Characterization of miR-574-5p decoy to CUGBP1 in human lung cancer cells using a mass spectrometry proteomics approach Vom Fachbereich Biologie der Technischen Universität Darmstadt Zur Erlangung des akademischen Grades Doctor rerum naturalium (Dr. rer. nat.) Dissertation von M. Sc. Anne Caterina Emmerich Erstgutachterin: Dr. Meike Julia Saul Zweitgutachterin: Prof. Dr. Beatrix Süß Darmstadt 2020 __________________________________________________________________________ 2 Emmerich, Anne Caterina: Characterization of miR-574-5p decoy to CUGBP1 in human lung cancer cells using a mass spectrometry proteomics approach Darmstadt, Technische Universität Darmstadt Jahr der Veröffentlichung der Dissertation auf TUprints: 2020 URN: urn:nbn:de:tuda-tuprints-91406 Tag der mündlichen Prüfung: 28.02.2020 Veröffentlicht unter CC BY-SA 4.0 International https://creativecommons.org/licenses/ __________________________________________________________________________ 3 In der Wissenschaft gleichen wir alle nur den Kindern, die am Rande des Wissens hie und da einen Kiesel aufheben, während sich der weite Ozean des Unbekannten vor unseren Augen erstreckt. Sir Isaac Newton (1643 – 1727) __________________________________________________________________________ 4 Table of Contents Summary ...............................................................................................................................9 Zusammenfassung .............................................................................................................10 1. Introduction .................................................................................................................12 1.1 Post-transcriptional mechanisms of gene regulation ...................................................12 1.1.1 Alternative splicing................................................................................................14 1.1.2 RNA-binding proteins (RBPs) ...............................................................................16 1.1.2.1 CUGBP1 and the CELF family of RBPs .........................................................18 1.1.3 MicroRNAs (miRs) ................................................................................................21 1.1.3.1 Canonical miR functions ................................................................................23 1.1.3.2 Non-canonical miR functions ..........................................................................24 1.1.3.3 MiR-574-5p ....................................................................................................25 1.2 mPGES-1-derived PGE2 in cancer development .........................................................27 1.2.1 Regulation of mPGES-1 by the miR-574-5p/CUGBP1 decoy mechanism in human lung cancer....................................................................................................................29 1.3 Aim of the study ..........................................................................................................30 2. Materials and Methods ................................................................................................31 2.1 Cell culture methods ...................................................................................................31 2.1.1 Cell culture conditions ..........................................................................................31 2.1.2 Depletion of CUGBP1 using RNA interference .....................................................31 2.1.3 Overexpression of miR-574-5p .............................................................................32 2.1.4 Depletion of miR-574-5p by LNA™ inhibitors ........................................................32 2.1.5 Wound healing assay ...........................................................................................33 2.1.6 Trans-well migration assay ...................................................................................33 2.2 RNA methods .............................................................................................................34 2.2.1 RNA extraction .....................................................................................................34 2.2.2 mRNA or miR quantification by qRT-PCR.............................................................34 2.2.3 RNA immunoprecipitation (RIP) ............................................................................36 2.3 Protein methods ..........................................................................................................38 2.3.1 Soluble and microsomal fraction preparation ........................................................38 2.3.2 Determination of protein concentration .................................................................38 2.3.3 SDS-PAGE and Western Blot ...............................................................................38 2.3.4 TMT labelling and mass spectrometry ..................................................................39 2.4. Bioinformatical methods .............................................................................................40 __________________________________________________________________________ 5 2.4.1 3’UTR analysis .....................................................................................................40 2.4.2 Mass Spectrometry data analysis .........................................................................41 2.4.3 Ingenuity pathway analysis (IPA) ..........................................................................41 2.5 Fluorescent labeling techniques ..................................................................................42 2.5.1 Immunostaining ....................................................................................................42 2.5.2 Fluorescence in situ hybridization (FISH) .............................................................42 2.6 Microscopy and image acquisition...............................................................................43 2.6.1 Immunostaining and FISH images ........................................................................43 2.6.2 Wound healing assay images ...............................................................................43 2.7 Statistics .....................................................................................................................43 3. Results .........................................................................................................................44 3.1 Verification of CUGBP1 binding via RIP ......................................................................44 3.1.1 Establishment of CUGBP1 RIP protocol ...............................................................44 3.1.2 CUGBP1 binds to miR-574-5p and mPGES-1 mRNA ...........................................45 3.2. TMT-based proteomics study of IL-1β-stimulated A549 cells .....................................46 3.2.1 Proteome changes in A549 upon ΔCUGBP1, ΔmiR-574-5p and miR-574-5p oe ..48 3.2.2 Validation of TMT proteomics study using Western blot analysis ..........................52 3.3 Physiological impact ...................................................................................................55 3.3.1 Pathway analysis predicts canonical pathways, upstream regulators and biological functions ........................................................................................................................55 3.3.2 Influence of miR-574-5p and mPGES-1 on migratory behavior of A549 cells .......56 3.4 Identification of new CUGBP1 targets .........................................................................57 3.4.1 Western blot analysis of microsomal proteins upon ΔCUGBP1 ............................57 3.4.2 Binding of CUGBP1 to mRNAs of novel canonical targets ....................................58 3.5 Identification of novel miR-574-5p/CUGBP1 decoy targets .........................................59 3.5.1 Investigating a “decoy regulation pattern” via Western blot analysis .....................59 3.5.2 Stringent “decoy regulation pattern” in the proteomics study ................................62 3.5.3 Binding analysis of potential decoy targets ...........................................................63 3.6 Subcellular localization of CUGBP1 and miR-574-5p in A549 cells .............................64 3.7 Bioinformatical analysis of 3’UTR splicing patterns .....................................................65 4. Discussion ...................................................................................................................69 4.1 Insights into the proteome of A549 lung cancer cells ..................................................69 4.2. Discovery and verification of new canonical CUGBP1 targets ....................................70 4.3 Decoy target search ....................................................................................................72 4.4 Bioinformatical 3’UTR analysis revealed unique splice pattern ....................................73 __________________________________________________________________________ 6 4.5 Physiological impact ...................................................................................................74 4.5.1 Influence of miR-574-5p on metastasis .................................................................74 4.5.2 Influence of mPGES-1 on metastasis ...................................................................75 4.6 Outlook .......................................................................................................................76 References ..........................................................................................................................78 Appendix .............................................................................................................................96 Abbreviations ....................................................................................................................96 Supplementary data ..........................................................................................................99 Curriculum vitae .............................................................................................................. 104 Ehrenwörtliche Erklärung ................................................................................................ 106 Danksagungen ................................................................................................................ 