Differential phosphoproteomics for the study of olfactory ...

Differential Phosphoproteomics for the

Study of Olfactory Receptor-mediated

Signaling Processes

Dissertation zur Erlangung des Grades

eines Doktors der Naturwissenschaften

der Fakultät für Biologie und Biotechnologie

an der Internationalen Graduiertenschule Biowissenschaften

der Ruhr-Universität Bochum

angefertigt im

Medizinischen Proteom-Center

in der Arbeitsgruppe

Cellular Proteomics

vorgelegt von

Heike Piechura

aus

Bochum

Bochum

Oktober 2010

Differentielle Phosphoproteomics zur Studie

von Geruchsrezeptor-vermittelten

Signalwegen

Dissertation zur Erlangung des Grades

eines Doktors der Naturwissenschaften

der Fakultät für Biologie und Biotechnologie

an der Internationalen Graduiertenschule Biowissenschaften

der Ruhr-Universität Bochum

angefertigt im

Medizinischen Proteom-Center

in der Arbeitsgruppe

Cellular Proteomics

vorgelegt von

Heike Piechura

aus

Bochum

Bochum

Oktober 2010

1. Gutachter: Prof. Dr. Bettina Warscheid

2. Gutachter: Prof. Dr. Dr. Dr. Hanns Hatt

Für mein Familie In liebevoller Erinnerung an Oma Lena, Oma Else und Opa Alfred

Danksagung

Promovieren kann man nicht alleine. Deshalb möchte ich mich ganz herzlich bedanken bei:

Meiner Doktormutter Prof. Dr. Warscheid

Für die Vergabe dieser interessanten und fordernden Doktorarbeit. Danke für die Möglichkeiten zu

lernen und mich weiter zu entwickeln, das mir entgegen gebrachte Vertrauen und die vielen

Freiheiten, die ich hatte. Besonders möchte ich mich bedanken für die sorgfältige Korrektur meiner

Arbeit, trotz der widrigen Umstände.

Meinem Zweitgutachter Prof. Dr. Dr. Dr. Hatt

Für die Begutachtung dieser Arbeit, fürs Zeit nehmen und Zuhören, sowie für die gute

Zusammenarbeit mit seinem Lehrstuhl.

Prof. Dr. Meyer

Für das MPC an sich und die exzellenten und einmaligen Arbeitsbedingungen dort.

Prof. Dr. Neuhaus

Für die gute Zusammenarbeit mit ihr und ihrer Arbeitsgruppe, für gute Ideen und genug Geduld, sich

auch noch die nächste Tabelle anzusehen.

Dr. Silke Oeljeklaus

Für die Korrektur dieser Arbeit, für die stets offenen Ohren, für etliche Edwards-Kaffees und noch

mehr Kilometer.

Dr. Katja Kuhlmann

Für die Zusammenarbeit beim Schreiben des Manuskripts, die Korrekturen dieser Arbeit und den

gemeinsamen Kampf mit der HCT.

Dr. Lian Gelis und Markus Osterloh

Für die Hilfe und fruchtbare Zusammenarbeit. Für die gelungenen Messungen, aufmunternde

Gespräche, Last-Minute Emails und produktive Mittagessen.

Christian Bunse

Für… wo soll ich anfangen? Für dein unerschöpfliches Wissen über alle am MPC befindlichen MS und

HPLCs, für deine Bereitschaft, dieses Wissen weiter zu geben und dich für die exzellente Performance

dieser Geräte einzusetzen und so exzellentes Arbeiten überhaupt zu ermöglichen. Vielen Dank für

tröstende Worte, lustige Briefe, Feierabendrätsel und die unzähligen Mittagspausen. Vielen Dank für

meinen ersten Spitznamen.

Nadine Stoepel, Magdalena Pawlas, Nadine Palacios und Jenny Dworschak – den Mädels

Für die Hilfe in Labor und Büro.Nadine und Magdalena, euch danke ich besonders für die Betreuung

der Orbitrap.

Besonders bedanken möchte ich mich bei euch für unsere Mädelsabende mit so vielen Stunden

quatschen, weinen, schimpfen, zuhören und vor allem lachen. Für die stetige Begleitung in allen

Lebenslagen, die mich dazu veranlasst hat, mich auf jeden einzelnen Arbeitstag zu freuen.

Heiner Falkenberg

Für die Wiederherstellung meines Glaubens an Studenten und dafür, dass ich mindestens genauso viel

von dir gelernt habe, wie du von mir.

Allen Mitgliedern der Bioinformatikgruppe des MPCs

Dafür, dass ihr durch euren unermüdlichen Einsatz an der CPU-Front meine Arbeit erst möglich

gemacht habt. Dafür, dass ich alles fragen konnte, auf alles eine Antwort bekommen habe und dass ihr

trotzdem immer wieder ans Telefon gegangen seid, wenn ich angerufen habe.

Speziell bedanken möchte ich mich bei Dr. Martin Eisenacher, Dr. Michael Kohl und Dr. Christian

Stephan für die viele Hilfe während all der Jahre und für die gute Zusammenarbeit.

Eva Hawranke, Anna Lendzian und Christiane Schary

Für viel Hilfe im Labor und eure Freundschaft. Vielen Dank für einfach unglaubliche Treks, von denen

ich noch meinen Kindern erzählen werde.

Allen anderen Mitgliedern der AG Cellular Proteomics und des ganzen MPCs

Für das unglaublich gute Arbeitsklima und die allgegenwärtige Hilfsbereitschaft

Dr. Andrea Blöchl

Für Erleuchtungen, Ideen und alles, was du mir beigebracht hast. Für dein andauerndes Interesse und

dass du nie aufgehört hast mich zu betreuen.

Nicht zuletzt möchte ich mich bei den Menschen bedanken, die mich immer und unerschütterlich in

allen Lebenslagen begleiten und tragen und die mich besonders in diesem Jahr viel unterstützt

haben. Ohne euch wäre das alles nicht möglich gewesen:

Mama und Papa, danke für die den Rückhalt, für Hilfe, wenn ich sie brauche und für die Kraft, die ihr

mir gebt, weil ihr an mich glaubt. Danke für ein zu Hause und eine Familie, ohne die ich nicht wäre,

was ich bin.

Tobias, Anja und Sabine, danke für unendlich viele schöne Erinnerungen. Danke für gemeinsames

Lachen und Weinen, für das voneinander Lernen und Wachsen, für gemeinsames Arbeiten und

ausgelassenes Feiern. Danke für eure Freundschaft und dass ihr immer für mich da seid.

Opa Walter, danke für alles was ich von dir gelernt habe. Schön dass du da bist.

Christiane und Daniel, danke für eure Freundschaft und Unterstützung und dass ihr nun dazu gehört.

Manfred, Anita, Ulla, Martin, Maya und Julian, vielen Dank für eure stetige Unterstützung, für die

aufmunternden Worte und ein neu entdecktes Universum sportlicher Möglichkeiten. Ich fühle mich

sehr wohl bei euch.

Sebastian, vielen Dank für dein Durchhaltevermögen und die viele Hilfe im Zusammenhang mit dieser

Arbeit. Vielen Dank für deine Fürsorge, deine Geduld und deine Liebe. Vielen Dank für die

Geborgenheit, die mir die Kraft gegeben hat durchzuhalten. Ich bin so froh, dass ich dich habe!

- Gutta cavat lapidem -

Ovid

1 Content

2 Summary.......................................................................................................................................... 1

3 Zusammenfassung ........................................................................................................................... 3

4 Objectives ........................................................................................................................................ 6

5 Introduction ..................................................................................................................................... 7

5.1 Olfaction .................................................................................................................................. 7

5.1.1 Odor perception .............................................................................................................. 7

5.1.2 Olfactory receptor protein mediated signaling ............................................................... 7

5.1.3 Plasticity in the olfactory epithelium............................................................................... 8

5.2 Prostate specific G-protein coupled receptor ......................................................................... 9

5.3 Prostate cancer ........................................................................................................................ 9

5.4 Proteomics ............................................................................................................................. 10

5.4.1 Principles of mass spectrometric protein identification ............................................... 10

5.4.2 Ion trap mass spectrometer .......................................................................................... 12

5.4.3 The LTQ-Orbitrap mass spectrometer ........................................................................... 14

5.5 Phosphoproteomics .............................................................................................................. 16

5.5.1 Gel-based phosphoproteomics ..................................................................................... 17

5.5.2 Gel-free phosphoproteomics ........................................................................................ 17

5.5.3 Alternative fragmentation techniques .......................................................................... 18

5.5.4 Bioinformatics ............................................................................................................... 19

5.5.5 Quantitative techniques ................................................................................................ 20

6 Materials and Methods ................................................................................................................. 23

6.1 Reagents and Consumables .................................................................................................. 23

6.2 Animal preparation ............................................................................................................... 26

6.3 Gel electrophoresis................................................................................................................ 26

6.4 Staining procedures ............................................................................................................... 28

6.5 Chromatography.................................................................................................................... 30

6.6 Mass spectrometry ................................................................................................................ 31

6.7 Cell culture ............................................................................................................................. 34

6.8 Cell migration assays ............................................................................................................. 34

6.9 Phosphoproteomics sample preparation .............................................................................. 35

6.10 Titanium dioxide affinity purification .................................................................................... 36

6.11 Bioinformatics ....................................................................................................................... 36

7 Results ........................................................................................................................................... 42

7.1 Pro-Q® Diamond Phosphoprotein Gel Stain .......................................................................... 42

7.2 Establishment of a gel free phosphoproteomics workflow .................................................. 49

7.2.1 Ortho-vanadate treatment of LNCaP cells .................................................................... 50

7.2.2 Cell lysis and tryptic digestion ....................................................................................... 50

7.2.3 Desalting and preparation for SCX ................................................................................ 51

7.2.4 SCX chromatography ..................................................................................................... 53

7.2.5 Titanium dioxide affinity enrichment ............................................................................ 55

