5 Department of Pathology, Hacettepe University, 06410 Ankara, Turkey Correspondence to: Ozgur Sahin, email: sahinozgur@gmail.com Keywords: miRNAs, CTBP1, p53, EMT, therapy resistance Received: January 04, 2016 Accepted: June 26, 2016 Published: July 08, 2016 ABSTRACT Tumor cells develop drug resistance which leads to recurrence and distant metastasis. MicroRNAs are key regulators of tumor pathogenesis; however, little is known whether they can sensitize cells and block metastasis simultaneously. Here, we report miR-644a as a novel inhibitor of both cell survival and EMT whereby acting as pleiotropic therapy-sensitizer in breast cancer. We showed that both miR-644a expression and its gene signature are associated with tumor progression and distant metastasis-free survival. Mechanistically, miR-644a directly targets the transcriptional co-repressor C-Terminal Binding Protein 1 (CTBP1) whose knock-outs by the CRISPR-Cas9 system inhibit tumor growth, metastasis, and drug resistance, mimicking the phenotypes induced by miR-644a. Furthermore, downregulation of CTBP1 by miR-644a upregulates wild type- or mutant-p53 which acts as a ‘molecular switch’ between G1-arrest and apoptosis by inducing cyclin-dependent kinase inhibitor 1 (p21, CDKN1A, CIP1) or pro-apoptotic phorbol-12-myristate-13-acetate-induced protein 1 (Noxa, PMAIP1), respectively. Interestingly, an increase in mutant-p53 by either overexpression of miR-644a or downregulation of CTBP1 was enough to shift this balance in favor of apoptosis through upregulation of Noxa. Notably, p53-mutant patients, but not p53-wild type ones, with high CTBP1 have a shorter survival suggesting that CTBP1 could be a potential prognostic factor for breast cancer patients with p53 mutations. Overall, re-activation of the miR-644a/CTBP1/p53 axis may represent a new strategy for overcoming both therapy resistance and metastasis.

Due to the simplicity and firm mathematical foundation, Support Vector Machines (SVMs) have been intensively used to solve classification problems. However, training SVMs on real world large-scale databases is computationally costly and sometimes infeasible when the dataset size is massive and non-stationary. In this paper, we propose an incremental learning approach that greatly reduces the time consumption and memory usage for training SVMs. The proposed method is fully dynamic, which stores only a small fraction of previous training examples whereas the rest can be discarded. It can further handle unseen labels in new training batches. The classification experiments show that the proposed method achieves the same level of classification accuracy as batch learning while the computational cost is significantly reduced, and it can outperform other incremental SVM approaches for the new class problem.

Polarimetric SAR data have been used extensively for terrain classification applying primitives from various target decompositions as well as texture features. However, there is a source of information that has been neglected so far from polarimetric SAR classification: Color. It is a common practice to visualize polarimetric SAR data by color coding methods and thus it is possible to extract powerful color features from such pseudocolor images. In this paper, we investigate and evaluate discrimination power of color features extracted over various pseudocolor images. Experiments are conducted over the San Francisco Bay region on RADARSAT-2 data by using Support Vector Machines. The classification results show that the additional color features introduce a new level of discrimination and provide noteworthy improvement in classification performance (compared to the traditionally employed polarimetric SAR and texture features) within the application of land use and land cover classification.

Terrain classification over polarimetric synthetic aperture radar (SAR) images has been an active research field where several features and classifiers have been proposed up to date. However, some key questions, e.g., 1) how to select certain features so as to achieve highest discrimination over certain classes?, 2) how to combine them in the most effective way?, 3) which distance metric to apply?, 4) how to find the optimal classifier configuration for the classification problem in hand?, 5) how to scale/adapt the classifier if large number of classes/features are present?, and finally, 6) how to train the classifier efficiently to maximize the classification accuracy?, still remain unanswered. In this paper, we propose a collective network of (evolutionary) binary classifier (CNBC) framework to address all these problems and to achieve high classification performance. The CNBC framework adapts a “Divide and Conquer” type approach by allocating several NBCs to discriminate each class and performs evolutionary search to find the optimal BC in each NBC. In such an (incremental) evolution session, the CNBC body can further dynamically adapt itself with each new incoming class/feature set without a full-scale retraining or reconfiguration. Both visual and numerical performance evaluations of the proposed framework over two benchmark SAR images demonstrate its superiority and a significant performance gap against several major classifiers in this field.