107 __________________________________________________________________________ 7 List of Figures Figure 1: mRNA processing..................................................................................................13 Figure 2: Different types of AS. .............................................................................................15 Figure 3: General structure of a GU-AG intron......................................................................16 Figure 4: Interaction of RBPs with RNAs. .............................................................................17 Figure 5: General structure of the CELF family members. ....................................................19 Figure 6: miR biogenesis. .....................................................................................................22 Figure 7: Canonical targets regulated by miR-574-5p. ..........................................................26 Figure 8: Prostanoid biosynthesis. ........................................................................................28 Figure 9: Regulation of mPGES-1 gene expression via the miR-574-5p/CUGBP1 decoy mechanism. ..........................................................................................................................29 Figure 10: Boyden chamber set-up. ......................................................................................34 Figure 11: Validation of RIP protocol. ...................................................................................45 Figure 12: CUGBP1 binding to mPGES-1 mRNA and miR-574-5p in RIP assays. ...............46 Figure 13: Quantification of CUGBP1 knockdown. ...............................................................47 Figure 14: TMT based proteomics approach. .......................................................................48 Figure 15: Expected regulations in the proteomics study. .....................................................49 Figure 16: Numbers of proteins differentially expressed upon ΔCUGBP1, ΔmiR-574-5p or miR- 574-5p oe in soluble and microsomal fraction of the proteomics study. ................................51 Figure 17: Proteomics validation using Western blot analysis. ..............................................54 Figure 18: Top ten regulated biological processes predicted by IPA. ....................................56 Figure 19: Migration assays..................................................................................................57 Figure 20: Protein levels of potential CUGBP1 targets in IL-1β-stimulated A549 cells with manipulated CUGBP1 levels. ...............................................................................................58 Figure 21: Binding of CUGBP1 to potential new target mRNAs. ...........................................59 Figure 22: Schematic overview of the decoy mechanism. ....................................................60 Figure 23: Investigating a “decoy regulation pattern” via Western blot analysis. ...................61 Figure 24: Proteins with a stringent “decoy regulation pattern” in the proteomics study. .......63 Figure 25: Binding of CUGBP1 to potential decoy targets.....................................................63 Figure 26: Subcellular localization of CUGBP1 and miR-574-5p in A549 cells......................64 Figure 27: High stringency and low stringency approach in bioinformatical 3‘UTR analysis. .66 Figure 28: Transcripts from low stringency 3’UTR analysis. ..................................................68 file:///D:/PhD%20-28.10.19/Doktorarbeit%20Dissertation%20191014/Anne%20Diss%20191213%20final.docx%23_Toc29285323 file:///D:/PhD%20-28.10.19/Doktorarbeit%20Dissertation%20191014/Anne%20Diss%20191213%20final.docx%23_Toc29285331 file:///D:/PhD%20-28.10.19/Doktorarbeit%20Dissertation%20191014/Anne%20Diss%20191213%20final.docx%23_Toc29285334 __________________________________________________________________________ 8 List of Tables Table 1. Comparison of different GRE clusters. ....................................................................19 Table 2. PCR program for mRNA quantification ...................................................................35 Table 3. Primer used for qRT-PCR .......................................................................................35 Table 4. PCR program for miR quantification .......................................................................36 Table 5. RIP buffer composition ...........................................................................................37 Table 6. Gel composition for SDS-PAGE..............................................................................39 Table 7. Primary antibodies for Western blot analysis ..........................................................39 Table 8. Numbers of increased (↑) and decreased (↓) proteins in each fraction and each condition of the proteomics study compared to their respective controls...............................50 Table 9. Potential canonical miR-574-5p targets. .................................................................52 Table 10. List of potential binding motifs of CUGBP1 ...........................................................65 Table 11. Number of transcripts fulfilling the low stringency analysis criteria. .......................67 Table 12. Top three upregulated proteins of the proteomics study. .......................................99 Table 13. Top three downregulated proteins of the proteomics study. ..................................99 Table 14. IPA prediction of top five canonical pathways. .................................................... 100 Table 15. IPA prediction of top five upstream regulators. .................................................... 100 Table 16. Summary of all analyzed proteins. ...................................................................... 101 Table 17. Transcripts from bioinformatical 3’UTR analysis ................................................. 103 Summary __________________________________________________________________________ 9 Summary The bioactive lipid mediator prostaglandin (PG) E2 is generated by the enzyme microsomal prostaglandin E2 synthase-1 (mPGES-1). Especially in lung tumors, mPGES-1 was shown to be significantly overexpressed which contributes to a pro-tumorigenic microenvironment. Current medication interfering with the negative effects of PGE2 comprise only non-steroidal anti-inflammatory drugs (NSAIDs). While these have effective analgesic properties and are commonly used as pain killers, treatment of tumor growth is still inconclusive. Probably, only subgroups of cancer patients exhibit an abnormal prostanoid profile. Therefore, a reliable biomarker is necessary to identify patients who could benefit from said treatment. In a recent study, it was discovered that a specific microRNA (miR) can induce mPGES-1 gene expression. The miR-574-5p prevents binding of the inhibitory CUG-RNA binding protein 1 (CUGBP1) to the 3´ untranslated region (UTR) of mPGES-1. This non-canonical decoy function of miR-574-5p leads to an increased mPGES-1 protein level. Following, an induction of PGE2 formation triggers the progression of lung tumor growth in vivo. Interestingly, the entire influence on tumor progression could be blocked with the addition of a specific mPGES-1 inhibitor, confirming the huge influence of miR-574-5p on (patho-) physiological mPGES-1 functions. In this study, a proteomics approach was conducted in order to further characterize this decoy mechanism in human lung cancer cells. The aim was to gather global insights into the proteome changes related to miR-574-5p and CUGBP1, especially in a compartment specific manner. Further, it was aimed to identify new CUGBP1 targets and find out if they are also affected by the decoy function of miR-574-5p. Two new CUGBP1 targets were validated herein: NADH-Ubiquinone Oxidoreductase Core Subunit S2 (NDUFS2) and Mothers against decapentaplegic homolog 2 (SMAD2). However, both NDUFS2 and SMAD2 are independent from miR-574-5p levels. In a bioinformatical 3’UTR analysis of potential CUGBP1 targets, it was shown that the specific splicing pattern of mPGES-1 is unique, comprising two long CUGBP1 binding motifs with a 3’UTR intron in between. Only 11 other transcripts harbor a similar but not identical pattern in their sequence. Hence, it is assumable that this novel decoy mechanism is specifically regulating mPGES-1 in A549 lung cancer cells. This might be caused by the unique splice pattern of mPGES-1. However, further experiments are needed to confirm this hypothesis. Nevertheless, specificity of the decoy mechanism would open up new opportunities for lung cancer patients. By using miR-574-5p as a biomarker, one could stratify those patients with high mPGES-1 levels who have a higher chance to benefit from the anti- tumorigenic potential of NSAID therapy. Zusammenfassung __________________________________________________________________________ 10 Zusammenfassung Der biologisch aktive Lipidmediator Prostaglandin (PG) E2 wird enzymatisch von der mikrosomalen Prostaglandin E2 Synthase (mPGES-1) generiert. Es konnte gezeigt werden, dass mPGES-1 speziell in Tumoren der Lunge stark angereichert ist. Durch die damit verbundene vermehrte Bildung von PGE2 kommt es zu einer kanzerogenen Tumorumgebung. Hier setzen sogenannte non-steroidal anti-inflammatory drugs (NSAIDs) an. Während diese effektive analgetische Eigenschaften aufweisen und häufig als Schmerzmittel genutzt werden, ist die Behandlung von Tumoren dennoch umstritten. Es wird vermutet, dass nur eine Subpopulation von Krebspatienten ein entartetes Prostanoidprofil aufweist. Daher wäre ein verlässlicher Biomarker vonnöten, mit dem man diese Patienten identifizieren kann, die dann von einer NSAID-Therapie profitieren könnten. Kürzlich wurde eine spezielle mikroRNA (miR) identifiziert, die die mPGES-1 Genexpression induzieren kann. Die miR-574-5p verhindert die Bindung des inhibitorischen CUG RNA Bindeprotein 1 (CUGBP1) an den 3‘ untranslatierten Bereich (UTR) von mPGES-1. Diese nicht-kanonische Decoy Funktion von miR-574-p führt zu einem erhöhten mPGES-1 Proteinlevel. Dadurch kommt es zur vermehrten PGE2 Synthese, welche daraufhin das Tumorwachstum in vivo begünstigt. Durch die zeitgleiche Gabe eines spezifischen mPGES-1 Inhibitors konnte jedoch der gesamte Einfluss auf das Voranschreiten des Tumors verhindert werden. Dadurch konnte der immense Einfluss der miR-574-5p auf die (patho-) physiologischen Funktionen von mPGES-1 gezeigt werden. In dieser Studie wurde nun der Decoy Mechanismus mit Hilfe einer Proteomik-Studie in humanen Lungenkarzinomzellen weiter charakterisiert. Das Ziel war es globale Kompartment- spezifische Einsichten in die miR-574-5p- und CUGBP1-vermittelten Änderungen des Proteoms zu erhalten. Des Weiteren sollten neue CUGBP1-regulierte Transkripte identifiziert werden, um herauszufinden, ob diese ebenfalls durch den Decoy Mechanismus der miR-574-5p beeinflusst werden. Zwei neue CUGBP1-regulierte Transkripte konnten dabei validiert werden: die NADH-Ubiquinone Oxidoreductase Core Subunit S2 (NDUFS2) sowie das Signalmolekül Mothers against decapentaplegic homolog 2 (SMAD2). Die Regulation beider Zielgene war jedoch unabhängig von miR-574-5p. In einer bioinformatischen Analyse aller 3’UTRs von möglichen CUGBP1-regulierten Transkripten stellte sich heraus, dass das spezifische mPGES-1 Spleißmuster einzigartig ist. Es umfasst zwei lange CUGBP1 Bindesequenzen, getrennt durch ein 3’UTR Intron. Ein ähnliches, wenn auch nicht identisches Muster, konnte in nur 11 weiteren Transkripten gefunden werden. Daher ist es annehmbar, dass der Decoy Mechanismus spezifisch nur die mPGES-1 Expression in A549 Lungenkarzinomzellen reguliert. Dies könnte potenziell auf das spezifische Spleißmuster zurückzuführen sein, obwohl weitere Experimente nötig sind, um diese Hypothese zu bestätigen. Nichtsdestotrotz könnte die Spezifität des Decoy Mechanismus neue Zusammenfassung __________________________________________________________________________ 11 Möglichkeiten für Lungenkrebspatienten darstellen. MiR-574-5p könnte als Biomarker genutzt werden, um jene Patienten mit hohen mPGES-1 Leveln zu identifizieren. Diese könnten dann von den inhibitorischen Effekten einer NSAID-Behandlung auf das Tumorwachstum profitieren. Introduction __________________________________________________________________________ 12 1. Introduction Transcription is one of the most fundamental processes in biological systems. It reliably produces the required amounts of RNA which are necessary for the maintenance of general cellular functions but also for the reaction to rapid changes of environmental circumstances. In multicellular organisms, every individual cell shares the same genome, however expressing a characteristic cell type specific protein profile. To orchestrate this kind of specificity, a tremendous amount of regulatory action is required. In fact, various post-transcriptional regulation mechanisms are responsible for fine-tuning of the intracellular protein repertoire [1]. 