7.2.6 Mass spectrometric analysis ......................................................................................... 56

7.2.7 Bioinformatics analysis .................................................................................................. 58

7.3 Global phosphoproteomic analysis of orthovanadate treated LNCaP cells .......................... 62

7.3.1 Strategy for the phosphoproteome analysis of LNCaP cells ......................................... 62

7.3.2 MS-based analysis of LNCaP cell lysates without phosphopeptide enrichment ........... 64

7.3.3 MS-based anaylsis of LNCaP cell with phosphopeptide enrichment ............................ 65

7.3.4 Comparison of mass spectrometric and bioinformatic analysis platforms ................... 67

7.4 Quantitative time-resolved phosphoproteomics to study receptor-mediated signaling

pathways in LNCaP cells .................................................................................................................... 70

7.4.1 SILAC labeling of LNCaP cells ......................................................................................... 70

7.4.2 Quantitative time-resolved phosphoproteomic strategy ............................................. 71

7.4.3 Phosphoproteome data of β-ionone-treated LNCaP cells ............................................ 74

8 Discussion ...................................................................................................................................... 85

8.1 Phosphoproteomics applied to the mouse olfactory epithelium ......................................... 85

8.2 Establishment of a global phosphoproteomics strategy to study olfactory receptor-

mediated signaling events in LNCaP cells.......................................................................................... 88

8.2.1 Establisment and refinement of the phosphoproteomics strategy .............................. 88

8.2.2 Global phosphoproteomics analysis of orthovanadate-treated LNCaP cells ................ 91

8.2.3 Concluding remarks ....................................................................................................... 95

8.3 Quantitative and time-resolved phosphoproteomic study of β-ionone-treated LNCaP cells

97

8.3.1 SILAC labeling ................................................................................................................ 97

8.3.2 Time resolved and quantitative β-ionone treatment experiment ................................ 98

8.3.3 Recapitulation of phospho-protein regulation in β-ionone-stimulated LNCaP cells .. 140

9 Conclusion ................................................................................................................................... 143

10 Outlook .................................................................................................................................... 145

11 Literature ................................................................................................................................. 147

12 Supplementary Tables ............................................................................................................. 167

13 Publications ............................................................................................................................. 168

Curriculum Vitae .................................................................................................................................. 169

Erklärung ............................................................................................................................................. 171

Summary

1

2 Summary

While the mechanisms of olfactory habituation have long been studied in the olfactory bulb and

higher brain region, less is known about the underlying mechanisms in the periphery, i.e. the

olfactory sensory neurons. To gain new insights into the molecular events leading to habituation, the

effects of long-term odorant treatment on the protein composition of the olfactory epithelium of

mice have recently been investigated employing a gel-based quantitative proteomics approach

(Barbour et al. 2008). In a continuative study, a differential phosphoproteomics analysis of dynamic

changes in the phosphorylation level of proteins present in the olfactory epithelium of mice following

receptor activation upon odorant binding was conducted in the first part of this work. Following the

strategy of Barbour et al., a gel-based method using 2-dimensional gel electrophoresis in

combination with phosphospecific fluorescent ProQ®Diamond (Pro-Q) staining and subsequent

protein identification by mass spectrometry was employed. Although this approach allowed the

detection of odorant-induced changes in the Pro-Q stained spot pattern, its applicability to a global

differential phosphoproteomics study of odorant treated murine OE was limited by an insufficient

ability to identifiy Pro-Q stained phosphoproteins and phosphosites by mass spectrometry.

In the second part of this work, olfactory receptor-mediated signaling was studied in non-olfactory

tissue using LNCaP cells as model system. LNCaP is a human prostate carcinoma cell line, which

endogenously expresses the prostate-specific G-Protein coupled receptor (PSGR), which is a member

of the olfactory receptor family that is specifically expressed in the prostate and that has been

implicated in prostate cancer development and progression. To study the phosphoproteome and, in

particular, dynamic changes in protein phosphorylation levels induced by PSGR activation in LNCaP

cells, an advanced phosphoproteomics strategy needed to be established to allow for the

comprehensive identification and accurate quantification of thousands of phosphopeptides in a

single experiment. For this purpose, a phosphoproteomics strategy was established and thoroughly

refined that comprised multidimensional separation and titanium dioxide-based phosphopeptide

enrichment as well as two different state-of-the-art LC/MS instrumentational setups (LTQ-

Orbitrap XL and HCT Ultra PTM) employing electron transfer dissociation (ETD) and multistage

activation (MSA) for phosphospecific fragmentation experiments in combination with refined

bioinformatic analyses.

This approach was first utilized for the global analysis of the phosphoproteome of orthovanadate-

treated LNCaP cells. This experiment was conducted in two biologically independent replicates using

as little as 200 µg of starting material per experiment. Following the workflow employing the HCT

Ultra ion trap mass spectrometer in combination with neutral loss-triggered ETD, 891 unique

phosphopeptides were identified. Using the LTQ-Orbitrap XL mass spectrometer in combination with

Summary

2

MSA, 1310 unique phosphopeptides were identified. These two approaches provided highly

complementary data sets, resulting in the identification of a total of 2569 unique phosphorylation

sites in 2095 phosphopeptides originating from 726 different phosphoproteins. Amongst these, 164

hitherto unknown phosphorylation sites were reported in this work for the first time. Several of the

newly identified phosphorylation sites stem from proteins, which are highly relevant in the context of

prostate cancer, including androgen receptor-related proteins, tumor proteins and kinases. These

proteins are novel candidate proteins for follow-up studies on prostate cancer which may eventually

lead to a better understanding of molecular processes involved in the development and proliferation

of prostate cancer.

In addition to the so far most comprehensive characterization of the phosphoproteome of LNCaP

cells, dynamic changes in protein phosphorylation induced by PSGR activation were studied using

quantitative phosphoproteomics methods. To this end, triple stable isotope labeling by amino acids

in cell culture (SILAC) was established for LNCaP cells and a differential, time-resolved

phosphoproteomics analysis of LNCaP cells treated with the known PSGR agonist β-ionone for 2 min

and 10 min was conducted. This study was performed in triplicate using 200 µg of starting material

per experiment and resulted in the identification of 1154 unique phosphopeptides. Of these, 99

phosphopeptides from 73 proteins were found to be differentially up- or downregulated upon

β-ionone treatment. Regulated phosphopeptides of low abundance were typically quantified in one

of three experiments only using the MaxQuant software. To improve the quantification for these

target peptides, an in-house developed Microsoft Excel-based VBA script was developed and

successfully employed in this work for the first time. As a result, higher accuracy in the determination

of regulation factors was obtained and a very high reproducibility of regulation factors between

replicates was demonstrated.

Based on proteins identified as being regulated upon β-ionone treatment, initial signaling events

after PSGR activation located at the plasma membrane were hypothesized. In addition, dynamic

phosphorylation was determined for several proteins, which are involved in the formation of

junctions and cell-cell contacts as well as in cytoskeletal dynamics, implicating a role for

PSGR-mediated signaling in migration. In a first migration assay, it was further demonstrated, that

β-ionone treatment efficiently inhibits serum-directed migration of LNCaP cells. Furthermore,

proteins involved in processes such as transcription, translation and cell cycle progression were

found to be differentially phosphorylated upon PSGR activation. These proteins may provide new

targets, which play an important role in mediating the previously reported antiproliferatory effects of

PSGR stimulation and are therefore of special interest in the context of prostate cancer and drug

development.

Zusammenfassung

3

3 Zusammenfassung

Während die Mechanismen der Geruchsgewöhnung auf der Ebene des Riechkolbens und höherer

Gehirnregionen bereits seit längerer Zeit untersucht werden, ist deutlich weniger über die zugrunde

liegenden Mechanismen in der Peripherie, dem olfaktorischen Epithel, bekannt. Um neue Einblicke in

die zur Geruchsgewöhnung führenden molekularen Ereignisse zu erhalten, wurde kürzlich unter

Anwendung einer Gel-basierten quantitativen proteomischen Strategie untersucht, welchen Einfluss

Langzeitbehandlungen mit einem Duftstoff auf die Proteinkomposition des olfaktorischen Epithels

haben (Barbour et al. 2008). In einer weiterführenden Studie wurde dann im ersten Teil der

vorliegenden Arbeit eine differentielle Phosphoproteomstudie durchgeführt, um dynamische

Veränderungen im Phosphorylierungslevel der Proteine im olfaktorischen Epithel nach

Duftstoffaktivierung des Rezeptors zu analysieren. In Anlehnung an die Studie von Barbour et al.

wurde ein Gel-basierter Ansatz gewählt, in dem zweidimensionale Gelelektrophorese mit

phosphospezifischer Fluoreszenzfärbung mit ProQ® (Pro-Q) Diamond kombiniert wurde. Mit diesem

Ansatz konnten Duftstoff-induzierte Veränderungen im Pro-Q gefärbten Proteinmuster detektiert

werden. Wie jedoch in dieser Arbeit gezeigt wurde, ist die limitierte Nachweisbarkeit von

Phosphorylierungstellen in Pro-Q gefärbten Proteinspots ein großer Nachteil für die Anwendbarkeit

dieser Methode auf eine globale Phosphoproteomstudie des OEs.