MicroRNA-200c (miR-200c) has been shown to suppress epithelial-mesenchymal transition (EMT), which is attributed mainly to targeting of ZEB1/ZEB2, repressors of the cell-cell contact protein E-cadherin. Here we demonstrated that modulation of miR-200c in breast cancer cells regulates cell migration, cell elongation, and transforming growth factor β (TGF-β)-induced stress fiber formation by impacting the reorganization of cytoskeleton that is independent of the ZEB/E-cadherin axis. We identified FHOD1 and PPM1F, direct regulators of the actin cytoskeleton, as novel targets of miR-200c. Remarkably, expression levels of FHOD1 and PPM1F were inversely correlated with the level of miR-200c in breast cancer cell lines, breast cancer patient samples, and 58 cancer cell lines of various origins. Furthermore, individual knockdown/overexpression of these target genes phenocopied the effects of miR-200c overexpression/inhibition on cell elongation, stress fiber formation, migration, and invasion. Mechanistically, targeting of FHOD1 by miR-200c resulted in decreased expression and transcriptional activity of serum response factor (SRF), mediated by interference with the translocation of the SRF coactivator mycocardin-related transcription factor A (MRTF-A). This finally led to downregulation of the expression and phosphorylation of the SRF target myosin light chain 2 (MLC2) gene, required for stress fiber formation and contractility. Thus, miR-200c impacts on metastasis by regulating several EMT-related processes, including a novel mechanism involving the direct targeting of actin-regulatory proteins.

In this paper, we introduce dynamic and scalable Synthetic Aperture Radar (SAR) terrain classification based on the Collective Network of Binary Classifiers (CNBC). The CNBC framework is primarily adapted to maximize the SAR classification accuracy on dynamically varying databases where variations do occur in any time in terms of (new) images, classes, features and users' relevance feedback. Whenever a "change" occurs, the CNBC dynamically and "optimally" adapts itself to the change by means of its topology and the underlying evolutionary method MD PSO. Thanks to its "Divide and Conquer" type approach, the CNBC can also support varying and large set of (PolSAR) features among which it optimally selects, weighs and fuses the most discriminative ones for a particular class. Each SAR terrain class is discriminated by a dedicated Network of Binary Classifiers (NBC), which encapsulates a set of evolutionary Binary Classifiers (BCs) discriminating the class with a distinctive feature set. Moreover, with each incremental evolution session, new classes/features can be introduced which signals the CNBC to create new corresponding NBCs and BCs within to adapt and scale dynamically to the change. This can in turn be a significant advantage when the current CNBC is used to classify multiple SAR images with similar terrain classes since no or only minimal (incremental) evolution sessions are needed to adapt it to a new classification problem while using the previously acquired knowledge. We demonstrate our proposed classification approach over several medium and highresolution NASA/JPL AIRSAR images applying various polarimetric decompositions. We evaluate and compare the computational complexity and classification accuracy against static Neural Network classifiers. As CNBC classification accuracy can compete and even surpass them, the computational complexity of CNBC is significantly lower as the CNBC body supports high parallelization making it applicable to grid/cloud computing.

With the advances in ubiquitous computing, there is an increasing focus on personalization of user information especially in web-based applications and services. Currently those personalized user profiles are scattered, mostly stored for each individual service. Therefore, this prohibits the usage of those profiles in different environments such as other web-based services, shopping in local stores or sharing interests among people. The so-called Portable Personality focuses on the management and distribution of personalized profiles (in form of a digital personality representing the real-world user) through mobile devices. These portability aspects merge with the idea of cross-system personalization using a single generic user profile. We will briefly introduce some aspects related to profile representation and management with focus on attempts towards such a generic representation. The main discussion will be concentrated around profile portability and its effects on personalization especially towards cross-system support. We include different portable profile scenarios and their personalization methodologies. Furthermore, current personalization algorithms are considered with possible associations towards the presented portable scenarios. At the end, we reflect on existing challenges of current approaches in the field of portable personalization and try to provide some recommendations.

Reverse phase protein arrays (RPPAs) emerged as a very useful tool for high-throughput screening of protein expression in large numbers of small specimen. Similar to other protein chemistry methods, antibody specificity is also a major concern for RPPA. Currently, testing antibodies on Western blot for specificity and applying serial dilution curves to determine signal/concentration linearity of RPPA signals are most commonly employed to validate antibodies for RPPA applications. However, even the detection antibodies fulfilling both requirements do not always give the expected result. Chemically synthesized small interfering RNAs (siRNAs) are one of the most promising and time-efficient tools for loss-of-function studies by specifically targeting the gene of interest resulting in a reduction at the protein expression level, and are therefore used to dissect biological processes. Here, we report the utilization of siRNA-treated sample lysates for the quantification of a protein of interest as a useful and reliable tool to validate antibody specificity for RPPAs. As our results indicate, we recommend the use of antibodies which give the highest dynamic range between the control siRNA-treated samples and the target protein (here: EGFR) siRNA-treated ones on RPPAs, to be able to quantify even small differences of protein abundance with high confidence.