1.1 Post-transcriptional mechanisms of gene regulation Post-transcriptional regulation can occur at any step of mRNA processing, from transcription to translation, both within the nucleus as well as in the cytoplasm [2]. Shortly, mRNA processing starts as soon as transcription is initiated by Polymerase II [3]. Afterwards, pre- mRNAs are capped, spliced, edited and polyadenylated. Then, mature mRNAs are exported into the cytoplasm through the nuclear pore complex [4]. In the cytoplasm, mRNAs can be translated into proteins, stored in stress granules, processing bodies (P-bodies) or they can be marked for degradation (see Figure 1). All these steps are mediated by a myriad of factors, most of them RNA-binding proteins (RBPs) with some of them binding the pre-mRNA even during transcription [5]. Regulatory mechanisms include among others interference with splicing, editing or polyadenylation as well as mRNA (de-) stabilization, localization and finally translational inhibition [2]. For instance, polyadenylation is a critical step as it stabilizes the mRNA molecule and prevents rapid degradation in the cytoplasm [6]. The longer the 3’ poly(A)-tail, the longer the mRNA can survive in the cytoplasm, where it gradually gets shorter by deadenylation [7]. Initiation of translation however, stops further deadenylation which indicates that poly(A)-shortening is a mechanism of regulation. The deadenylation is mediated by the poly(A) ribonuclease (PARN) [8]. Recruitment of PARN is thereby mediated by binding of RBPs or miRs [9] [10] [11] [12]. Another example for a regulatory mechanism is mRNA storage in intracellular particles called P-bodies. mRNAs get recruited there by interaction with RBPs or miRs [13]. P-bodies were described to play a role in all kinds of mRNA decay mechanisms, however it was also demonstrated that mRNAs were temporary stored there for later translation [13] [14] [15]. Introduction __________________________________________________________________________ 13 Figure 1: mRNA processing. Genes consists of a promoter region directly upstream of the transcription start site (TSS), numerous exons and introns as well as untranslated regions at the 5’ and 3’ end. The DNA is transcribed by Polymerase II, generating the precursor mRNA (pre-mRNA). Processing includes addition of a 5’ 7-methylguanylate (5’ m7G) cap, polyadenylation of the 3’ end (An 3’) as well as splicing to remove intronic sequences. All these processing steps can concurrently occur during transcription although a consecutive order is indicated in the figure for facilitation purpose. Mature mRNA is exported into the cytoplasm where its fate is influenced by localization, degradation or successful translation. Modified from [14] [15]. Post-transcriptional regulation is mainly based on cis-regulatory elements (CRE) [18]. In this context, CREs are defined as regions of non-coding DNA or RNA that provide binding sites for trans-acting factors such as RBPs or transcription factors. CREs can be upstream or downstream of coding sequences (CDS) or even within introns and are mostly termed Introduction __________________________________________________________________________ 14 enhancers or silencers, depending on their regulatory function [19]. Of note, one CRE can be bound by numerous trans-acting factors and vice versa, which is called pleiotropy. Moreover, interactions, synergisms or competition of various trans-acting factors as well as the combination of activating or inhibitory CREs further elevates the complexity of gene expression regulation [18]. In the following chapters, three types of post-transcriptional regulation mechanisms are described in more detail as they stand in the focus of this thesis (see chapters 1.1.1 Alternative Splicing, 1.1.2 RNA-binding proteins (RBPs) and 1.1.3 microRNAs (miRs)). 1.1.1 Alternative splicing Alternative splicing (AS) is a fine tuning process of higher eukaryotes which enables a variation in the in- or exclusion of sequence parts of a pre-mRNA and thus provides greater biodiversity of proteins from an established number of genes [20]. There are several estimations towards how many of all transcripts are alternatively spliced which range up to 25% in Caenorhabditis elegans (C. elegans), 60% in Drosophila melanogaster [21] and even 90% in humans [22] [23]. Investigations concerning different human tissues revealed that roughly 50% of alternatively spliced isoforms are differentially expressed among tissues indicating that AS also provides cell type specific isoforms [24]. However, there are recent publications questioning the impact of AS on protein diversity, as apparently many RNA isoforms could not be found on protein level in large scale proteomics studies [19] [20]. Nevertheless, this does not render the fact that AS is a pivotal process in regard of post-transcriptional regulation and physiological homeostasis. Defects in AS can even cause different diseases most of all cancer development and progression [27]. During the splicing process it is decided which parts of the sequence are included in the mature mRNA and which ones are removed [28]. Consequentially, several mature mRNAs can result from one pre-mRNA. There are distinct types of AS (see Figure 2). Constitutive splicing describes the canonical form including one exon after the other and removing all the intronic parts. One variant is exon skipping, which describes the case when one exon is cut out together with the adjacent introns. Vice versa, an intron can also be included in the mature mRNA. Further, there can be mutually exclusive exons, also called cassette exons. Finally, the 5’ and 3’ splice sites can vary. All these variations lead to different mRNAs and thereby also to different amino acid sequences during translation [29]. Introduction __________________________________________________________________________ 15 Figure 2: Different types of AS. Colored boxes indicate exons; thin lines in between resemble introns. The different forms of AS can lead to distinct mature mRNAs which also influences the amino acid sequence of the proteins. Modified from [30]. The large complex which mediates the splicing process is called spliceosome. It comprises over 300 proteins and nucleic acids [31] [32], while the core is composed of five small nuclear ribonucleoproteins (snRNPs) U1-2, 4-6 [33]. Furthermore, additional trans-acting factors of the heterogeneous nuclear ribonucleoprotein (hnRNP) [34] or Serine/arginine (SR) protein family [35] act as repressors or activators by binding to silencer or enhancer regions to regulate splicing activity depending on the cell type, the developmental stage or the gene [36]. Generally, the spliceosome binds to splice sites and cuts out the intronic sequences. Thereby, assembly and dissociation of the spliceosome subunits appear periodically and recur for every intron [37]. Thus, the splicing process can be divided in three parts: assembly of the spliceosome, catalytic splicing (actual removal of the intron) and recycling of the snRNPs [29]. The decision which part of the pre-mRNA is an exon and which is intron depends on a number of cis-elements. Conserved cis-elements adjacent to splicing sites are called splicing acceptor sites. They are found on exon-intron-boundaries of pre-mRNAs and are often UG-rich such as UUCUG and UGUU [38] [39]. Generally, introns consist of a 5’ donor site, a branch point and a 3‘ acceptor site. Apart from the few self-splicing introns [40], most introns need a spliceosome to be cut out. There are two types of introns: the most common type of intron is processed by the so-called major Introduction __________________________________________________________________________ 16 spliceosome and has a 5’ GU and a 3’ AG, whereupon the GU is strictly conserved and surrounded by a less conserved sequence (see Figure 3). The much more uncommon AU-AC type intron is processed by the minor spliceosome [41]. Figure 3: General structure of a GU-AG intron. Framed by two exons, the intron starts with a 5’ splice site that contains a conserved GU within a less conserved sequence. The branch point containing a highly conserved A is followed by a pyrimidine rich region and finally the AG comprising 3’ splice site. Modified from [41] [42]. Py: Pyrimidine nucleobase (cytosine or uracil) Interestingly, splicing not only occurs in the CDS of pre-mRNAs but also in UTRs. Removal of introns obviously has a tremendous impact on the length. Shorter 3'UTR isoforms have fewer binding sites for trans-acting factors such as miRs and are consequently more stable, resulting in higher protein level [43]. Higher expression rates in turn are linked to proliferating cells. Therefore, it is no surprise that shorter 3’UTRs can often be found in oncogenes and are associated with carcinogenic cells [44]. Nevertheless, it is often thought that 3’UTR splicing inevitable leads to nonfunctional transcripts due to nonsense-mediated decay (NMD). However, this is only the case when the intron is less than 55 nucleotides (nt) from a termination codon resulting in a pre-mature stop codon [45] [46]. Splicing within UTRs still does not gain much attention, although estimations on how many human UTRs contain introns range from 35% in 5’UTRs [47] to 6-16% in 3’UTRs [48] [49]. In general, AS has a crucial impact on all kinds of cellular functions. Dysregulation can even lead to variances in cell cycle control, proliferation or apoptosis [50]. Therefore, it is discussed in literature to announce AS an additional hallmark of cancer [51]. Especially genomic splice site point mutations seem to be affected. For instance, there are at least 29 different splice site mutations in the Tumor Protein P53 (TP53/p53) gene that are found in all kinds of tumor types including lung cancer, breast cancer and leukemia [52]. It is well described that during normal differentiation oncogenes are inactivated via AS, whereas in tumor cells AS is manipulated to inactivate tumor suppressors [53] [54] [55] [56]. This underlines the importance of AS and post- transcriptional regulation in general for physiological and cellular homeostasis. 1.1.2 RNA-binding proteins (RBPs) RBPs fulfill a crucial role in post-transcriptional gene expression. Generally, it is proposed that unbalanced expression and function of RBPs occurs in the context of uncontrolled cell Introduction __________________________________________________________________________ 17 proliferation and promotes tumor growth [57]. This reflects the relevance of protein-RNA interactions in cellular homeostasis. However, a definite causal connection has not yet been described [58] [59]. In fact, RBPs control stability, decay, translation as well as localization of mRNAs (see Figure 4A). They are able to shuttle mRNAs between the nucleus and the cytoplasm to actively translating ribosomes, stress granules or P-bodies [4] [60] [61] [62] [63] [64]. Vice versa, RBPs can also be the target of regulation by RNAs rather than being a regulating factor. The discovery of long non-coding RNAs (lncRNAs) and their association with the organization, scaffolding or inhibition of protein arrangement made it clear that RNA can also act on its bound protein, which contradicts the general view that it is normally the other way round. Thereby, RNAs can have an impact on localization, stability, interactions or functions of a protein (see Figure 4B) [65]. Figure 4: Interaction of RBPs with RNAs. Functional crosstalk of RBP/RNA interaction can occur in both directions. (A) RBPs can bind to RNAs via RNA-binding domains, influencing various aspects of the RNAs functions and fate. (B) By displaying protein-binding activity, certain RNAs (e.g. lncRNA) can affect various protein functions. Modified from [65]. The formation of a RNA-Protein-complex, also called ribonucleoprotein (RNP) complex is mediated by specific RNA-binding domains (RBDs) [66] [67]. Thereby, different specificities and affinities are based on sequence and structure of the RNA target. RBDs can be classified as followed: RNA-recognition motifs (RRMs), double stranded RBDs or Zinc finger domains [4]. Those RBDs can then bind to CREs mostly in 3’UTRs of mRNAs. One of the best described CREs are probably AU-rich elements (AREs). They are ubiquitously found in 3’UTRs of mRNAs [68] and interact with a variety of RBPs which can tag the RNA for Introduction __________________________________________________________________________ 18 rapid degradation [69] but also stabilization [70]. In recent years, a similar sequence element called GU-rich element (GRE) was discovered. GREs are highly conserved throughout evolution and were primarily found in 3’UTRs of mRNAs with short half-lives [71]. Generally, GU-rich sequences appear in ca. 5% of RNAs in the human transcriptome [72]. They can regulate splicing, translation, deadenylation or mRNA decay, depending on the RBP they interact with during different intracellular settings [73] [74]. It was elucidated that GREs are specifically targeted by the CELF (CUG-Binding protein and embryonically lethal abnormal vision-type RNA binding protein like factors) family of RBPs [71]. 