Im zweiten Teil dieser Arbeit wurde die Geruchsrezeptor vermittelte Signalweiterleitung in nicht

olfaktorischen Geweben studiert. Dafür wurde die LNCaP Zelllinie als Modelsystem verwendet. Diese

Zelllinie ist eine humane Prostatakarzinom-Zelllinie, die den Prostate-specific G-Protein coupled

Receptor (PSGR) endogen exprimiert. Dieser Rezeptor ist ein Mitglied der Familie der olfaktorischen

Rezeptoren und wird spezifisch in der Prostata und in Prostatakrebszellen exprimiert wird. Um das

Phosphoproteom und insbesondere die Duftstoff-induzierten dynamischen Veränderungen im

Protein-Phosphorylierungslevel in LNCaP Zellen zu analysieren, war es erforderlich, einen

leistungsfähigen phosphoproteomischen Ansatz zu etabieren, der eine umfassende Charakterisierung

und akkurate Quantifizierung von tausenden von Phosphopeptiden in einem einzigen Ansatz

ermöglicht. Zu diesem Zweck wurde ein Phosphoproteomansatz etabliert und optimiert, der

multidimensionale Peptidauftrennung und Titandioxid-basierte Phosphopeptidanreicherung mit

Messungen an „state-of-the-art“ LC/MS Systemen (LTQ-Orbitrap XL und HCT Ultra PTM) unter

Verwendung von Electron Transfer Dissociation (ETD) und Multistage Activation (MSA)

Fragmentierung und adäquater bioinformatischer Datenanalyse vereint.

Dieser Ansatz wurde zunächst für die globale Analyse des Phosphoproteoms von Orthovanadat-

behandelten LNCaP-Zellen verwendet. Dieses Experiment wurde in zwei unabhängigen biologischen

Replikaten durchgeführt, für die jeweils nur 200 µg Ausgangsmaterial verwendet wurde. Mittels der

Zusammenfassung

4

Messungen auf dem HCT Ultra Ionenfallenmassenspektrometer unter Anwendung der Neutralverlust

induzierten ETD Fragmentierung wurden 891 Phosphopeptide identifiziert. Unter Verwendung eines

LTQ-Orbitrap Massenspektrometers mit MSA Fragmentierung konnten insgesamt 1310

Phosphopeptide identifiziert werden. Diese beiden Datensätze waren hochgradig komplementär.

Insgesamt wurden 2095 nicht-redundante Phosphopeptide aus 726 verschiedenen

Phosphoproteinen identifiziert. In den identifizierten Phosphopeptiden konnten insgesamt 2569

einzelne Phosphorylierungsstellen identifiziert werden, von denen 164 bislang unbekannt waren, und

die in dieser Arbeit erstmalig nachgewiesen wurden. Einige der neu entdeckten

Phosphorylierungsstellen kommen in Proteinen vor, die im Kontext von Prostatakrebs von großem

Interesse sein könnten, wie z.B. Androgenrezeptor-assoziierte Proteine, Tumorproteine und sieben

Kinasen. Diese Proteine stellen neue Kandidatenproteine für weiterführende Studien an

Prostatakrebs dar, die im Folgenden zu einem besseren Verständnis der molekularen Prozesse führen

könnten, die zur Entstehung und Entwicklung von Prostatakrebs führen. Die Ergebnisse dieser Studie

stellen die bislang umfassenste Charakterisierung des Phosphoproteoms von LNCaP Zellen dar.

Für die Beobachtung von Geruchsstoff-induzierten dynamischen Veränderungen im

Phosphorylierungslevel von Proteinen wurde die Dreifachmarkierung mit Stable Isotope Labeling by

Amino Acids in Cell Culture für LNCaP-Zellen etabliert und eine zeitaufgelöste und quantitative

Phosphoproteomstudie an LNCaP-Zellen, die für jeweils 2 und 10 Minuten mit dem PSGR-Liganden β-

Jonon stimuliert wurden, durchgeführt. Diese Studie wurde in drei Replikaten unter Verwendung von

jeweils 200 µg Startmaterial durchgeführt und resultierte in der Identifizierung von insgesamt 1154

individuellen Phosphopeptiden. 99 dieser Phosphopeptide aus 73 verschiedenen Proteinen wurden

als differentiell durch β-Jonon Behandlung hoch- und runter-reguliert identifiziert. Niedrig abundante

Phosphopeptide wurden typischerweise in einem von drei Replikaten indentifiziert, wenn

ausschließlich MaxQuant als Auswertesoftware verwendet wurde. Um die Quantifizierung dieser

Peptide zu verbessern, wurde ein Microsoft Excel-basiertes VBA Skript entwickelt, das in dieser

Arbeit erfolgreich zum ersten Mal angewendet wurde. Durch die Verwendung dieses Skripts konnte

die akkurate Quantifizierung von Peptiden verbessert und eine hohe Reproduzierbarkeit für die

ermittelten Regulationsfaktoren gezeigt werden.

Auf Basis der differentiell phosphorylierten Proteine konnte eine Hypothese über die initialen

Signalereignisse an der Plasmamembran aufgestellt werden. Desweiteren wurden regulierte

Phosphorylierungsstellen in einer Reihe von Proteinen identifiziert, die mit der Ausbildung von

Junctions und Zell-Zell Kontakten sowie der dynamischen Regulierung des Zytoskeletts in Verbindung

gebracht werden, was auf einen Einfluss PSGR-vermittelter Signalwegen auf das Migrationsverhalten

der Zellen hinweist. In einem ersten Migrationstest konnte gezeigt werden, dass die Behandlung mit

β-Jonon die Migration von LNCaP-Zellen auf einen Serumstimulus hin verhindern kann. Darüber

Zusammenfassung

5

hinaus wurden Proteine als differentiell phosphoryliert gefunden, die mit Transkription, Translation

und Zell-Zykluskontrolle assoziiert werden. Diese Proteine könnten eine wichtige Rolle für die bereits

vorher beschriebene antiprolfieratorische Wirkung von β-Jonon-Stimulation des PSGR spielen und

stellen interessante neue Kandidatenproteine für weiterführende Studien dar, da diese

wachstumshemmende Wirkung gerade im Zusammenhang mit Prostatakrebs und der Entwicklung

von neuen Medikamenten von großer Bedeutung sein könnten.

Objectives

6

4 Objectives

The aim of this work is to study ligand-induced olfactory receptor- (OR-) mediated signaling cascades.

Olfactory receptors comprise the largest family of G-protein coupled receptors (GPCRs) encoded in

the mammalian genome. GPCRs of this family are expressed in olfactory sensory neurons in the

olfactory epithelium but also in other tissue such as skin, pancreas and prostate. As receptor-

triggered signaling is largely mediated by phosphorylation and dephosphorylation of signaling

proteins, dynamic changes in the phosphoproteome of OR-expressing tissue or cells upon receptor

activation should be investigated. For this purpose, adequate methodologies for quantitative and

time-resolved phosphoproteomics analyses need to be established.

For the analysis of olfactory epithelia of mice exposed to the odorant octanal, a gel-based approach

using 2-dimensional gel-electrophoresis in combination with a phosphospecific fluorescent dye is

implemented. The comparison between octanal treated and non-treated control samples is done

densitometric image analysis of the resulting spot patterns. Proteins from differentially stained

spots are subsequently identified using nano-HPLC in comination with tandem mass spectrometry

(MS) and subsequent database searches.

In further studies, signaling networks activated by the prostate specific G-protein coupled receptor

(PSGR) are investigated. This receptor is expressed in the prostate and has been implicated in

prostate cancer development and progression. The signaling events triggered by the activation of

PSGR are largely unknown to date. Ligands of this receptor, namely androstenone derivateves and

the odorant β-ionone, have recently been reported (Neuhaus et al. 2009), which renders the PSGR

and its signaling network amenable to concerted studies. Therefore, a gel-free phosphoproteomics

workflow is established and refined that include prefractionation by strong cation exchange

chromatography and titanium dioxide-based phosphopeptide enrichment in combination with

alternative phosphospecific MS methods regarding different mass spectrometric instruments and

subsequent bioinformatics analysis. Using this MS-based phosphoproteomics workflow, cells from a

prostate carcinoma cell line (LNCaP) treated with either ortho-vanadate or β-ionone for different

intervals are analyzed. In these analyses the global phosphoproteome of LNCaP cells is analyzed as

well as dynamic changes in protein phosphorylation levels upon PSGR activation. The results could

provide interesting new candidate proteins both in the context of prostate cancer development and

progression and for PSGR mediated signaling processes.

Introduction

7

5 Introduction

5.1 Olfaction

The mammalian olfactory system is able to detect and distinguish between an enormous variety of

volatile chemicals and plays an important role in the choice of food (Elsaesser and Paysan 2007) as

well as in detecting social cues (Spehr et al. 2006). For example, rats smell the state of health of

individuals of their species and avoid adults, when they are ill, but not juveniles (Arakawa et al.

2009).

5.1.1 Odor perception

Odor perception is mediated by two distinct systems located in the nasal cavity: the main olfactory

epithelium (OE) and the vomeronasal organ (VNO), where pheromones are detected (Keller et al.

2009). The initial event of odor sensation is the binding of odorant molecules to olfactory receptor

(OR) proteins, which are located in the membrane of cilia protruding from the olfactory knob at the

apical end of olfactory sensory neurons (OSN). Upon detection of an odorant, action potentials are

triggered, which are conducted along the axons of OSNs to the olfactory bulb (OB). OSNs expressing

the same olfactory receptor project to the same glomeruli in the OB and odors are recognized by the

specific pattern of activated glomeruli (Mombaerts et al. 1996; Firestein 2004).

5.1.2 Olfactory receptor protein mediated signaling

OR proteins are 7-transmembrane proteins, which have been identified by Buck and Axel (Buck and

Axel 1991) as a superfamily of G-protein coupled receptors (GPCRs), comprising about 1000

members in mice (Zhang et al. 2004) and about 400 members in human (Armbruster and Roth 2005).