In this paper, we propose a dedicated application of collective network of binary classifiers (CNBC) to address the problem of incremental learning, which occurs by introducing new SAR terrain classes. Furthermore, another major goal is to achieve a high classification performance over multiple SAR images even though the training data may not be entirely accurate. The CNBC in principle adopts a “Divide and Conquer” type approach by allocating an individual network of binary classifiers (NBCs) to discriminate each SAR terrain class among others and performing evolutionary search to find the optimal binary classifier (BC) in each NBC. Such design further allows dynamic SAR class and feature scalability in such a way that the CNBC can gradually adapt its internal topology to new features and classes with minimal effort. Experiments visually demonstrate the classification accuracy and efficiency of the proposed system over eight fully polarimetric NASA/JPL AIRSAR data sets.

The EGFR-driven cell-cycle pathway has been extensively studied due to its pivotal role in breast cancer proliferation and pathogenesis. Although several studies reported regulation of individual pathway components by microRNAs (miRNAs), little is known about how miRNAs coordinate the EGFR protein network on a global miRNA (miRNome) level. Here, we combined a large-scale miRNA screening approach with a high-throughput proteomic readout and network-based data analysis to identify which miRNAs are involved, and to uncover potential regulatory patterns. Our results indicated that the regulation of proteins by miRNAs is dominated by the nucleotide matching mechanism between seed sequences of the miRNAs and 3′-UTR of target genes. Furthermore, the novel network-analysis methodology we developed implied the existence of consistent intrinsic regulatory patterns where miRNAs simultaneously co-regulate several proteins acting in the same functional module. Finally, our approach led us to identify and validate three miRNAs (miR-124, miR-147 and miR-193a-3p) as novel tumor suppressors that co-target EGFR-driven cell-cycle network proteins and inhibit cell-cycle progression and proliferation in breast cancer.

In this paper, we propose the application of collective network of (evolutionary) binary classifiers (CNBC) to address the problems of feature/class scalability and classifier evolution, to achieve a high classification performance over full polarimetric SAR images even though the training (ground truth) data may not be entirely accurate. The CNBC basically adopts a “Divide and Conquer” type approach by allocating an individual network of binary classifiers (NBCs) to discriminate each SAR image class and performing evolutionary search to find the optimal binary classifier (BC) in each NBC. Such design further allows dynamic class and SAR image feature scalability in such a way that the CNBC can gradually adapt itself to new features and classes with minimal effort. Experiments demonstrate the classification accuracy and efficiency of the proposed system over the fully polarimetric AIRSAR San Francisco Bay data set.

In this paper, we propose an incremental evolution scheme within collective network of (evolutionary) binary classifiers (CNBC) framework to address the problem of incremental learning and to achieve a high retrieval performance for content-based image retrieval (CBIR). The proposed CNBC framework can still function even though the training (ground truth) data may not be entirely present from the beginning and thus the system can only be evolved incrementally. The CNBC framework basically adopts a “Divide and Conquer” type approach by allocating several networks of binary classifiers (NBCs) to discriminate each class and performs evolutionary search to find the optimal binary classifier (BC) in each NBC. This design further allows such scalability that the CNBC can dynamically adapt its internal topology to new features and classes with minimal effort. Both visual and numerical performance evaluations of the proposed framework over benchmark image databases demonstrate its efficiency and accuracy for scalable CBIR and classification.

The content-based image retrieval (CBIR) has been an active research field for which several feature extraction, classification and retrieval techniques have been proposed up to date. However, when the database size grows larger, it is a common fact that the overall retrieval performance significantly deteriorates. In this paper, we propose collective network of (evolutionary) binary classifiers (CNBC) framework to achieve a high retrieval performance even though the training (ground truth) data may not be entirely present from the beginning and thus the system can only be trained incrementally. The CNBC framework basically adopts a “Divide and Conquer” type approach by allocating several networks of binary classifiers (NBCs) to discriminate each class and performs evolutionary search to find the optimal binary classifier (BC) in each NBC. In such an evolution session, the CNBC body can further dynamically adapt itself with each new incoming class/feature set without a full-scale re-training or re-configuration. Both visual and numerical performance evaluations of the proposed framework over benchmark image databases demonstrate its scalability; and a significant performance improvement is achieved over traditional retrieval techniques.

Reverse phase protein arrays (RPPAs) emerged as a very useful tool for high-throughput screening of protein expression in large numbers of small specimen. Similar to other protein chemistry methods, antibody specificity is also a major concern for RPPA. Currently, testing antibodies on Western blot for specificity and applying serial dilution curves to determine signal/concentration linearity of RPPA signals are most commonly employed to validate antibodies for RPPA applications. However, even the detection antibodies fulfilling both requirements do not always give the expected result. Chemically synthesized small interfering RNAs (siRNAs) are one of the most promising and time-efficient tools for loss-of-function studies by specifically targeting the gene of interest resulting in a reduction at the protein expression level, and are therefore used to dissect biological processes. Here, we report the utilization of siRNA-treated sample lysates for the quantification of a protein of interest as a useful and reliable tool to validate antibody specificity for RPPAs. As our results indicate, we recommend the use of antibodies which give the highest dynamic range between the control siRNA-treated samples and the target protein (here: EGFR) siRNA-treated ones on RPPAs, to be able to quantify even small differences of protein abundance with high confidence.