1.1.2.1 CUGBP1 and the CELF family of RBPs The CELF family influences a wide range of post-transcriptional processes, such as AS [75] [76] [77], deadenylation [9], C-U editing [78] [79], transport [80] [81] [82], translation [83] and most of all mRNA decay [84] . The RBP family is evolutionary conserved and comprises 6 members: CELF1-6 [85] [86] [87]. All of them harbor three RRMs, two N-terminal RBDs and one in the C-terminal region [88] [89] (see Figure 5). The divergent domain is potentially important for functional regulation but this is still discussed throughout literature. While CELF1 (CUG-RNA binding protein 1; CUGBP1) and CELF2 (CUGBP2) are ubiquitously expressed among cell types and tissues and fulfill a role in embryonic development [90] [91] [92] [93], CELFs 3-6 are only expressed in fully developed cells and are exclusively found in nervous tissue [94] [95] [88]. Although, it was proposed several years ago that the family members have redundant functions in mRNA regulation [96], it was later demonstrated that all have specific RNA binding affinities and distinct functions [97]. Physiologically, CELFs are crucial regulators for all kinds of developmental processes. This is especially well described for xenopus [98]. Besides, there are also several mouse models describing CELF-mediated shifting from fetal to adult alternative splice variants of several skeletal muscle transcripts [75] [99] [100]. Whereas it is experimentally confirmed that CELFs bind to C/UG-rich splicing acceptor sites, a prediction if this inhibits or activates AS is impossible, as it strongly depends on the cellular context [101]. Introduction __________________________________________________________________________ 19 Figure 5: General structure of the CELF family members. All CELF members consist of three RRMs: two N-terminal ones and one C-terminal RRM, with a divergent domain in-between that distinguishes them. Numbers indicate amino acids. Modified from [102]. CELFs preferentially bind to 15-22 nt long GU-rich sequences [103] [104] [105] whereas, most RBDs bind shorter (GU-rich) motifs like TAR DNA Binding Protein (TARDBP) [106]. GREs have defined consensus sequences based on the pentameric GUUUG (see Table 1) which was originally identified in human T-cells [71]. Today, it is known that GRE-containing transcripts appear in a variety of cells, including other immune cells, mouse brain cells or human cancer cells [84]. While the exact outcome mostly depends on cellular and environmental context, GREs also transfer instability when cloned in otherwise stable transcripts [71]. Table 1. Comparison of different GRE clusters. GRE mRNAs were clustered (one mismatch allowed) into five subclasses based on the number of pentameric (GUUUG) repeats and surrounding sequences. K stands for G or U. Clusters I and II contain four or more overlapping GUUUG pentamers and are found in only a few hundred transcripts such as transcription factors, cell cycle regulators and intercellular communication genes. Clusters III, IV and V represent shorter sequences with less repetition and are found in several thousand transcripts. CELF: CUGBP Elav-Like Family Member; ELAVL4: Embryonic Lethal Abnormal Vision Like Neuron-Specific RNA Binding Protein 4; RBM38: RNA Binding Motif Protein 38; TARDBP: TAR DNA Binding Protein. Based on [107] and [72]. Cluster GRE sequences Functional categories Trans-acting factors I GUUUGUUUGUUUGUUUGUUUG transcription factors, cell cycle, cell metabolism, cell–cell communication regulators CELF1, CELF2, ELAVL4, RBM38, TARDBP, FUS II GUUUGUUUGUUUGUUUG III GUKUGUUUGUKUG IV KKGUUUGUUUGKK V KKKU/GUKUG/UKKK Introduction __________________________________________________________________________ 20 In the focus of this thesis is the CELF family member CUGBP1. It was first discovered in 1996 and described to regulate myotonic dystrophy type 1 (DM1) [108]. Initial SELEX experiments (systematic evolution of ligands by exponential enrichment) demonstrated that CUGBP1 preferably binds GU-repeat sequences (UGU) [104]. To date, it is also described to bind to GC-rich or even A-containing sequences [109]. CUGBP1 in general is known as an inhibitory post-transcriptional regulator. It is responsible for mRNA deadenylation [9], subsequent degradation [110] or AS [111] [112] and is conserved in a variety of species including humans, mice, drosophila and xenopus [113] [114]. It was found that knockdown of CUGBP1 led to a severe stabilization of GRE containing transcripts [115] [116] [117] which underlines its function as gene expression repressor. In contrast to its paralogue, CUGBP2 which rather stabilizes targets [118]. The activity of CUGBP1 is regulated via its phosphorylation status [119]. In total it has 9 described phosphorylation sites mostly on serines or threonines [120]. Through hyperphosphorylation by Protein Kinase C, CUGBP1 is stabilized and reveals elevated splicing activity in DM1 [121]. In mouse myoblasts, CUGBP1 is described to be phosphorylated at serine 28 by AKT Serine/Threonine Kinase 1, which influences its function as translational regulator during myocyte differentiation and murine heart development [122]. Finally, it was shown that phosphorylation by cyclin D3-CDK4/6 additionally interferes with CUGBP1’s RNA binding capacity [123]. So overall, phosphorylation seems to be one key factor for CUGBP1 regulation on many levels. As regulator of AS, CUGBP1-mediated exon skipping or inclusion depends on the developmental stage of the cell [112]. This function is best investigated in the context of DM1 [111]. The autosomal dominant neuromuscular disease is characterized by a trinucleotide repeat extension in the gene for myotonic dystrophy protein kinase (DMPK) resulting in an impaired gene expression [124]. In that regard, a balance between CUGBP1 and the splicing factor muscle blind like protein 1 (MBNL1) is essential. Gain of CUGBP1 function goes along with loss of function of MBNL1 which leads to AS of a variety of crucial transcripts [124]. The outcome can include heart conduction problems, impaired muscle strength, cataract development or insulin resistance [125]. Moreover, CUGBP1 influences ca. 50% of heart development-related transcripts by changing the splicing events between fetal and adult developmental stages [126]. Several promising studies with mouse models are investigating CUGBP1’s effects on cardiac dysfunction and cardiomyopathy [126] [127]. However, CUGBP1 functions are too diverse to distinguish between effects based on AS, mRNA degradation or deadenylation. Interestingly, up to now it is not known how exactly CUGBP1 mediates deadenylation of human transcripts. Concerning mammalians, it is only known that CUGBP1 recruits PARN in the human hepatoma Introduction __________________________________________________________________________ 21 cell line Huh7 [128] and cell-free assays [9]. Interaction with PARN surely does indicate involvement of deadenylation [84] but this is still under investigation. As deadenylation is a crucial step in degradation of mammalian transcripts [129] [130], it was consequential to investigate if CUGBP1 is involved in other mechanisms of mRNA decay. It is postulated that there exists some kind of CUGBP1-mediated decay process [71], but to date there are no studies elucidating the exact mechanism. It is well studied that CUGBP1 regulates whole networks of transcripts (regulons) involved in murine myoblast growth and differentiation, including crucial targets associated with cell cycle and survival [110]. In addition, CUGBP1 plays an important role in the rapid alteration of expression profiles during the activation of human T-cells through alternative polyadenylation [131]. In activated primary T-cells, hyperphosphorylation of CUGBP1 impairs its binding to target mRNAs. The results are increased protein levels of the targets which include a variety of proteins associated with an activated proliferative cell type [132]. Additionally, CUGBP1 also plays a role in the regulation of translation. It has activating properties, observed at many stages of cellular development [133] [134]. However, under stressful conditions CUGBP1 can also act as silencer and suppress translation in conjunction with several other proteins [135]. Additionally, the mode of action apparently depends on the context and cell type. It was recently described that in mesenchymal cells as well as MCF-10A breast cancer cells, CUGBP1 has a positive effect on translation of a variety of mRNAs involved in epithelial to mesenchymal transition (EMT) [136] [137]. Whereas in intestinal epithelial cells it represses translations of the insulin like growth factor 2 receptor mRNA. Overall, the mechanisms of how CUGBP1 is involved in translation, deadenylation and mRNA decay are not fully elucidated to date. 1.1.3 MicroRNAs (miRs) MiRs are highly conserved non-coding RNAs and complete the complex network of post- transcriptional regulators covered within this thesis. MiRs are described as short single stranded RNAs of approximately 21 nt length. The first miR was discovered in 1993, when Lee et al. found lin-4 in the first larval stage of the nematode C. elegans. They discovered that this small RNA was able to repress the lin-14 gene expression by complementary binding to its 3’UTR [138]. The name “microRNA” was defined not before the year 2001, though [139]. There are several ways of miR biogenesis (see Figure 6). On the one hand, a subset of miRs is derived from introns (mirtrons) or even exons of protein-coding genes, meaning their expression is depending on the host gene [140] [141] [142]. On the other hand, miRs can also by transcribed from specific miR-genes by RNA polymerase II [143] [144]. Transcription of miR-genes creates so-called primary miRs (pri-miRs) which are further processed via splicing, Introduction __________________________________________________________________________ 22 editing and polyadenylation [144] [145]. The double stranded pri-miRs are usually several hundred nucleotides long and form a hairpin structure [146] [147]. A large microprocessor complex comprising the RNA binding protein DiGeorge Syndrome Critical Region 8 (DGCR8) and the ribonuclease III Drosha further processes the pri-miR to generate the much shorter precursor-miR (pre-miR) [146] [148] [149]. In contrast, mirtron-derived pre-miRs are generated by the spliceosome and an additional processing step mediated by the debranching enzyme, to form a hairpin from a single stranded intron [150]. Nuclear export of all pre-miRs is then conducted by the shuttle protein exportin-5 which recognizes a two nt overhang at the 3’ end of the hairpin [149]. During this energy consuming step, the Ras-related nuclear protein (RAN) provides the necessary guanosine triphosphate (GTP) [151]. Cytosolic pre-miRs need to be further processed. Therefore, the RNA Polymerase III Dicer is recruited [149] [152]. Enzymatically, the loop structure is removed from the hairpin by Dicer, resulting in an 18-22 nt long miR duplex [153]. Although either strand of the miR duplex is potentially functional, usually only one strand does fulfill physiological functions, while the other one is degraded [147] [154]. Figure 6: miR biogenesis. miRs can either be transcribed from specific miR genes or spliced from intronic sequences of host genes. In the ladder case, the spliceosome and a