Each OR type can bind a set of structurally related odorants, which can function as either agonists or

antagonists (Hatt 2004). The immediate effect of OR agonist binding is the activation of the Golf –

protein which in turn mediates the activation of adenylate cyclase III and the production of cyclic

adenosine monophosphate (cAMP). The increase in the intracellular cAMP level leads to the opening

of cyclic nucleotide-gated (CNG) channels and thus, to an influx of Ca2+ and Na+ ions. The entering

Ca2+ ions additionally open a chloride channel to allow efflux of Cl- ions. Thereby, the depolarization

is further increased eventually leading to the generation of an action potential, which is conducted

along the axon into the olfactory bulb (Firestein 2001; Hatt 2004). Besides these events responsible

Introduction

8

for the generation of an action potential, also other signaling events take place upon receptor

activation. For example, the accelerated intracellular cAMP level also activates the cAMP-dependent

protein kinase (PKA), which is then able to phosphorylate various proteins and especially voltage-

gated sodium and calcium channels, which may contribute to the adaptation process in odorant

perception (Wetzel et al. 2001). Furthermore, some odorants are known to induce phospholipase C

(PLC) and to mediate cellular response via the second messengers inositol triphosphate (IP3) and

diacylglycerol (DAG) rather than via cAMP (Zufall and Munger 2001). Blocking of the

phosphatidylinositol 3-kinase (PI3K) was shown to elevate odorant-induced increases in intracellular

calcium levels, thereby implicating a role of this pathway in the inhibition of odorant response (Spehr

et al. 2002).

5.1.3 Plasticity in the olfactory epithelium

The olfactory system has to detect dynamic changes in odorant cues in the individual’s environment.

Hence, to increase its sensitivity, the olfactory system has to filter for background odorants through

desensitation or even habitutation towards these continuously present odorants. On the one hand,

sensitivity of olfactory perception can be modulated at higher brain regions through the plasticity in

the OB and the olfactory cortex (Davis 2004; Wilson et al. 2004). On the other hand, adaptation can

be mediated on the level of olfactory sensory neurons (OSNs). While short-term adaptation has

already been studied extensively (Zufall and Leinders-Zufall 2000), long-term changes in the OE

induced by continuous odorant exposure are still poorly understood. To gain new insights into the

effects of long-term odorant exposure on the protein composition of the OE, Barbour et al.

performed a quantitative proteomics study of OE of mice treated with either continuous or pulsed

exposure to the odorant octanal for 20 days (Barbour et al. 2008). Mice exposed to the pulsed

treatment regime showed slight desensitization towards octanal, whereas contiuously treated mice

showed actual octanal habituation. The OE protein composition from differentially treated mice was

quantitatively compared using differential in-gel electrophoresis and regulated proteins were

subsequently identified using mass spectrometry (MS). Proteins determined as differentially

regulated were functionally classified in three groups: calcium-binding proteins, cytoskeletal proteins

and lipocalins (odorant-binding proteins, OBPs), with proteins from the lipocalin group being

regulated to the greatest extent. While the role of OBPs in the regulation of adaptation is still

unknown, a calcium-binding protein, namely calcium calmodulin, has recently been reported to

mediate sensory adaptation in the VNO (Spehr et al. 2009).

Introduction

9

5.2 Prostate specific G-protein coupled receptor

Several members of the OR family have been found to be expressed in other tissues than the OE

(Feldmesser et al. 2006; Zhang et al. 2007). However, their functions in these tissues are largely

unknown. One such receptor is the prostate specific G-protein coupled receptor (PSGR). It was first

described by Xu et al. (Xu, L. L. et al. 2000) as a member of the OR family and was found to be

specifically expressed in the prostate with elevated expression levels in prostate cancer. Its

expression levels were found to be especially increased in early prostate cancer stages, suggesting its

role in early prostate cancer development and progression (Weng et al. 2005b). Later on, the PSGR

was proposed as a biomarker for prostate cancer (Wang et al. 2006). While PSGR expression was

reported to be regulated by interleukin-6 via two different promoters (Weng et al. 2005a), its actual

function and the signaling events triggered by its activation are still largely unknown. Noteworthy,

the ligands of this receptor have recently been identified (Neuhaus et al. 2009). Neuhaus and

coworkers were able to demonstrate, that PSGR activation by androstenone derivatives mediates

rapid, non-genomic steroid actions in LNCaP cells, strengthening a potential role of PSGR in

cancerogenesis. Beyond that, the odorant β-ionone, having a characteristic odor of violet, was

identified as ligand. It is an isoprenoid, which is widely found in plants (Sacchettini and Poulter 1997).

Plant isoprenoids in general and specifically β-ionone and geraniol have been shown to inhibit cancer

cell growth (Elson et al. 1999; Duncan et al. 2004). Neuhaus et al. demonstrated that β-ionone

inhibits cancer cell proliferation via activation of the PSGR, suggesting that PSGR-triggered events

mediate the anti-tumorigenic effects of isoprenoids. In addition, activated PSGR was shown to lead

to enhanced phosphorylation of the mitogen-activated protein kinases p38 and stress-activated

protein kinase/c-Jun NH2-terminal kinase (Neuhaus et al. 2009). Nonetheless, the exact mechanisms

underlying these processes still remain elusive.

5.3 Prostate cancer

In developed countries, prostate cancer (PCa) is the second most frequently diagnosed cancer in

men, with an estimated 192,000 new cases and 27,000 deaths in Northern America in 2009 (Damber

and Aus 2008; Jemal et al. 2010). Although long-term survival rates are generally high, PCa remains a

lethal disease with poor treatment options when it has developed to a hormone-independent state

(Bonkhoff and Berges 2010; Ferlay et al. 2010).

Many signaling pathways have been found to be involved in prostate carcinogenesis. The most

prominent pathway is androgen receptor (AR) signaling, which is involved in development and

Introduction

10

progression of PCa cells from an early disease state to an hormone-independent and hitherto

incurable state. But also other pathways such as abnormal growth factor receptor signaling are

involved in the progression of normal to malignant prostate cells (Kung and Evans 2009; Ramsay and

Leung 2009; Traish and Morgentaler 2009; Bonkhoff and Berges 2010). These pathways are not linear

but interact with each other in the context of a network. In order to learn more about prostate

carcinogenesis, it is therefore essential to dissect the underlying signaling networks.

A widely used model system to study PCa is the LNCaP cell line. This cell line was established from a

metastatic lesion of human prostatic adenocarcinoma from the left supraclavicular lymph node from

a 50-year-old Caucasian male (Horoszewicz et al. 1980; Horoszewicz et al. 1983). The cell line is

androgene-sensitive (Horoszewicz et al. 1983), has relatively low tumorigenic potential (Witkowski et

al. 1993) and exhibits a low intrinsic migratory capacity, requiring a stimulus for migration (Slack et

al. 2001). Hence, it represents relatively early stages in prostate cancerogenesis. LNCaP cells, which

endogeneously express PSGR, were used by Neuhaus and coworkers to study PSGR-mediated effects

(Neuhaus et al. 2009).

5.4 Proteomics

As the sequencing of genomes proceeded from the first sequenced genome of the phage φ-X174

(Sanger et al. 1977) to the completion of the human genome (Science issue, 11th of April 2003 and

Nature issue, 24th of April 2003), the basis was provided for the study of the resulting proteomes.

From each gene, several different protein entities can be expressed through alternative splicing.

These proteins can further be modified by diverse post-translational modifications such as

phosphorylation, glycosylation or methylation. Which gene is translated, which protein is expressed

from a given gene and how the resulting protein is modified depends on several macroscopic and

molecular prerequisites such as age, tissue, cell type, environmental conditions or cell cycle state.

The composition of this highly dynamic universe of expressed protein species at a given time, under

specified conditions and in a specific organell, cell or tissue is termed proteome (Wilkins, Siena

meeting 1994). A powerful tool in proteomics is the identification of proteins by MS.

5.4.1 Principles of mass spectrometric protein identification

MS-based protein identification has become the method of choice to analyze complex biological

samples. The access to genome databases of various model organisms, significant improvements of

Introduction

11

"soft" ionization methods (Nobel Prize in chemistry 2002) and MS instrumentation for the efficient

analysis of biomolecules as well as dramatic advancement in computional technology during the last

decade nowadays allow the identification of thousands of proteins within a single study (Walther and

Mann 2010).

A standard proteomics workflow is depicted in Figure 1. Protein populations isolated from cells or

tissue (Figure 1A) are separated by one-dimensional gel electrophoresis and the resulting protein

bands are proteolytically digested, typically using trypsin as enzyme (Figure 1B). Another approach is

the direct in-solution digestion of the protein mixture with subsequent fractionation of the resulting

peptide mixture by chromatographic methods (Figure 1C). In both cases, gaseous peptide ions can

subsequently be formed from the liquid phase by electrospray (ES) for further MS analysis. ES

ionization (ESI) is often used in combination with high performance liquid chromatography (HPLC)

and fast tandem MS instrumentation (LC-MS/MS) (Figure 1D) in order to enable the efficient analysis

of complex samples (Aebersold and Mann 2003).

Fig. 1: General proteomics workflow. Proteins are extracted from any kind of biological samples derived from

tissue, animal or cell culture using adequate protocols (A). Complex protein mixtures are then separated by

one-dimensional gel electrophoresis (B) and respective protein bands are excised and proteolytically digested

to generate peptide samples. Alternatively, protein samples are directly digested in-solution (C) and the

resulting complex peptide mixtures are fractionized by chromatographic means (e.g. strong cation-exchange

chromatography) in the first dimension. Subsequently, peptide fractions are further separated by high

performance liquid chromatography directly coupled with electrospray tandem mass spectrometry.

During this process, peptides are separated by HPLC and continuously transferred to the ES source

followed by mass-to-charge (m/z) analysis in the high vacuum region of the MS instrument. In order

to perform peptide sequencing, the most intense precursor ions detected in the MS survey scan are

Introduction

12

sequentially isolated for further fragmentation (MS/MS) analysis. To induce fragmentation, analyte

ions are accelerated in an electric field and collide with inert gas molecules, typically helium or

nitrogen. The collisional energy is converted into internal vibration energy and distributed over the

molecule, thereby disrupting bonds and causing the peptide ion to fragment. Peptide ions are

fragmented predominantly at the amide bonds along the peptide backbone, giving rise to b- and y-

ion series (Roepstorff and Fohlman 1984). This process is called collision-induced dissociation (CID).