Background Reverse phase protein arrays (RPPA) have been demonstrated to be a useful experimental platform for quantitative protein profiling in a high-throughput format. Target protein detection relies on the readout obtained from a single detection antibody. For this reason, antibody specificity is a key factor for RPPA. RNAi allows the specific knockdown of a target protein in complex samples and was therefore examined for its utility to assess antibody performance for RPPA applications. Results To proof the feasibility of our strategy, two different anti-EGFR antibodies were compared by RPPA. Both detected the knockdown of EGFR but at a different rate. Western blot data were used to identify the most reliable antibody. The RNAi approach was also used to characterize commercial anti-STAT3 antibodies. Out of ten tested anti-STAT3 antibodies, four antibodies detected the STAT3-knockdown at 80-85%, and the most sensitive anti-STAT3 antibody was identified by comparing detection limits. Thus, the use of RNAi for RPPA antibody validation was demonstrated to be a stringent approach to identify highly specific and highly sensitive antibodies. Furthermore, the RNAi/RPPA strategy is also useful for the validation of isoform-specific antibodies as shown for the identification of AKT1/AKT2 and CCND1/CCND3-specific antibodies. Conclusions RNAi is a valuable tool for the identification of very specific and highly sensitive antibodies, and is therefore especially useful for the validation of RPPA-suitable detection antibodies. On the other hand, when a set of well-characterized RPPA-antibodies is available, large-scale RNAi experiments analyzed by RPPA might deliver useful information for network reconstruction.

Cold-adapted species in the Northern Hemisphere frequently show arctic-alpine discontinuous ranges at high latitudes and on mountains farther south, but area connectivity through current and historical gene flow remains unclear. We used the coalescent-based program IMa (Isolation with Migration-analytic) to test for migration among disjunct European areas of arctic-alpine wolf spiders of the Pardosa saltuaria group. Mitochondrial (ND1) and nuclear (ITS1, 5.8S rDNA, ITS2) markers were analyzed simultaneously. Gene flow was unidirectional from Scandinavia to the Alps and the Carpathians, complex with respect to intermediate relict areas in central Europe, and very limited in outlying areas in the Balkans and Pyrenees. Population connectivity may have been greater during glacial events that might alternatively account for the inferred arctic-alpine links. A simulation study under various demographic histories (using a new module in the Mesquite package, which models episodic migration) showed that the empirical results are equally consistent with moderate levels of ongoing (continuous) migration or, alternatively, with strong migration bursts at the last glacial maximum but not at earlier times. Habitat connectivity was probably maximal during glacial events, illustrating the potential influence of ecology and life history on organismal responses to past climatic change.

Breast cancer is the most common cancer and the second leading cause of cancer-related deaths among women. MicroRNAs (miRNAs) have been shown to be key regulators of cancer-related processes including cell proliferation, survival, migration and invasion. Here, we performed a small scale miRNA mimic screen to identify miRNAs leading to inhibition of cell survival and epithelial-mesenchymal transition (EMT), both of which are key parameters for tumor growth and metastasis. We identified a miRNA (miR-644) which leads to apoptosis in p53-mutated breast cancer cell lines, but not in p53-wild type ones. Furthermore, in vitro migration and invasion assays revealed that miR-644 inhibits both migration and invasion by interfering with the EMT process. Then, we combined whole genome mRNA profiling with target prediction algorithms to determine the potential targets of this miRNA, and identified transcriptional co-repressor CTBP1 as a novel direct target which is already known for limiting apoptosis by repressing Bax transcription. CTBP1 has also been reported to bind ZEB transcription factors and assists EMT by enhancing ZEB-mediated downregulation of E-Cadherin. We showed that silencing of CTBP1 phenocopies the effects of miR-644 in induction of p53-independent apoptosis and blocking EMT and cell invasion. Currently, we are testing the role of miR-644 and its target CTBP1 in tumor growth and metastasis in in vivo assays. Overall, our results suggest that miR-644 is a novel tumor suppressor miRNA co-regulating EMT and cell survival and it could be a potential target for breast cancer therapy. Note: This abstract was not presented at the meeting. Citation Format: Umar Raza, Stefan Uhlmann, Emre Yurdusev, Stefan Wiemann, Ozgur Sahin. A novel tumor suppressor miRNA co-regulating EMT and p53-independent cell survival in breast cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 241. doi:10.1158/1538-7445.AM2015-241