debranching enzyme are necessary to generate the pre-miR. Specific miR genes produce an intermediate molecule called pri-miR which is capped and polyadenylated and further processed by Drosha to create the pre-miR. With the help of RAN-GTP and Exportin-5, the pre-miR is shuttled to the cytoplasm where it is processed by Dicer to generate a miR duplex, which finally leads to the mature miR of 18-22 nt length. Modified from [155]. Introduction __________________________________________________________________________ 23 MiRs fulfill an essential role in physiological homeostasis by regulating all kinds of cellular functions. Thus, it is no surprise that dysregulation of miRs can cause severe impairment of normal body functions and can lead to diseases including autoimmune conditions [156], heart [157] [158] [159] and kidney diseases [160], hereditary diseases such as non-symptomatic progressive hearing loss [161] and many different types of cancer [162]. Indeed, many miRs are associated with cancer development and progression and are intensively studied in that regard. In general, miRs that are associated with cancer are called oncomiRs [163]. This term applies to all miRs that show a differential expression level in tumor cells, if they act as oncogenes or tumor suppressors [164]. A prominent example is miR-21, which has been shown to be increased in most types of cancers including tumors of the colon, lung, breast, pancreas, as well as leukemia and lymphoma [165] [166] [167]. In contrast, in many tumors types, a global decrease in overall miR levels has been described [168] [169]. Additionally, a severe shortening of 3’UTRs was observed. Shorter 3’UTRs are beneficial because they provide a smaller number of miR binding sites, allowing for upregulation of oncogenes [170]. This is based on the canonical miR function as inhibitor of gene expression, which will be explained in the following chapter. 1.1.3.1 Canonical miR functions Conventionally, miRs are known as global gene expression repressors. They bind to 3’UTRs of target mRNAs and impair proper translation or even lead to degradation of the mRNA. Either mechanism leads to a decreased protein level. This mode of action is called RNA interference (RNAi). Originally, it was described for small interfering RNAs (siRNAs) which act against viral infections [171]. Mainly in plants, miRs show a near perfect pairing, while target recognition in animals is mediated by a specific seed region within the miR sequence. It is located at position 2-8 on the 5’ end of the miR [172]. In most cases, the seed region base pairs with responsive elements in the 3’UTR of the mRNA targets. Although, computational analysis reported that miR binding sites are also found all over 5’UTRs and coding-sequences [173] [174]. The key to canonical miR functionality is the formation of the RNA-induced silencing complex (RISC) [175]. This protein complex is formed minimally by Argonaute 2 (AGO2) [176] [177] and the respective miR, but usually it also comprises other proteins from the AGO family [178] as well as further RISC-associated proteins such as DEAD-box helicase 20 [179]. However, to date the exact composition of the RISC is not yet fully understood and seems to vary between studies. The miR duplex is recruited to the RISC by Dicer. Within the complex, it is decided which strand of the duplex functions as guide strand and which one is degraded by RISC [180]. Usually, the Introduction __________________________________________________________________________ 24 strand with higher 5’ end stability is the functional one [181]. The bound miR is then used to target specific mRNAs via Watson-crick base pairing while the AGO proteins are responsible for the mode of action of gene regulation [180]. MiRISC can influence the gene expression of mRNA transcripts via two different mechanisms [182] [183]. Dependent on the level of complementarity, miR binding either leads to translational repression or RISC-mediated degradation of the mRNAs. These are the commonly known functions of miRs, however in the last few years diverse studies described a variety of further functions and modes of action that miRs are involved with. 1.1.3.2 Non-canonical miR functions For a long time, miRs were only considered to bind to mRNAs and act as post-transcriptional repressors. Only a few years ago, it was discovered that miRs can also have a variety of totally different functions. For example, miRs which are secreted in exosomes are incorporated by donor cells and can interact with toll-like receptor 7/8 (TLR7/8) [184]. Fabbri et al. found that human miR-21 and miR-29a can both be secreted by tumor cells to act on human TLR8 or murine TLR7 in adjacent immune cells and induce a pro-inflammatory immune response [184]. In the following, several studies confirmed the interaction e.g. in the context of neuroblastoma [185], neuropathic pain [186] or murine myoblasts [187] or even in the context of Alzheimer’s disease [188]. The discovery of this unusual miR function was accompanied by a variety of studies also describing novel modes of actions. For instance, miRs were found to act as activator or silencer of transcription. The first one was miR-327, which was described to induce Cadherin-1 (CDH1) and Cold Shock Domain Containing C2 (CSDC2) in human prostate cancer cells via a complementary promoter sequence [189]. Later, there was similar evidence for various other miRs, however the exact mechanism is still unclear. Furthermore, miRs can also control miR maturation. For example, Let-7 forms a positive feedback loop by binding to its own pri-miR. In turn, further processing of the pri-miR is enhanced which elevates the mature let-7 levels in C. elegans [190]. On the other hand, miR-709 is able to block maturation of other miRs. It binds to a specific motif in the miR-15a/16-1 pri-miR structure in murine cells and inhibits further processing [191]. Moreover, miRs can also interact with other non-coding RNAs. It was found that 4% of all AGO-mRNA tags were associated with lncRNAs, indicating that miRs could recruit AGOs to lncRNAs to influence their stability or function [192]. This thesis focuses on the new non-canonical decoy function of miRs. Eiring et al. initially demonstrated that miR-328 can interact with the RBP hnRNP E2 [193]. In this context, miR-328 positively influences the gene expression of CCAAT/enhancer-binding protein alpha (CEBPA) Introduction __________________________________________________________________________ 25 by acting as competitive inhibitor to hnRNP E2 in leukemic blasts. This new decoy function is independent of the miR’s seed region and solely functions through interference with the RBP. In 2016, it was revealed that this novel mode of action does not only influence gene expression of CEBPA alone, but also S100A9 was validated as miR-328/hnRNP E2 decoy target in monocytes [194]. The list was recently extended to HMGB1 as well as 141 further proteins which were also predicted as decoy targets [195]. Hence, the miR-328 and hnRNP E2 decoy seems to have a global impact on a variety of targets and was even observed in different cell types. Recently, this decoy mechanism was also described for miR-574-5p and CUGBP1 in A549 lung cancer cells [196] which will be discussed more closely in the following chapters. 1.1.3.3 MiR-574-5p MiR-574-5p is a mirtron encoded in the first intron of the gene FAM114A1. This gene is coding for the nervous system overexpressed protein 20 (NOXP20), which is overexpressed in the brain [197] although NCBI GEO data [198] indicate that it might be expressed all over the body, especially in mesenchymal cells [199]. Nevertheless, there is still very little known about the function of NOXP20. Only the fact that it contains a caspase recruiting domain gives a hint that it might be involved in apoptosis [197]. It is implied that miR-574-5p expression is connected to NOXP20 expression. However, since there are no publications on the regulation of NOXP20 expression, no further conclusions can be drawn from that. So far, only two publications describe how miR-574-5p expression is regulated. There is evidence that NFκB transcription factor p65 could regulate its transcription in mice in a context of neurological disorders [200]. Furthermore, it has been observed that the amyloid precursor protein APP influences the miR-574-5p level in the development of the cerebral cortex, although by an unknown mechanism [201]. Regulation of not the expression but rather the functionality of miR-574-5p is described more closely in literature. There are reports of three different lncRNAs to regulate miR-574-5p. While lncRNA-MFI2-AS1 (melanotransferrin) influences miR-574-5p in colon cancer cells [202], in breast cancer it is lnc-Zinc Finger Protein 469 (ZNF469)-3 which binds to miR-574-5p [203]. In papillary thyroid carcinoma cells, lnc-PTCSC3 (Papillary Thyroid Carcinoma Susceptibility Candidate 3) is described to have an impact by binding to miR-574-5p [204]. In this case, the interaction is further associated with Wingless-Type MMTV Integration Site Family Member 1 (Wnt) signaling. MiR-574-5p regulates β-catenin/Wnt-signaling via suppressor of cancer cell invasion (SCAI) and thereby influenced invasion and migration of the tumor cells [204]. Indeed, the relationship between miR-574-5p and Wnt signaling is described in several other studies. In colon and thyroid cancer, Wnt signaling was found to be activated by inhibition of Introduction __________________________________________________________________________ 26 the RBP quaking (Qki) [205] [206]. In fact, miR-574-5p targets Qki-6/7 which results in a reduced protein level [205]. In turn, Qki no longer acts as suppressor of β-catenin and the Wnt signaling pathway is activated [207]. Both leads to further progression of tumor growth by facilitating proliferation and inhibiting apoptosis of the colon cancer cells. In contrast, miR-574-5p is also described to act anti-tumorigenic in two other colorectal cancer studies by targeting metastasis-associated in colon cancer protein 1 (MACC1) [208] or MYC Binding Protein (MYCBP) [202]. On the other hand, the influence of miR-574-5p on lung cancer development seems to be unambiguous. Several publications showed that miR-574-5p acts pro-metastatic and enhances tumor progression of (non-) small cell lung cancer ((N)SCLC) by targeting among others checkpoint suppressor 1 (CHES1) and protein tyrosine phosphatase receptor type U (PTPRU) [209] [210] [211]. Additionally, miR-574-5p also seems to promote cell growth in the context of coronary artery disease by binding to the mRNA of zinc finger DHHC-type containing 14 (ZDHHC14) [212]. Finally, miR-574-5p is described to repress the expression of ceramide synthase 1 (CerS1) together with HDAC1 [213], while it can also act in concert with miR-361-5p in white adipose tissue to regulate early B cell factor 1 (EBF1) [214]. Figure 7: Canonical targets regulated by miR-574-5p. miR-574-5p has been described to directly target a variety of mRNAs. Thereby, it influences various types of cancers, as well as cardiovascular and neurological conditions. miR-574-5p binds the mRNAs of the respective proteins, and downregulates the expression. Based on [200] [202] [204] [205] [206] [208] [209] [210] [211] [212] [213] [214]. Introduction __________________________________________________________________________ 27 It gets clear that miR-574-5p plays a role in a variety of diseases and cellular processes by direct interaction with various targets (see Figure 7). Besides the direct base pairing with a mRNA, miR-574-5p was recently described to interact with the RBP CUGBP1 [196]. By this mechanism, miR-574-5p has an impact on lipid metabolism and tumor progression which will be discussed in the next two chapters. 