Peptide fragmentation spectra generated by CID can be subsequently compared to theoretical

fragmentation spectra by database searching in order to eventually identify the amino acid sequence

of the respective peptides in an automated process. Based on the identified peptide sequences,

proteins are assembled and a final list of all the proteins identified in the sample is compiled.

In the work presented here a three-dimensional (3-D) ion trap instrument (HCT Ultra PTM, Bruker

Daltonic) and the LTQ-Orbitrap XL (Thermo Scientific), a hybrid MS instrument comprised of a linear

ion trap and an orbitrap mass analyzer were employed. Both are state-of-the-art MS instruments for

proteomics analysis.

5.4.2 Ion trap mass spectrometer

The first quadrupole ion trap was developed by Wolfgang Paul in the 1950s (Paul and Steinwedel

1953). He later shared the 1989 Nobel Prize for Physics with the inventer of the Penning trap Hans

Georg Dehmelt. Quadrupole ion traps or Paul ion traps exist in both linear and 3-D configurations. In

such a trap, ions can be stored by the application of constant DC fields and AC electric fields

oscillating with radio frequency.

As schematically shown in Figure 2, a 3-D ion trap consists of a hyperbolic ring electrode and two

hyperbolic end cap electrodes. The application of DC and AC voltages create a 3-D quadrupole

electric field in which ions can be stored at the center between the three electrodes. According to

Mathieu´s equations, the stability of ions is dependent on their m/z-ratio (March 1997). Variations in

the AC frequency lead to mass selective instability (Stafford et al. 1984), enabeling the use of an ion

trap as mass analyzer.

Introduction

13

Fig. 2: Schematic scetch of a 3-D ion trap. A 3-D ion trap consists of a hyperbolic ring electrode and two

hyperbolic end cap electrodes. Analyte ions can enter the trap through one end cap and can be stored in the

center between the three electrodes by applying a quadrupole field. The stored ions can be sequentially

ejected through the second end cap electrode according to their mass-to-charge ratio (Figure provided by

Bruker Daltonics).

One of the MS instruments mainly used in this work, is the HCT Ultra PTM from Bruker Daltonik

(Bremen, Germany). The schematic composition of the ion trap instrument is shown in Figure 3.

Analytes are ionized at atmospheric pressure in the ES source. Gaseous ions are then transferred

through a heated glass capillary and via dual octopole ion optics into the 3-D trap. Here, ions are

stored and sequentially ejected. The ejected ions are subsequently detected by a detector comprised

of a conversion dynode and a secondary electron multiplier. Survey scans as well as fragmentation

experiments are performed in the ion trap with helium as damping and collision gas. In the HCT Ultra

PTM instrument, a chemical ionization source (nCI module) is implemented, providing the possibility

to additionally introduce other chemical ions to allow for alternative fragmentation experiments

within the ion trap.

Introduction

14

Fig. 3: Schematic composition of the HCT Ultra PTM MS instrument. The HCT Ultra PTM ion trap instrument is

equipped with an atmospheric pressure electrospray ion source and gaseous ions are subsequently transferred

into the high vacuum compartments of the mass spectrometer. To enhance desolvatization, a heated drying

gas flow is applied and a skimmer further ensures that only charged particles are transferred into the ion trap.

In the high vacuum region, analyte ions are focused and transferred by dual octopole optics to the ion trap, in

which both MS and MS/MS experiments are conducted. Ions sequentially ejected from the trap are then

detected and translated to intensity over m/z mass spectra (Figure provided by Bruker Daltonics).

5.4.3 The LTQ-Orbitrap mass spectrometer

The Orbitrap mass analyzer is in principal an optimized Kingdon ion trap (Kingdon 1923). It was first

published in the year 2000 (Makarov 2000) and introduced as the core component of a commercially

available mass spectrometer in 2005 (Hu et al. 2005). Since then, these mass spectrometers have

been combined with diverse liquid chromatographic systems (Makarov and Scigelova 2010). As

depicted in Figure 4, an Orbitrap consists of an outer barrel-shaped electrode and an inner spindle-

shaped electrode. Ions injected into the Orbitrap oscillate in ring-like orbits around the center rod

and simultaneously move in axial direction up and down the center rod in harmonic oscillations.

While the frequency of the oscillation around the inner electrode is independent of the respective

m/z-value, the frequency of the axial oscillation depends on the m/z-ratio of the respective ions and

can be monitored at the outer electrodes of the Orbitrap. This so called image current is amplified

and transformed to a mass spectrum by fast Fourier transformation.

Introduction

15

Fig. 4: Cutaway of the Orbitrap mass analyzer. Ions are injected into the Orbitrap with a velocity perpendicular

to the axis of the center electrode and start to oscillate in the static electric field in rings around and along the

axis of the center electrode (Figure from www.thermo.com, modified).

The LTQ-Orbitrap XL instrument is a hybrid instrument combining a linar ion trap (LIT) and an

Orbitrap mass analyzer. The schematic construction of the LTQ-Orbitrap XL is depicted in Fig. 5.

Gaseous ions are generated in an atmospheric pressure ion source via electrospray and transferred

to the LIT via several desolvatization steps and ion optics. In the LIT, ions can be stored and either

transferred further via the C-trap to the Orbitrap analyzer or directly scanned out and detected with

the two detectors. Fragmenation experiments are typically performed in the LIT and the resulting

fragment ions can likewise be sequentially scanned out and detected via the two LIT detectors or can

be transferred to the Orbitrap analyzer for high resolution mass analyses. Alternatively, ions can be

fragmented by Higher Energy Collision (HCD) in an octopole termed HCD cell. Fragment ions resulting

from HCD, can solely be detected in the Orbitrap analyzer.

Ion detection in the LIT section is fast but provides limited resolution of about 2,000. The resolution

of the Orbitrap depends on the time taken to record the image current. Although a resolution of up

to 150,000 is theoretically possible, the resolution is usually set between 30,000 and 60,000 to keep

duty cycle times short. However, since the LIT and the Orbitrap analyzer can be used independently,

MS/MS experiments are typically conducted in the LIT while high resolution survey scans of

precursor ions are conducted in the Orbitrap. Thereby no actual delay is caused through high

resolution scans.

Introduction

16

Fig. 5: Schematic overview of the LTQ-Orbitrap XL mass spectrometer. Analytes are ionized at atmospheric

pressure (AP) and subsequently transferred into the high vacuum region of the instrument via a heated

capillary and ion optics. Ions are accumulated in the linear ion trap (LIT) and can either be transferred into the

Orbitrap mass analyzer via the C-trap or detected directly in the LIT. Fragmentation is accomplished in the LIT

and resulting fragment ions can be detected in the LIT or in the Orbitrap analyzer. In addition, fragmentation

can be conducted in the higher energy collision dissociation (HCD) cell. Fragments generated in the HCD cell

have to be detected in the Orbitrap mass analyzer (Figure from www.thermo.com, modified).

5.5 Phosphoproteomics

The function of proteins can be dynamically regulated by post-translational modifications (PTMs).

One of the most comprehensively studied PTM is protein phosphorlyation. Specific protein function

alterations as well as signal transduction are mainly mediated by phosphorylation and

dephosphorylation events via specific kinases and phosphatases. It is therefore crucial not only to

study changes in protein abundance, which typically occur on a longer time scale, but also to reveal

immediately triggered changes in the phosphorylation status of proteins to understand complex

biological signaling processes such as receptor activation and its effects on disease progression. Due

to their low abundance, substoichiometric presence and poor detectability in mass spectrometric

measurements, phosphorylated proteins have been notoriously difficult to analyze in global

approaches. In recent years, the proteomic techniques to characterize the phosphorylation status

and, beyond that, the dynamic changes in protein phosphorylation in a given system have improved

significantly (Grimsrud et al. 2010). Currently, there are two major approaches to

phosphoproteomics, namely gel-based and gel-free methods.

Ion Optics HCD Cell C-Trap Linear Ion Trap AP Ion Source

Orbitrap

Introduction

17

5.5.1 Gel-based phosphoproteomics

One recent approach to study dynamic changes in protein phosphorylation is the combination of gel

electorphoresis with a phosphospecific fluorescence dye. Thereby staining patterns can be compared

between different samples to detect differentially phosphorylated proteins. Proteins from

differentially stained spots are then subsequently identified using mass spectrometry. In most cases,

2-D gel electrophoresis is employed to provide maximal protein resolution and the only phospho

specific dye, which is commercially available at the moment, is the Pro-Q® Diamond (Pro-Q) stain.

This combination of 2-D PAGE and Pro-Q staining has successfully been employed in diverse

phosphoproteomics studies using plants (Agrawal and Thelen 2006; Chitteti and Peng 2007),

microorganisms (Dimina et al. 2009), mammalian cells and tissue (Gannon et al. 2008), bodyfluids

(You et al. 2010) and for kinase target screens (Orsatti et al. 2009). A main advantage of this and any

2-D gel-based technique is that protein isoforms and in particular different protein phosphorylation

isoforms are separated in the gel and thus, can be quantified independently. Nevertheless, gel-based

techniques are increasingly replaced by gel-free methods in large-scale studies, as only about 1,000

of the most abundant proteins are routinely visualized on a 2-D gel and potentially interesting

candidate (phospho)proteins are often of low abundance and thus, often evade detection (Rabilloud

et al. 2010).

5.5.2 Gel-free phosphoproteomics

Major challenges of the analysis of phosphorylated proteins from very complex mixtures stem from

the fact that often proteins become phosphorylated which are already present at low concentration

and in addition, protein phosphorylation typically occurs at substoichiometric level. Hence, the

concentration of non-phosphorylated proteins typically exceeds by far the concentration of

phosphoproteins in any given sample. Furthermore, the ionization efficiency of phosphopeptides

compared to their unphosphorylated counterparts is generally lower (Craig et al. 1994; Hunter and

Games 1994), hampering their routine detection in MS analysis. To facilitate the successful analysis

of hundreds to thousands of phosphopeptides from very complex mixtures (e.g. whole cell lysates),

both reduction of the sample complexity and the efficient enrichment of phosphopeptides prior to

MS analysis is a must (Dunn et al. 2010).