1.2 mPGES-1-derived PGE2 in cancer development In 2011, alteration in lipid metabolites was announced a hallmark of cancer [215]. Metabolic reprogramming of lipid mediators obviously also includes prostanoids such as prostaglandin E2 (PGE2). The metabolite PGE2 is a bioactive lipid mediator which shows great importance in the regulation of a variety of physiological and pathophysiological processes, such as inflammation, pain and tumorigenesis [216] [217] [218] [219]. It is generated in a first step by the enzymes cyclooxygenase (COX)-1 or COX-2 by converting arachidonic acid to PGH2.This instable intermediate is then further converted by microsomal prostaglandin E2 synthase 1 (mPGES-1) [220]. The generated PGE2 is secreted by the cell and elicits a wide range of pro- tumorigenic functions by binding to the EP1-4 receptors. High levels of mPGES-1 could be observed in various types of cancers including colon [221], prostate [222] and lung cancer [223]. Increased levels of COX-2 as well as mPGES-1 are further associated with a poor overall survival rate [224]. MPGES-1 activity does not only have an impact on pain and inflammation in the tumor microenvironment but also enhances the progression of the tumor itself. Moreover, PGE2 is responsible for the crosstalk of cancer and stromal cells within the tumor tissue. The result is a highly efficient immune evasion, which facilitates further tumor growth [219]. One example is cervical cancer-derived PGE2 which was observed to induce monocyte differentiation into tumor-associated macrophages (TAMs) [225]. Furthermore, PGE2 not only suppresses an anti-tumorigenic immune response but also facilitates tumor growth directly [226]. In the case of breast cancer, PGE2 leads to lymph angiogenesis while the EP4 receptor seems to be the most important one in that context [227]. Also in lung cancer, EP4 is the crucial receptor, leading to enhanced tyrosine kinase c-Src activation and subsequently to an increased tumor growth as well as metastasis [228]. Overall, there are numerous publications describing the influence of PGE2 on tumor progression (for reviews see [218] [229] [230] [231]). Therefore, it is no surprise that the pro- tumorigenic properties of PGE2 attracted great attention to potential pharmacological inhibition of its synthesis. However, current approved medication to interfere with the negative effects of PGE2 comprise only inhibitors of the COX enzymes. Especially in long time treatment, these therapeutic approaches show severe side effects like increased cardiovascular events or gastrointestinal bleeding [232] [233] [234] [235] [236]. With inhibition of the COXs, other Introduction __________________________________________________________________________ 28 prostanoids like PGD2, prostacyclin (PGI2) and thromboxane (TXA) are also impaired (see Figure 08). For this reason, it would be much more reasonable to target mPGES-1 rather than COX-2 [237]. Inhibition of mPGES-1 would minimize side effects [238] and hypothetically also cause a shift towards PGI2 and PGD2, which would be beneficial for the cardiovascular system. [239] [240]. Several studies have shown that genetic deletion or pharmacological inhibition of mPGES-1 is indeed a promising tool against tumor growth [222]. Figure 8: Prostanoid biosynthesis. Arachidonic acid is converted by either COX-1 or COX-2. The resulting instable intermediate PGH2 is then further processed by respective synthases to generate the prostanoids PGE2, PGI2, TXA2, PGD2 or PGF2a. NSAIDs inhibit COX enzymes and therefore all prostanoids downstream of PGH2. Therefore, inhibition of only PGE synthase (mPGES-1) by CUGBP1/miR-574 decoy would be more beneficial and should not affect other prostanoids. Modified from [241]. However, most of the developed mPGES-1 inhibitors have a problematic limitation: they do not work in mouse or rat models, since mPGES-1 is not conserved in rodents [242]. As murine and human mPGES-1 differ in three amino acids near the active site of the enzyme [243], the majority of human inhibitors does not repress the murine enzyme activity. Therefore, any kind of pre-clinical study is impossible, due to a lack of established animal models. An exception is Compound III which inhibits both human and murine mPGES-1 [244] [245] [246] [247] and was already shown in pre-clinical studies to be an efficient tool against neuroblastoma [246]. Introduction __________________________________________________________________________ 29 Besides pharmacological inhibition of mPGES-1, recently a post-transcriptional regulation mechanism was unravelled which will be described more closely in the next chapter. 1.2.1 Regulation of mPGES-1 by the miR-574-5p/CUGBP1 decoy mechanism in human lung cancer Recently, the PGE2-generating synthase mPGES-1 was found to be regulated in a non- canonical way [196]. Until then, it was not much known about post-transcriptional regulation of mPGES-1. In its 3’UTR there are two long GREs which represent binding sites for the RBP CUGBP1. A similar sequence can be found in miR-574-5p. In an inflammatory environment like stimulation with IL-1β, miR-574-5p is able to sequester CUGBP1 away from the mPGES-1 mRNA (see Figure 9). This eliminates the negative influence of the RBP and additionally results in an AS event. The mPGES-1 3’UTR is spliced which removes a conserved ALU element in between the two GREs. The shorter splice variant has a higher translational rate. Therefore, the decoy has an overall enhancing effect on mPGES-1 gene expression [196]. Figure 9: Regulation of mPGES-1 gene expression via the miR-574-5p/CUGBP1 decoy mechanism. CUGBP1 binds to two GREs within the mPGES-1 3’UTR. Upon IL-1β simulation, miR-574-5p acts as decoy to CUGBP1, preventing it from binding to the GREs. This results in an AS event creating a shorter 3’UTR isoform with a higher translational rate. As a result, mPGES-1 levels and PGE2 synthesis are increased as well as subsequent tumor growth in vivo. Modified from [196]. Introduction __________________________________________________________________________ 30 The increased mPGES-1 protein level then leads to a higher level of its enzymatic product PGE2. As described above, PGE2 has a crucial influence on the progression of cancer. In a xenograft mouse model, it could be demonstrated that this decoy mechanism has a tremendous impact on lung tumor growth. When miR-574-5p overexpressing lung cancer cells were injected into nude mice hind flanks, it was revealed that they displayed a strongly increased proliferation. The tumor weight and volume was significantly increased compared to control tumors. Moreover, urinary PGE-M levels linked this to enhance PGE2 formation. Interestingly, progression of miR-574-5p overexpressing tumors was reduced back to control level with the simultaneous administration of the mPGES-1 inhibitor Compound III. This proved that the pro-tumorigenic effects of miR-574-5p were solely caused by the decoy-mediated mPGES-1 induction [196]. This conclusion is consequential because a delayed growth of murine xenograft tumors was already observed upon mPGES-1 knockdown in an earlier study [222]. Hence, this new non-canonical mPGES-1 regulation mechanism is an intriguing research topic in the context of future lung cancer research. 1.3 Aim of the study With the discovery of the new decoy mechanism in human lung cancer, it would be possible for the first time to regulate mPGES-1 expression on mRNA level. In order to further characterize the miR-574-5p/CUGBP1 decoy in lung cancer, a TMT-based proteomics study was used to unravel the overall impact on cellular protein levels. It should be revealed if the miR-574-5p/CUGBP1 decoy has a global impact similar to the miR-328/hNRNP E2 decoy or if mPGES-1 could be the only target. In that case, this would open up new options for NSCLC patients. Not all patients benefit from a treatment with medication that aims to reduce PGE2 levels [248] [249]. Potentially, because not all lung adenocarcinomas are comparably PGE2- dependent. Therefore, levels of miR-574-5p could be used as stratification marker in order to identify those patients with higher mPGES-1 and PGE2 levels. For this subgroup, a treatment with COX inhibitors could be highly beneficial in the fight against NSCLC. Materials and Methods __________________________________________________________________________ 31 2. Materials and Methods 2.1 Cell culture methods 2.1.1 Cell culture conditions The human cell line A549 (ATCC Manassas, VA, USA) is derived from a 58-year old male with lung adenocarcinoma. Cells were cultured in Dulbecco’s modified Eagle medium (DMEM, Life technologies) supplemented with 10% (v/v) fetal calf serum (FCS; Life technologies), 100 U/mL penicillin (PAA the Cell Culture Company) 100 µg/mL streptomycin (PAA the Cell Culture Company) and 1 mM sodium pyruvate (PAA the Cell Culture Company) (= fully complemented medium). Cells were grown in T75 cell culture flasks under standard growth conditions (humidified atmosphere of 5% CO2 at 37°C). When the cells reached a confluence of ~ 70-90%, medium was aspirated and cells were washed with pre-warmed phosphate buffered saline (PBS). Then, they were detached using pre-warmed Trypsin-EDTA (Invitrogen) at 37°C for 5 min. Reaction was stopped adding pre-warmed full culture medium (1:1) to the cells and number of viable cells was examined by trypan blue staining and counted using Bio- Rad TC10 automated cell counter (both Bio-Rad Laboratories). Approximately 1 million cells were transferred into a new T75 culture flask. For all experiments, cells were seeded in 6-well plates à 5x105 cells per well in 2 mL medium. Except for RIP assays, where 3 x 106 A549 cells were seeded in 10-cm dishes in 10 mL medium. For preparation of liquid nitrogen stocks, A549 cells were detached as described above and resuspended in medium containing 10% (v/v) DMSO (Carl Roth). The suspension was transferred into cryo vials (VWR) and stored at -80°C for 2 days. Afterwards, cryo vials were transferred into a liquid nitrogen tank until further use. In order to thaw cells again, the cryo vials were carefully pre-warmed at room temperature until suspension began to thaw. Then, pre warmed medium was added and the cryo vial was rinsed until the whole suspension was thawed and transferred in a 50 mL reaction tube. Cells were precipitated in a centrifuge for 5 min at 1,200 rpm (Eppendorf Centrifuge 5702), to remove DMSO. Supernatant was discarded, the cell pellet was resuspended in fresh pre-warmed medium and transferred in a T75 cell culture flask. 2.1.2 Depletion of CUGBP1 using RNA interference By using siRNA oligonucleotides, CUGBP1 was transiently knocked down. Therefore, a previously published siRNA (5´-GCUGUUUAUUGGUAUGAUU-3´) was used. 24 h prior to transfection, A549 cells were seeded in a 6-well plate as described above. For transfection, 20 pmol/µL siRNA oligonucleotides were transfected using Lipofectamin2000® (Invitrogen) according to manufacturer’s instructions. A siRNA against GFP, naturally not expressed, was Materials and Methods __________________________________________________________________________ 32 designed (5´-UCUCUCACAACGGGCAUUU-3´) and used as negative control. After 24 h, cells were stimulated with 5 ng/mL interleukin (IL)-1β (Sigma-Aldrich). Further 24 h later, the samples were harvested using 500 µL pre-warmed Trypsin-EDTA (Invitrogen) as described above. Transfection efficiency was tested by Western blot analysis (see chapter 2.3.3 SDS- PAGE and Western Blot). 2.1.3 Overexpression of miR-574-5p For transient overexpression (oe) of miR-574-5p, the miRIDIAN hsa-miR-574-5p mimic (HMI0794, Sigma-Aldrich) and negative control (HMC0002, Sigma-Aldrich) were used. A549 cells were seeded 24 h prior to transfection in a 6-well plate. 