To reduce the sample complexity, different prefractionation techniques like gel electrophoresis

(Black et al. 2007), isoelectric focusing (Beranova-Giorgianni et al. 2006) or ion exchange

chromatography (Gruhler et al. 2005; Trinidad et al. 2006) have been applied in phosphoproteomics

studies. The most frequently employed techniques to enrich for phosphopeptides are metal affinity-

Introduction

18

based enrichment approaches, which exploit the affinity of different metal ions or oxides to the

oxygen atoms of the phosphate group. The first such method was immobilized Fe(III)-metal affinity

chromatography (IMAC, (Porath et al. 1975)). Although this method has already been known since

1975, first applications to phosphoproteins and -peptides were reported not before the 1990s (Scanff

et al. 1991; Muszynska et al. 1992). In recent years, other ions like Ga(III), Zr(IV) and Al(III) have been

investigated for application in IMAC enrichment (Nuhse et al. 2003). Metal oxide affinity

chromatography (MOAC) was introduced as a further efficient method for phosphopeptide

enrichment (Pinkse et al. 2004). Since then, the applicability of different metal oxides like TiO2, ZrO2,

Al2O3 and Nb2O5 to phosphopeptide enrichment has been shown, while TiO2 is today the most

commonly used resin in MOAC (Gerrits and Bodenmiller 2010).

Enabled by the use of a combination of efficient prefractionation and phosphopeptide enrichment

techniques, a vast number of phosphopeptides and newly identified phosphorylation sites have

increasingly been reported (Lemeer and Heck 2009). For example, Olsen et al. recently reported the

identification of over 20,000 phosphopeptides (Olsen et al. 2010). While such gel-free MS-based

approaches do often not allow for distinguishing between distinct protein isoforms, they provide the

capability of obtaining intriguing new insights into complex signaling processes in various biological

systems.

5.5.3 Alternative fragmentation techniques

Low-energy CID is the most commonly used peptide fragmentation technique in proteomics.

However, in serine- and threonine-phosphorylated peptides the phosphoryl bond is more labile than

the protonated peptide bond. Therefore, disscociation at this bond competes with backbone

fragmentation, resulting in dominant losses of phosphoric acid. Due to this lability of the phosphate

moiety, application of conventional low-energy CID for phosphopeptide analysis is often limited, as

the resulting spectra are dominated by neutral loss fragments and contain little or no sequence

information (Boersema et al. 2009). Alternative fragmentation techniques have therefore been

sought for phosphoproteomics applications. A recently reported technique well applicable to

phosphopeptide fragmentation is multi-stage activation (MSA) (Schroeder et al. 2004; Palumbo et al.

2008). With MSA, sequence-informative fragementation spectra of phosphopeptides are achieved by

low-energy CID followed by a further activation step. In this method, first generation CID ions

produced by one or multiple neutral losses are further activated at adequate fragementation

energies, eventually resulting in peptide backbone fragmenation. In contrast to a MS3 experiment,

the second activation is carried out without emptying the ion trap first, resulting in a pseudo-MS3

spectrum, which contains both MS2 and MS3 fragments (Boersema et al. 2009). By using this

Introduction

19

technique, the identification of phosphopeptides is significantly enhanced through enhanced

backbone fragmentation and thus, the generation of more sequence-determining fragment ions.

Another alternative (phospho)peptide fragmentation technique is termed electron transfer

dissociation (ETD) (Syka et al. 2004). This technique is based on a completely different fragmentation

mechanism. Here, peptide ion fragmentation is induced by the transmission of an electron from a

transfer reagent to the peptide to yield a peptide radical cation. This increases the basicity of the

amide carbonyl oxygen, which abstracts a proton from another amino acid in the peptide sequence.

Thereby the N-Cα bond is significantly weakend and peptide ions dissociate at this bond giving rise to

c- and z-ion series (Boersema et al. 2009). Through application of this technique, modifications like

phosphorylation and glycosylation remain intact at the respective amino acid. Hence, spectra contain

only sequence-specific fragment ions. Furthermore, as the phosphorylation site remains at the

specific amino acid, it can therefore directly be assigned to the correct position in the peptide.

5.5.4 Bioinformatics

In proteomics, peptide and protein identification is done by database searches employing specialized

algorithm such as Mascot (Perkins et al. 1999). For that purpose, a database of all potentially existing

proteins in the sample (e.g. a database restricted to a specific organism) is theoretically digested into

peptides and the theoretical MS/MS spectrum for each peptide is calculated. By comparing

measured fragmentation spectra to theoretical MS/MS spectra, peptides can be identified with a

certain probability and subsequently assembled to proteins. In order to give a measure for the

number of false positive hits in the protein list, a decoy database strategy is employed (Kall et al.

2008). In this strategy, the protein database in use is randomly shuffled to give nonsense sequences.

These sequences are added to the actual database and likewise theoretically digested in order to

generate artificial peptide sequences of which theoretical spectra are eventually calculated. If a

resulting protein list contains 1 % of decoy hits, the content of unknown false positive hits identified

from the original database is likewise expected to be 1 %.

For the characterization of phosphorylated proteins, it is essential not only to identify the respective

phosphopeptides, but also to determine the exact position of the phosphate moiety. Common search

engines used for automated protein identification generally enable to identify phosphopeptides with

quite high confidence, but no measure is given for the correctness of the phosphorylation site

localization (Boersema et al. 2009). To overcome this limitation, scoring algorithms have recently

been developed (Beausoleil et al. 2006; Cox and Mann 2008; Ruttenberg et al. 2008; Wan et al. 2008;

Bailey et al. 2009), which provide a score for the probability for the correct assignment of the

respective phosphorylation site in a given protein. However, these algorithms are generally not

Introduction

20

applicable to all kind of phosphoproteomic data sets but rather limited to data acquired with certain

fragmentation techniques, instrument types, data file formats or search engines.

5.5.5 Quantitative techniques

For effectively addressing biological questions by proteomics methodology, it is often essential to

obtain quantitative information on tausends of proteins in the same experiment. Relative

quantification of proteins in 2D gel-based approaches is generally performed via densitometry. In gel-

free approaches, protein quantification is performed based on signal intensities observed in MS

survey or MS/MS spectra. When samples to be quantitatively compared are jointly analyzed by MS,

signals derived from the different conditions have to be distinguishable. This is achieved by

introducing mass-coded labels (i.e. an internal reference). Otherwise, the samples need to be

separately processed and analyzed with subsequent comparison. As depicted in Figure 6 (Bantscheff

et al. 2007), there are different ways of introducing isotopic labels, namely metabolically, chemically

or enzymatically and alternatively as external standard components. By metabolic labeling, isotopic

labels are introduced in animals, cells or plants by supplying isotopically labeled nutrients. In plants,

this can be done by the application of normal and 15N-labeled inorganic salts (e.g. NO3). Plants

subsequently incorporate “light” or “heavy” nitrogen in all proteins (Oeljeklaus et al. 2009), resulting

in a mass shift of 15N-containing proteins. In cells or animals, labels can be introduced by providing

different isotopically labeled amino acids as nutrients. These are likewise incorporated in the

proteins, resulting in differentially labeled protein populations. This methodology was termed stable

isotope labeling by amino acids in cell culture (SILAC) (Ong et al. 2002; Mann 2006). The advantage of

metabolic labeling is illustrated in Figure 6. By this method, the label is introduced at the earliest

possible time point in the quantification workflow and allows joint sample processing, thereby

minimizing variances introduced by different sample handling. This methodology however, is

generally not applicable for tissue samples. Alternatively, isotopic labeles are therefore introduced

chemically. This can be performed either at the protein or at the peptide level, as shown in Figure 6.

Following labeling, samples are be combined, jointly processed and subsequently relatively

quantified via MS. The two most common protein labeling techniques are termed isotope-coded

affininty tag (ICAT) (Smolka et al. 2001) and isotope-coded protein label (ICPL) (Schmidt et al. 2005).

The use of both labeling methods results in differentially labeled peptides, which are therefore

relatively quantified on the basis MS survey scans. The two most common reagents used for isotopic

peptide labeling are iTRAQ® (Ross et al. 2004) and TMT® (Dayon et al. 2008). The use of these

reagents results in isobaric peptides, which are indistinguishable in MS survey scans. As a result of

peptide fragmentation however, reporter ions with different low molecular weight masses are

Introduction

21

generated. Therefore, relative quantification is here conducted on the basis of the respective

fragmentation spectra. It is of further note that ion trap instruments are typically not applicable due

to a lwo molecular mass cut-off in CID ion trap mass spectra. The advantage of such chemical

labeling, is the potential for multiplexing. iTRAQ®, for example, is commercially available for 4-, 6-

and 8-plexing approaches and, hence, allows for the simultaneous quantification of up to eight

different samples in the same experiment.

In addition, isotopic labels can be introduced enzymatically, for example, by proteolytic digestion in

“heavy” water, leading to the incorporation of one 18O per peptide (Fenselau and Yao 2009).

Resulting “heavy” and “light” peptides are then compared and relatively quantified on the basis of

MS spectra.

Beyond the methods used for relative quantification, protein concentrations can be absolutely

determined by the addition of labeled, external standard peptides of known quantity, as depicted in

Figure 6. This method is termed absolute quantification assay (AQUA) (Gerber et al. 2003).