20 pmol/µL per well of the mimics or control were transfected using Lipofectamin2000® (Invitrogen) according to the manufacturer´s instructions. After 24 h, cells were stimulated with 5 ng/mL IL-1β (Sigma- Aldrich). Further 24 h later, the samples were harvested using 500 µL pre-warmed Trypsin- EDTA (Invitrogen) as described above. The efficiency was assessed by qRT-PCR analysis (see chapter 2.2.2 mRNA or miR quantification by qRT-PCR) as stated in [196]. For stable overexpression of miR-574-5p, the lentiviral particles Mission® lenti miR-574-5p (HLMIR0794, Sigma-Aldrich) or Mission® lenti control (NCLMIR001, Sigma-Aldrich) were used. A549 cells were seeded at a density of 5 x 105 per well in a 6-well plate 24 h prior to transduction. Lenti viral particles were rapidly thawn and added to the cells at a MOI of 0.83 for spinoculation (875 x g, at 32°C for 60 min). Transduced cells were incubated for 24 h in fully complemented DMEM before 10 µg/mL puromycin (Sigma-Aldrich) were added for four days to select the transduced clones. These stable A549 miR-574-5p overexpression and control cell lines were generously provided by Stefan Stein, Georg-Speyer Haus, Frankfurt [196]. The transduction efficiency was verified by qRT-PCR (see chapter 2.2.2 mRNA or miR quantification by qRT-PCR). 2.1.4 Depletion of miR-574-5p by LNA™ inhibitors Transient depletion of miR-574-5p was achieved using LNAs™ from Exiqon (miR-574-5p- LNA™ inhibitor and negative control MIMAT0004795). A549 were seeded 24 h prior to transfection at a density of 5 x 105 cells in a 6-well plate. 40 pmol/µL per well was transfected using Lipofectamin2000® (Invitrogen) according to manufacturer’s instructions. After 24 h, cells were stimulated with 5 ng/mL IL-1β (Sigma-Aldrich). Further 24 h later, the samples were harvested using 500 µL pre-warmed Trypsin-EDTA (Invitrogen) as described above. Efficiency of the knockdown was measured by qRT-PCR analysis (see chapter 2.2.2 mRNA or miR quantification by qRT-PCR) as stated in [196]. Materials and Methods __________________________________________________________________________ 33 2.1.5 Wound healing assay For determination of migratory behavior of A549 cells, wound healing assays were performed with stable A549 miR-574-5p overexpression and control cells. Therefore, cells were seeded in 6-well plates as described above. In order to minimize proliferation, cells were pre-starved for 24h in Opti-MEM (Life technologies). Each condition was assessed in duplicates. Scratching was performed in the middle of the well using a 10 µL pipette tip. Remaining cell debris was washed away with pre-warmed PBS, before reduced culture medium was applied (only containing 2% FCS). Images were taken immediately after scratching at time point t0 and after 24 h as described in chapter 2.6.2 Wound healing assay images. 2.1.6 Trans-well migration assay Trans-well assay, also called Boyden chamber assay, was performed with stable A549 miR-574-5p overexpression and control cells. Cultured cells were detached as described above and sedimented for 5 min at 1,200 rpm (Eppendorf Centrifuge 5702). The cell pellet was then resuspended in serum-free culture medium without any FCS. 5 x 104 cells were seeded in a volume of 100 µL in a 24-well cell culture insert (Corning, Cat. No. 353097). Those inserts have a membrane on the bottom with 8 µm pores to enable migration of A549 cells. The inserts were placed in a 24-well plate and cells were allowed to adhere for 20 min at room temperature. Subsequently, the bottom of the wells was filled with 700 µL of full complemented culture medium to encourage migratory behavior (see Figure 10). As negative control, one well was filled with serum-free medium which restrains migration. Samples were cultured for 5 h at 37°C. Then, the inserts were washed in PBS. Non-migrated cells that were still on the inside of the insert were removed by swiping. Migrated cells on the bottom of the insert were fixed for 3 min at room temperature by placing the inserts in new wells filled with 700 µL of methanol (VWR). Afterwards, cells were stained with 0.5% crystal violet (Carl Roth) for 10 min at room temperature. Residual dye was remove by washing the inserts with autoclaved Millipore water (MQ) followed by an additional swiping step to remove cells from the inside of the insert. The membranes were then cut out using a scalpel and were mounted on cover slides with Pertex® (VWR). Number of violet migrated cells was counted under a light microscope using a manual cell counter. The protocol was kindly provided by Dr. Kati Turkowski and PD Dr. Rajkumar Savai, Max-Planck-Institute for Heart and Lung Research, Bad Nauheim. Materials and Methods __________________________________________________________________________ 34 Figure 10: Boyden chamber set-up. Cells are seeded in a cell culture insert with a membranous bottom (pore size 8 µm) and migrate towards an increased FCS concentration. After 5 h, migrated cells on the bottom of the membrane can be fixed, stained and counted under a light microscope. 2.2 RNA methods 2.2.1 RNA extraction Total RNA was extracted using TRIzol Reagent (Invitrogen) performing a standard phenol chloroform protocol. In short, cell pellets were resuspended in an appropriate amount of TRIzol and incubated for 5 min. Then, 200 µL Chloroform (Carl Roth) were added and samples were thoroughly mixed before incubation on ice for 15 min. After centrifugation for 15 min at 4°C with 17,000 x g, the upper aqueous phase containing the RNA was transferred to a fresh tube and 500 µL isopropanol (VWR), 5 µL 3M sodium acetate (pH 6.5) (Carl Roth) and 1 µL GlycoBlue™ (Thermo Fisher Scientific) were added and mixed. Samples were incubated further 15 min on ice before another centrifugation step was performed. The precipitated RNA pellet was then dissolved in an appropriate amount of MQ. The RNA was then treated with Turbo DNase (Ambion) for 5 min according to the manufacturer’s instructions to remove residual DNA. DNase digested RNA was incubated for 30 min at -80°C together with 100 µL ethanol (VWR), 2 µL 3 M sodium acetate (pH 6.5) and 1 µL Glyco blue™. RNA was then precipitated for 30 min at 17,000 x g at 4°C. The pellet was washed with 70% ice-cold ethanol by rinsing the pellet and a subsequent centrifugation for 5 min at 17,000 x g at 4°C. The RNA pellet was air dried for 5 min at room temperature and resuspended in an appropriate amount of MQ. RNA concentration and purity were then measured by UV spectroscopic measurement using the NanoDrop ND-1000 spectrophotometer (Thermo Fischer Scientific). 2.2.2 mRNA or miR quantification by qRT-PCR For mRNA quantification, 1 µg of DNase-treated RNA was used for reverse transcription. Therefore, the High-Capacity RNA-to-cDNA Kit (Applied Biosystems) was used according to manufacturer's instructions. Generally, 10 µL 2x RT buffer and 1 µL RT enzyme were added to a RNA mix in 9 µL. Reverse transcription was conducted in a thermo cycler (Bio-Rad Laboratories) for 1 h at 37°C and 5 min at 95°C. Real-time PCR was then performed with the StepOne Plus™ Real-Time PCR System (Applied Biosystem) using Power Syber Green PCR Master Mix (Applied Biosystems). The PCR program can be found in Table 2. In general, one reaction contained 10 µL 10x Sybr Green Mastermix, 3.75 µL forward Primer (2 µM diluted in Materials and Methods __________________________________________________________________________ 35 MQ), 3.75 µL reverse Primer (2 µM diluted in MQ), 1.5 µL MQ and 1 µL cDNA (1:2 diluted in MQ). Each sample was set up in duplicates. To normalize variations in cDNA quantities throughout the samples, β-Actin was used as housekeeping gene. Fold inductions were calculated using the 2(-∆Ct) method. A list of primers can be found in Table 3. Table 2. PCR program for mRNA quantification Step Temperature Time Repeats Initial denaturation 95°C 20 s x Denaturation 95°C 3 s 40 cycles Annealing & Elongation 60°C 30 s 95°C 15 s x Melt curve 60°C 1 min x 95°C 15 s x Table 3. Primer used for qRT-PCR Target forward primer sequence reverse primer sequence β-Actin CGGGACCTGACTGACTAC CTTCTCCTTAATGTCACGCACG cJun TCG ACA TGG AGT CCC AGG A GGC GAT TCT CTC CAG CTT CC SMAD2 GGGATGCTTCAGGTAGGACA TCTCTTTGCCAGGAATGCTT SMAD3 CGCAGAACGTCAACACCAAG GGCGGCAGTAGATGACATGA COX-2 CCGGGTACAATCGCACTTAT GGCGCTCAGCCATACAG NDUFS2 GTTTTGCCCATCTGGCTGGT CATGCCATGGCCTATGGTGAA mPGES-1 GAAGAAGGCCTTTGCCAAC CCAGGAAAAGGAAGGGGTAG UBE2R2 ATGTGGCACCCCAACATT TCCACCTTTCAGAAGGCAGT CEP41 ACAGAACCCAAGATACCAGCATAT GGGAGCTGGTAAGATACACACA SLC39A6 GCACTTACTGCTGGCTTATTCA CGGCTACATCCATGGTCACT PAIP2 CCATTTGCAGAGTACATGTGGA CCGTACTTCACCCCAGGAAC GTF2E2 CCATGCAGGAATCTGGACCA AATCCTTCAGCACTCCAGCC LEO1 ACTGCCCAACTTTCTCAGTGT AGATGATTGTGGTCGCCCTG For miR quantification, the Qiagen miScript system was used according to manufacturer’s instructions. One reverse transcription reaction comprised of 4 µL 5x miScript HiSpec Buffer, 2 µL 10x miScript Nucleic Acid Mix, 2 µL reverse transcriptase and 1 µg DNase digested RNA in MQ ad 20 µL. The reverse transcription was performed for 1 h at 37°C and 5 min at 95°C using the thermo cycler (Peqlab Biotechnologie GmbH, advanced primus 25). CDNA was diluted 1:2 in MQ afterwards. Following, real time PCR was performed using either the miR- 574-5p specific primer (MS00043617, Qiagen) or miR-16-5p (MS0031493, Qiagen). Real-time PCR was performed according to the manufacturer’s instructions (see Table 4). Generally, a mix for one reaction contained 12.5 µL QuantiTect Syber Green Mastermix, 2.5 µL 10x miScript Universal primer, 2.5 µL specific primer, 1 µL miScript cDNA and 6.5 µL MQ. Fold inductions were calculated as described above. Materials and Methods __________________________________________________________________________ 36 Table 4. PCR program for miR quantification Step Temperature Time Repeats Initial denaturation 94°C 15 s x Denaturation 94°C 15 s 40 cycles Annealing 55°C 30 s Elongation 70°C 30 s 94°C 15 s x Melt curve 60°C 1 min x 95°C 15 s x 2.2.3 RNA immunoprecipitation (RIP) The RIP protocol was kindly provided by Prof. Michaela Müller-McNicoll, Goethe University Frankfurt. In order to precipitate CUGBP1, the GammaBind Plus Sepharose beads (GE Healthcare) were used. In preparation, the beads were blocked with blocking buffer containing 0.2 mg/mL bovine serum albumine (BSA) in PBS (Sigma-Aldrich) and 0.1 mg/mL yeast tRNA for 90 min at 4°C. Then, beads were washed with PBS and stored at 4°C until further use. A549 cells were seeded at a density of 3 x 106 in a 10-cm dish and incubated overnight. In case of stimulation, 5 ng/mL IL-1β were added for further 24 h. In order to harvest the cells, they were washed with 5 mL of ice-cold PBS and scraped in 5 mL PBS complemented with protease inhibitor EDTA-free (Roche). Cells were spinned down for 5 min and 400 x g at 4°C and resuspended in 1 mL lysis buffer (see Table 5). The suspension was incubated 10 min on ice and then sonicated 4 times for 10 seconds on 30% amplitude, with 20 sec pause (Branson Sonifier 250). Then, cell debris was spinned down for 10 min at 10,000 x g at 4°C. The supernatant was transferred into a fresh tube and 10% were taken as input sample. Before usage, the blocked beads were washed 3 times with lysis buffer and were centrifuged at 300 x g for 5 min. Beads and antibodies were linked by mixing 50 µL bead suspension with 10 µg of CUGBP1 antibody (05-621 clone3B1, Merck) or normal mouse IgG antibody (12-371, Merck) followed by incubation for 30-60 min at 4°C. Afterwards, immunoprecipitation (IP) was conducted by dividing the lysate equally to the CUGBP1- /IgG-bead mixture and incubating for 2 h at 4°C. Then, samples were washed with each wash buffer B1-B3 (composition see Table 5) for 5 min in the cold room, with centrifugation steps of 5 min and 300 x g in between. After the last washing step, 10% of each precipitate was taken for Western blot analysis in order to validate the immunoprecipitation (see chapter 2.3.3 SDS-PAGE and Western Blot). The remaining precipitates were resuspended in 500 µL TRIzol reagent (Invitrogen) and RNA was isolated as described above (see chapter 2.2.1 RNA extraction). Thereby, it was important to take the exact same volume of aqueous phase from each sample, since the complete isolated RNA was then used for reverse transcription. Materials and Methods __________________________________________________________________________ 37 Since there was no housekeeping gene for analysis of the real time PCR, data analysis was conducted without building a difference to a Ct of a housekeeping gene. After calculating the 2(-Ct), the Input value was multiplied by 10 to even out that it was 10% of the total cell lysate, while IgG-IP and CUGBP1-IP values were multiplied by 1.11 since 10% were taken for Western blot analysis and 90% were left. Then, a x-fold was calculated to obtain the RNA enrichment and the yield. The yield describes the amount of precipitated RNA in percent compared to the input, while the enrichment shows the specificity of CUGBP1-IP in comparison to IgG-IP. In this thesis, the graphs will depict the enrichment, as it gives the information if a certain RNA is indeed bound by CUGBP1. 