Costs for quantification methods based on the introduction of isotopic labels are comparably high. In

conctrast, label-free quantification based on signal intensities or spectral counts of separately

processed samples (Figure 6), is far more cost efficient. However, this method bears the risk of

introducing artificial differences to the samples by sample handling errors (Bantscheff et al. 2007)

Fig. 6: Different quantitative proteomics workflows. There are different techniques to introduce isotopic

labels in order to quantify proteins and peptides by mass spectrometry. Coloured boxes indicate different

experimental conditions and horizontal lines indicate the time point, when samples are combined. Metabolic

labeling provides distinguishable sample populations from the beginning of the experiment, whereas chemical

labels can be introduced at the protein level or after digestion at the peptide level. At this level also standard

components can be spiked in. If different sample populations should be compared lable-free, samples can only

be combined at data analysis stage (Figure from Bantscheff et al. 2007).

Introduction

22

For quantitative phosphoproteomics, stable isotope labeling by amino acids in cell culture (SILAC)

(Ong et al. 2002) has been established as method of choice. SILAC in combination with modern MS

has successfully been applied to the study of different signal transduction pathways (Gruhler et al.

2005; Kruger et al. 2008; Pan et al. 2009; Christensen et al. 2010). In this metabolic labeling

approach, certain amino acids are replaced in the cell culture media by their heavy isotopically

labeled counterparts. “Light” and “heavy” amino acids are then incorporated in proteins during the

growth and division of cells. In proteomics experiments, arginine and lysine are typically employed as

“heavy” amino acids when trypsin is used as proteolytic enzyme. Since trypsin cleaves C-terminally

after arginine and lysine residues, each peptide except for the C-terminal peptide of a given protein

contains at least one labeled amino acid (Mann 2006). To ensure the virtually complete incorporation

of labeled amino acids into proteins, cell culture conditions have to be optimized thoroughly. For

example, a typical problem encountered in such experiments is that labeled amino acids are

metabolically converted to other amino acids. In eukaryotes, mostly arginine is converted to proline

(Ong et al. 2003). This conversion has to be prevented in order to achieve easily interpretable and

quantifyable isotopic patterns in MS experiments.

SILAC can be performed using either two or three different labeling regimes. With the establishment

of triple labeling SILAC experiments, the relative quantitative comparison of proteins from multiple

conditions became possible and furthermore, quantitative changes over time could be accurately

monitored. Such time-resolved SILAC experiments in combination with phospho-specific sample

preparation and high-resolution MS analyses provide an effective quantitative proteomics approach

to gain new insight(s) into established signaling cascades as well as for the analysis of so far unknown

signaling events (Mann 2006).

Materials and Methods

23

6 Materials and Methods

6.1 Reagents and Consumables

Reagents

Acetic acid Normapur/VWR

Acetonitrile (ACN) BioSolve

Acrylamide (electrophoresis grade) BioRad

Acrylamide solution (37.5 : 1) Serva

Agarose (low melt, preparative grade) Merck

Amberlite (ion exchanger) Serva

Ammonium bicarbonate Fluka/Sigma-Aldrich

Ammonium dihydrogen phosphate Fluka/Sigma-Aldrich

Ammonium hydroxide (30 %) J.T.Baker

Ammonium persulfate (APS) BioRad

Ammonium sulfate J.T.Baker

Ampholine Amersham

Arginine Sigma-Aldrich

Arginine (13C6 and 13C615N4 labeled) euriso-top

BisTris AppliChem

Bradford staining solution BioRad

Bromphenol blue Serva

Dihydroxybenzoic acid (DHB) Fluka/Sigma-Aldrich

Dithiothreitol (DTT) AppliChem

ESI tuning mix Agilent Technologies

ProteoMass ESI positive mode calibration mix Sigma-Aldrich

Ethylendiamine Merck

Ethylendiamine tetraacetat (EDTA) Merck

Fetal bovine serum (FBS) Gibco/Invitrogen

Fetal bovine serum dialysed (dFBS) Gibco/Invitrogen

Formic acid (FA) Normapur/VWR

Glycerine Baker

β-Glycerophosphate Merck

Glycin Applichem

Heptafluorobutyric acid (HFBA) Fluka/Sigma-Aldrich

β-Ionone


24

L-Glutamine Invitrogen

Lysine Sigma

Lysine (4D and 13C615N2 labeled) euriso-top

Marker, Mark12™ Invitrogen

β-Mercaptoethanol

Methanol J.T.Baker

3-(N-morpholino)propanesulfonic acid

Nitrogen (5.0) Linde Gas

Octansulfonic acid (OSA) MP Biomedicals

Penicillinium/Streptavidin (Pen/Strep)

Pharmalyt (5-8) Amersham

Pharmalyt (4-6.5) Amersham

Phosphoric acid J.T.Baker

Piperazine – diacrylamid (PDA) BioRad

Potassium chloride Normapur/VWR

Potassium dihydrogen phosphate Normapur/VWR

Proline Sigma

ProQ staining solution Molecular Probes/Invitrogen

Reserpine solution Agilent Technologies

RPMI 1640 medium GIBCO® Invitrogen

RPMI SILAC medium PAN Biotech GmbH

Sephadex G- 75 Amersham

Servalyte Serva

Servalyte (6 – 9) Serva

Sodium acetate J.T.Baker

Sodium dodecylsulfate (SDS) Applichem

Sodium fluoride Fluka/Sigma-Aldrich

Sodium orthovanadate (OV) Fluka/Sigma-Aldrich

Sodium pyrophosphate Fluka/Sigma-Aldrich

Thiourea J.T.Baker

Trifluoracetic acid (TFA) Merck

Tris-base Sigma

Tris-HCl AppliChem

Trypsin (sequence grade) Promega

N,N,N,N-Trimethylethylenediamine (TEMED) BioRad

Urea BioRad or J.T.Baker


25

Table 1: Standard proteins and peptides used in this work are listed. Bold S and Y indicate phospho-serine

and phospho-tyrosine respectively.

Peptide/Protein Description Source

Glucose oxidase from Aspergillus niger

Sigma Aldrich Albumin Bovine, 99 %

α-Casein (S1 and S2) from bovine milk, 70 %

Myoglobin From equine skeletal muscle 95-100 %

1211 APPDNLPSPGGSR

Donated by Karl Mechtler

1212 APPDNLPSPGGSR

1448 ENIMRSENSESQLTSK

1449 QLGEPEKSQDSSPVLSELK

1451 KFLSLASNPELLNLPSSVIK

1461 SVSDYEGK

1462 THILLFLPKSVSDYEGK

GluFib EGVNDNEEGFFSAR

1463 YEEIQ

1464 DYVPML

1467 YYYEI

1142 PQEFSSVERGR

1145 SSTRSHEYGRK

1544 KAVYNFATM

1895 GGGGKFAGAQLEDGR

Consumables

C8 discs for plugs 3M, Neuss, Germany

Cell culture plastics Techno Plastic Products AG

Distal coated silica tips (FS360-20-10-D) New Objective, Woburn, USA

Gel cassettes (1.0 and 1.5 mm) Invitrogen

Oasis® HLB cartridge Waters Corporation, Milford, USA

Titansphere column TiO (5 µm) GL Science Inc.


26

6.2 Animal preparation

All procedures were in accordance with the German animal welfare act (1998). CD6 mice were held

in standard rat cages with a 12-hour light/dark cycle. Water and food was given ad libidum. Mice

were either exposed to octanal or no odorant for 10 min. Directly after exposure, mice were

sacrificed and the olfactory epithelium (OE) was prepared essentially as previously described (Spehr

et al. 2002). Briefly, the OE was dissected form the septal bone and immediately placed in ice-cooled

ringers buffer (140 mM NaCl, 5 mM KCl, 1mM MgCl, 2 mM CaCl, 10 mM Hepes, 10 mM glucose)

supplemented with protease (Roche Complete® protease inhibitor cocktail) and phosphatases

inhibitors (10 mM Glycerophosphate, 1 mM Na-orthovanadate, 9.5 mM NaF and 10 mM Na-

pyrophosphate). Dissected OE were frozen in liquid nitrogen and stored at -80 °C.

6.3 Gel electrophoresis

1D-PAGE: Bis-Tris gels (NuPAGE)

Polyacrylamid gel: 5x sample buffer:

Solution Volume

Acrylamid (37.5 : 1) 3.33 ml

7x Bis-Tris buffer (2.5 M BisTris, pH 6.5) 1.42 ml

Ammonium persulfate (APS); 40 % 12 µl

TEMED 3 µl

Water (puriss.) 3.38 ml

Gels were casted in ready-to-use plastic gel cassettes and 10 well combs were applied to the gels.

Samples were diluted with 5x sample buffer to a final volume of maximal 15 µl. Gel electrophoresis

was conducted using 400 mA and 150 V for approximately 60 min in 1x running buffer.

20x running buffer

Solution Volume

β-Mercaptoethanol 12,5 ml

SDS 5 g

Bromphenolblau 0,5 g

Glycerin 25 ml

Tris- HCl (pH 6.8; 2.5 M) 5 ml

ad. 50 ml

Solution Volume

MOPS 104.6 g

Tris-HCl 60.6 g

SDS 10 g

EDTA 3 g

Water (puriss.) ad. 500 ml


27

2D-PAGE: isoelectric focusing (IEF) according to Klose

Ampholine stock solution: Acrylamide solution SepGels and CapGels: Solution Volume

Ampholine 8 ml

Servalyte 8 ml

Pharmalyt (4 – 6.5) 24 ml

Pharmalyt (5 – 8) 16 ml

For ampholine complete solution, 7 ml of ampholine stock solution were mixed with 1 ml Servalyte

(6 – 9).

CapGel working solution: SepGel working solution:

11.88 g Urea

3.85 g Glycerin solution (28.6 % (w/v))

4.4 g AA solution CapGels

12.9 µl TEMED

2.19 g Water (puriss.)

2.2 g Ampholine complete solution

IEF gels were casted in 8 cm glass tubes with an inner diameter of 1.5 mm. SepGel and CapGel

working solutions were degassed prior to use. Sep- and CapGel solutions were mixed with 45 µl and

13 µl APS (1.2 % (w/v)), respectively. A 7 cm separation gel followed by a 2 mm capping gel was

casted in the glass tubes.