𝑦𝑖𝑒𝑙𝑑 = 2−𝐶𝑇 𝑜𝑓 𝐼𝑃 × 1.11 2−𝐶𝑇 𝑜𝑓 10%𝐼𝑛𝑝𝑢𝑡 × 10 𝑒𝑛𝑟𝑖𝑐ℎ𝑚𝑒𝑛𝑡 = 2−𝐶𝑡 𝑜𝑓 𝐶𝑈𝐺𝐵𝑃1 × 1.11 2−𝐶𝑇 𝑜𝑓 𝐼𝑔𝐺 × 1.11 Table 5. RIP buffer composition Buffer End concentration Reagent Hypotonic lysis buffer 10 mM Tris-HCl pH 7.5 (Sigma-Aldrich) 10 mM KCl (Sigma-Aldrich) 1.5 mM MgCl2 (Sigma-Aldrich) 0.5 mM Ll fac (Carl Roth) 0.9% NP-40 (Igepal) (Sigma-Aldrich) 1x Protease inhibitor cocktail (Roche) 40 U/µL Ribonuclease Inhibitor Ad 10 mL MQ Wash buffer B1 20 mM Tris-HCl pH 7.5 (Sigma-Aldrich) 150 mM NaCl (Sigma-Aldrich) 2 mM EDTA (Roche) 0.1% SDS (Carl Roth) 1% Triton X-100 (Carl Roth) 1x Protease inhibitor cocktail (Roche) Ad 10 mL MQ Wash buffer B2 20 mM Tris-HCl pH 7.5 (Sigma-Aldrich) 500 mM NaCl (Sigma-Aldrich) 2 mM EDTA (Roche) 0.1% SDS (Carl Roth) 1% Triton X-100 (Carl Roth) 1x Protease inhibitor cocktail (Roche) Ad 10 mL MQ Wash buffer B3 10 mM Tris-HCl pH 7.5 (Sigma-Aldrich) 250 mM LiCl (Sigma-Aldrich) 1 mM EDTA (Roche) 1% Na Deoxycholate (Sigma-Aldrich) 1% NP-40 (Igepal) (Sigma-Aldrich) 1x Protease inhibitor cocktail (Roche) Ad 10 mL MQ Materials and Methods __________________________________________________________________________ 38 2.3 Protein methods 2.3.1 Soluble and microsomal fraction preparation As described in [250] [195], cell pellets of A549 cells were resuspended in 1 mL homogenization buffer (0.1 M potassium phosphate (Carl Roth) pH 7.4, 0.25 M sucrose (Scharlau), and EDTA-free protease inhibitor (Roche)). The suspension was then sonicated 4 x 10 seconds on ice with 20 seconds pause (Branson Sonifier 250). To remove all debris, samples were centrifuged with 5,000 x g at 4°C for 10 min and the supernatant was transferred in ultracentrifugation tubes. Then, samples were centrifuged at 100,000 x g at 4°C for 1 h (using Beckmann Optima XL-100K). The resulting supernatants contained the soluble fractions and were stored at -80°C while, the pellets were mixed with 500 µL 2.5 M NaBr (Sigma-Aldrich) and incubated for 45 min on ice with shaking. Another centrifugation step was performed at 4°C for 1 h at 100 000 x g. The supernatant containing membrane associated fraction was discarded, while the pellets resembled the microsomal protein fraction. They were resuspended in PBS supplemented with EDTA-free protease inhibitor (Roche) and sonicated on ice to improve dissolving of the proteins. All samples were stored at -80°C until further use. Concentration for Western blot analysis was measured using Bradford assay (Bio-Rad Laboratories) (see next chapter) while for proteomics samples the protein amount was determined by Pierce BCA Protein Assay (Thermo Fisher Scientific) following manufacturing instructions. 2.3.2 Determination of protein concentration Concentration of the protein solution was determined by Bradford assay (Bio-Rad Laboratories) according to manufacturer's instructions. A standard curve with bovine serum albumin (BSA) concentrations of 50-500 µg/mL was used. 10 μL of the different BSA solutions or protein samples (diluted 1:20 in MQ) were mixed in a 96-well plate and 190 μL of Bradford reagent (diluted 1:5 in MQ) was added. Each sample and standards were measured in duplicates. The absorption at a wavelength of 595 nm was measured at a Tecan Infinte M 200 (Tecan Group). The protein concentration was calculated according to the standard curve. 2.3.3 SDS-PAGE and Western Blot Proteins were separated by Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS- PAGE). Depending on the approach, either 80 µg of soluble/microsomal proteins or for RIP samples 20 µL were mixed with 5 µL 4x protein loading buffer containing 50% glycerine, 1.5% bromphenol blue, 4% SDS, 15% β-Mercaptoethanol (all Carl Roth). Samples were then boiled for 5 min at 95°C in order to denature proteins and loaded on a 12% SDS-Gel (composition see Table 6). As a marker the Precision Plus Protein™ All Blue Standard (Bio-Rad Materials and Methods __________________________________________________________________________ 39 Laboratories) was also applied and the gel ran for ca. 1 h at 120 V. Afterwards, proteins were transferred to a HyBond ECL nitrocellulose membrane (Amersham) with 230 mA for 80 min. Subsequently, membranes were blocked with Odyssey blocking buffer (LI-COR® Bioscience) for 1 h at room temperature. Then, they were incubated over night at 4°C with primary antibodies (see Table 7). The next day, membranes were washed 3 times with PBS pH 7.4 complemented with Tween20 0.1% (v/v) (Carl Roth) (PBS-T) to remove residual unbound antibody. Then an infrared dye conjugated secondary antibody (IRDye®, LI-COR® Bioscience) directed against the certain host animal, was incubated on the membranes for 45 min at room temperature. Membranes were washed three times with PBS-T before visualization. Detection and quantification were performed using the Odyssey Infrared Imaging System (LI-COR® Biosciences) and the Image Studio Software. Table 6. Gel composition for SDS-PAGE Stacking gel (7.4 %) Separating gel (12%) MQ 1.2 mL MQ 1.6 mL Acrylamide 30% (w/v) 266 µL Acrylamide 30% (w/v) 2 mL Tris-HCl, 0.5 M, pH 6.8 500 µL Tris-HCl, 1.5 M, pH 8.8 1.3 mL SDS 10% (w/v) 20 µL SDS 10% (w/v) 50 µL APS 10% (w/v) 12 µL APS 10% (w/v) 50 µL TEMED 3 µL TEMED 4 µL Table 7. Primary antibodies for Western blot analysis Target Host Supplier (order no.) β-Actin goat Santa cruz (sc-1616) CUGBP1 mouse Abcam (ab9549) mPGES-1 rabbit Cayman (cay160140) NDUFS2 rabbit Abcam (ab96160) SMAD2 goat Santa cruz (sc-6200) SMAD3 rabbit Abcam (ab28379) SMAD4 mouse Abcam (ab3219) P38 goat Santa cruz (sc-535-g) CUGBP1 (RIP-Western blot) rabbit Abcam (ab129115) 2.3.4 TMT labelling and mass spectrometry Soluble or microsomal proteins (see chapter 2.3.1 Soluble and microsomal fraction preparation) were solubilized in 50 μl buffer containing 0.05 M triethylammonium bicarbonate, 4 M Urea, 0.01 % SDS and 1 mg RapiGest SF Surfactant (Waters). From each sample 50 µg were taken for further preparation. Disulfide reduction was conducted for 30 min at 56°C by adding 5 μl 1 M DTT. Afterwards, sulfhydryl alkylation was performed by adding 4 μl 1 M iodoacetamide solution, while samples were incubated at room temperature for 1 h in the dark. Materials and Methods __________________________________________________________________________ 40 Trypsin (modified sequencing grade, Promega) was added in a ratio of 1:30 (trypsin: protein). Then, samples were incubated at 37°C overnight. By using tandem mass tags (TMT 6-plex) according to manufacturer's instructions (ThermoFisher Scientific), peptides were labelled. Using an SCX-cartridge (Phenomenex), excess reagents could be removed from the samples. Liquid chromatography tandem mass spectrometry (MS) of a TMT-labeled sample was performed on QExactive mass spectrometer (ThermoFisher Scientific). Peptide pre-fractionation was conducted as previously described in [251]. TMT-labeled protein samples were separated over a 60-minute gradient (3-55 % B) on a 2.1 × 250 mm XBridge BEH300 C18 column (Waters) using a flow rate of 200 µL/min. Buffers A contained 20 mM ammonia in MQ, whereas buffer B contained 20 mM ammonia in 80 % acetonitrile. The specific fractions were collected every minute and the fractions covering the peptide elution range were concatenated to yield 12 final pooled fractions. These fractions were evaporated to dryness by vacuum drying and stored at -20°C until nano Liquid chromatography-mass spectrometry (LC- MS) data capture. A Q-Exactive mass spectrometer (Thermo Scientific) was used to performe online LC-MS measurements. Peptide samples were trapped on an Acclaim PepMap trap column (C18, 3 µm, 100Å, 75 µm x 20 mm). Separation took place on a 15-cm long C18 picofrit column with 100 μm internal diameter and 5 μm bead size (Nikkyo Technos) which was installed onto a nano-electrospray ionization source. Solvent A was 97% water, 3% acetonitrile, 0.1% formic acid; and solvent B was 5% water, 95% acetonitrile, 0.1% formic acid. At a constant flow of 0.25 μl/min, the curved gradient went from 3% solvent B up to 48% solvent B in 50 min. Fourier transform mass analyzers (FTMS) master scans with 70,000 resolutions (and mass range 400-1200 m/z) were followed by data-dependent MS/MS (17,500 resolution) on the top 10 ions using higher energy collision dissociation (HCD) at 31% normalized collision energy. Precursors were isolated with a 2 m/z window. Automatic gain control (AGC) targets were 3e6 for MS1 and 2e5 for MS2. Maximum injection times were 250 ms for MS1 and 200 ms for MS2. Dynamic exclusion was used with 20 s duration. Precursors were excluded when they showed unassigned charge state or charge state of 1. An underfill ratio of 1% was used. 2.4. Bioinformatical methods 2.4.1 3’UTR analysis For the analysis of splice patterns, proteins that were upregulated at least 1.5-fold in response to ΔCUGBP1 in the mass spectrometry dataset were taken into account. This list of 399 proteins was then analyzed concerning their 3’UTRs. All described 3’UTR isoform sequences were downloaded from ensemble biomart (Human, GRCh38.p12 Ensembl variation resources [252]), resulting in a list of 1916 transcripts. Those sequences were then aligned with 42 known Materials and Methods __________________________________________________________________________ 41 binding motifs of CUGBP1 downloaded from the online tool Splice Aid F [109] [104]. Alignment was kindly conducted by Tobias Saul. For the high stringency analysis, three criteria were applied: (I) the binding sites should be of 39 or 46 nt length, (II) there should at least be 2 binding sites and (III) those binding sites should span a potential intron of at least 1000 nt. For a second less stringent approach (referred to as low stringency analysis), the three criteria were mitigated: (I) the binding sites should at least be of 8 nt length, (II) there should be 2 or more binding sites and (III) those binding sites should span a potential intron of minimum 100 nt. Analysis was performed with Microsoft Excel 2016. 2.4.2 Mass Spectrometry data analysis Acquired MS raw files were searched using Sequest-Percolator under the software platform Proteome Discoverer 1.4.1.14 (Thermo Fisher Scientific) against human Uniprot database (release 01.12.2015 [89]) and filtered to a 1% false discovery rate (FDR) cut off. A precursor ion mass tolerance of 10 ppm was used as well as product ion mass tolerances of 0.02 Da for HCD-FTMS and 0.8 Da for collision induced dissociation Ion Trap Mobility Spectroscopy (CID- ITMS). The algorithm considered tryptic peptides with maximum 2 missed cleavages; carbamidomethylation (C), TMT 6-plex (K, N-term) as fixed modifications and oxidation (M) as dynamic modifications. Quantification of reporter ions was done by Proteome Discoverer on HCD-FTMS tandem mass spectra using an integration window tolerance of 10 ppm. Only unique peptides in the data set were used for quantification. Fold values were calculated comparing proteins from ΔCUGBP1 to Scramble, ΔmiR-574-5p to negative control LNA and miR-574-5p oe to negative control mimic. Fold values of +1.5/-1.5 were considered up- or downregulated. 2.4.3 Ingenuity pathway analysis (IPA) Most genes are regulated by a variety of upstream regulators or transcription factors with often opposing effects. As it is unknown which will dominate in this specific system, predictions become difficult. Therefore, a statistical approach was used. A quantity “z‐score” was calculated that rates whether