Anode buffer (top): Cathode buffer (bottom):

Gel containing glass tubes were inserted into the gel chamber (CapGel down). The gels were covered

with 2 mm of Sephadex solution (40µl swelled Sephadex, 25 µl DTT, 25 µl ampholine complete

solution, 310 mg thiourea). Urea, DTT (21.6 % (w/w)) and Ampholine solution were added to the

sample to final proportions of 50, 5 and 5 % (v/v), respectively. The sample was stacked onto the

Sephadex cushion and covered with protective solution (7.6 ml of 30 % urea/5 % glycerin (w/v) plus

0.4 ml Servalyt 2-4). Focusing was started according to the following time scheme:

Incubation solution:

Solution Volume

Acrylamide (s) 4.375 g

PDA 0.375

Water (puriss.) ad. 25 g

deionize with Amberlite

27 g Urea

8.75 g Glycerin solution (28.6 % (w/v))

10 g AA solution SepGels

29.3 µl TEMED

4.97 g Water (puriss.)

5 g Ampholine complete solution

126 g Urea

35 ml Phosphoric acid (85 %)

ad 700 ml Water (puriss.)

270 g Urea

25 g Glycerin

25 ml Ethylene diamin


Scheme for 7 cm tube gel:

100 V 75 min

200 V 75 min

400 V 75 min

600 V 75 min

800 V 10 min

1000 V 5 min

125 mM Tris-base (pH 8.6)

40 % (v/v) Glycerin

65 mM DTT

104 mM SDS

ad. 500 g Water (puriss.)


28

After the isoelectric focusing, the tube gels were outpaced from the glass tubes and covered with

incubation solution for 30 min. Finally the tube gel was washed twice with SDS gel running buffer.

2D-PAGE: second dimension SDS – PAGE

SDS gel buffer SDS gel running buffer

138 g Tris base

56.75 g Tris-HCl

1200 µl TEMED

4 g SDS


The ready-to-use acrylamide solution was mixed 1:1 with SDS gel buffer. 72 ml of this solution and

144 µl of 40 % (w/w) APS were mixed and filled into ready-to-use plastic gel cassettes (1.5 mm) up to

1 cm below the maximum fill level. The gel was immediately covered with butanol (saturated with

water) to produce an even rim. After polymerization over night, the butanol was removed by

washing twice with 1x running buffer. The washed first dimension tube gel was placed void-free upon

the second dimension SDS gel and the cassette was filled with warm 1% agarose (in running buffer,

supplemented with bromphenol blue). Electrophoresis was carried out using the stated buffers at

400 mA and 150 V for approximately 60 min.

6.4 Staining procedures

Colloidal Coomassie staining

Protocol for Colloidal Coomassie staining

step buffers time

fixation 50 % methanol

2% phosphoric acid over night

wash water puriss.) 3 x 30 min

(3 x 10 min for minigels)

incubation

34 % methanol 2 % phosphoric acid

17 % ammonium sulfate

1 h (30 min for minigels)

staining Add a small amount of

coomassie 250 G

over night or up to 3 days (for

minigels 2 h sufficient)

destaining Water (puriss) Until desired destaining

720 g Glycin

151.4 g Tris base

50 g SDS

ad. 5000 ml Water (puriss.)


29

- For incubation and staining buffers add phosphoric acid and some water first. Then add

ammonium sulfate and mix thoroughly. Make sure the ammonium sulfate is completely

dissolved before adding methanol. Add water to the final volume.

ProQ staining

step buffers time

fixation 50 % methanol 10 %

acetic acid Change solution after 30 min,

second fixation over night

wash water (puriss.) 5 x 15 min (10 min for minigels)

staining ProQ stain 1.5 – 2 h, in the dark

destaining 20 % acetonitrile, 50 mM sodium acetate, pH 4.0

3 x 30 min

wash water (puriss.) 4 x 10 min

(if high background staining is observed, 20 - 30 min extra)

- Carry out all steps with slight agitation.

- Adhere strictly to the times specified in the protocol

- Use high density plastic containers.

- Use containers dedicated exclusively for ProQ staining.

- Clean containers thoroughly with ethanol before use.

To visualize proteins stained with the fluorescent Pro-Q® dye, gels were scanned on a Typhoon Trio

scanner (GE Healthcare, Munich, Germany) with an excitation wavelength of 532 nm (max. excitation

wave length Pro-Q® stain: 555 nm). Following staining with the Pro-Q® dye, all proteins in Pro-Q

stained gels, irrespective of their phosphorylation status, were observed to emit minor amounts of

light when scanned at an excitation wave length of 633 nm. This phenomenon was utilized to

visualize the total protein content as a loading control.


30

6.5 Chromatography

Reversed-phase separation: Ultimate classic system

Online reversed-phase capillary HPLC separations for mass spectrometric measurement using the Q-

Star XL (Applied Biosystems), Q-Trap 4000 (Applied Biosystems), LCQ XP (Thermo Fisher) and HCT

plus (Bruker Daltonik) instruments were performed using the Dionex LC Packings HPLC systems

(Dionex LC Packings, Idstein, Germany) as described previously (Schaefer et al. 2004). Briefly, the

HPLC system was composed of the components Famos (autosampler), Switchos (loading pump and

switching valves), and Ultimate (gradient pump and UV-detector). Peptides were loaded online and

preconcentrated on a C18 precolumn (0.3 mm i.d. × 5 mm, 5 μm; Dionex LC Packings) with 0.1 % TFA

at a flow rate of 30 μL/min for 6 min. Thereafter, the precolumn was switched in line with a Pep-Map

column (75 μm i.d. × 250 mm, 5 μm, Pep-Map; Dionex LC Packings) for reversed-phase (RP)

separation. A solvent system consisting of solvent A (0.1% (v/v) formic acid (FA)) and solvent B (0.1%

(v/v) formic acid; 84% (v/v) acetonitrile (ACN)) was used and flow rates were adjusted to 250 nL/min.

A gradient of 5 - 50% solution B in 35 min was carried out using the Ultimate system. The column was

washed for 5 min with 95 % solvent B and subsequently reequilibrated for 20 min with 95 % solvent

A. During separation, the second precolumn was washed with 50 % (v/v) ACN/0.1 % (v/v)

trifluoroacetic acid (TFA) for 20 min and 84 % (v/v) ACN/0.1 % (v/v) TFA for 10 min using the isocratic

loading pump. RP separations were carried out at room temperature.

Reversed-phase separation: U 3000 system

All samples from strong cation exchange (SCX) fractions and TiO2 affinity chromatography were

separated by nano HPLC-using the UltiMateTM 3000 HPLC system (Dionex LC Packings, Idstein,

Germany) online coupled to an LTQ Orbitrap XL instrument (Thermo Fisher Scientific, Bremen,

Germany) or to a HCT Ultra PTM instrument (Bruker Daltonics, Bremen, Germany). RP capillary HPLC

separations were performed as described previously (Schaefer et al. 2004) with slight modifications.

In brief, peptide mixtures were loaded onto one of the two C18 precolumns (0.3 mm inner diameter

x 5 mm; PepMap, Dionex LC Packings) equilibrated with 0.1 % (v/v) TFA, washed and

preconcentrated for either 20 min (SCX fractions) or 40 min (eluates from TiO2 affininty

chromatography) with the same solvent at a flow rate of 30 µl/min using the loading pump. The

precolumn was then switched in line with a C18 RP nano LC column (75 µm inner diameter x 150

mm; PepMap, 3µm, Dionex LC Packings). For peptide separations, a binary solvent system consisting

of 0.1% (v/v) FA (solvent A) and 0.1% (v/v) FA/84% (v/v) ACN (solvent B) was applied. For SCX


31

fractions and TiO2 eluates a gradient of 5 - 50% solvent B in 65 min and 45 min respectively, was used

at a flow rate of 300 nl/min. In both cases the gradient was continued as follows: 50 - 95% solvent B

in 2 min; the nano LC column was washed for 5 min with 95 % solvent B and equilibration of the

column with 5 % solvent B was carried out for 20 min.

Simultaneous with the peptide separation, the precolumn not in line with the nano LC column was

washed using a linear gradient of 0.1 % (v/v) TFA to 0.1% (v/v) TFA/84 % (v/v) ACN in 20 min. The

solvent composition was kept at 0.1 % TFA/84 % ACN for 10 min before reequilibration of the

precolumn with 0.1 % TFA. RP separation was carried out at 25 °C.

Strong Cation Exchange (SCX) Chromatography

200 µg of lyophilized tryptic peptides were redissolved in SCX buffer A (5 mM potassium dihydrogen

phosphate, 25% ACN (v/v), pH 2.7) and entirely loaded onto a Polysulfoethyl-Asp column (i.d. 1 mm,

15 cm, 5 µm, 300 Å, Dionex/LC Packings, Amsterdam, Netherlands) equilibrated with SCX buffer A

using a Dionex LC Packings Ultimate classic HPLC system (see page 26) without flow splitter and a

CMA 470 fraction collector (CMA Microdialysis AB, Solna, Sweden). The peptides were eluted from

the column with a flow rate of 50 µl/min using a linear gradient of 0 – 50 % SCX buffer B (5 mM

potassium dihydrogen phosphate, 25% ACN (v/v), 500 mM KCl, pH 2.7) in 70 min, followed by a

gradient of 50 – 100 % solvent B in 10 min. The column was washed with 100 % B for 10 min and re-

equilibrated with buffer A for 30 min. 32 peptide fractions of 4 min each were collected throughout

the gradient. 15 µl of each fraction were pipetted in a glass vial, dried in vacuo and stored at – 80 °C.

The remainder of the fractions (185 µl) was subjected to TiO2 chromatography for enrichment of

phosphopeptides.

6.6 Mass spectrometry

In the work presented here, a great variety of mass spectrometric instruments was used.

Phosphoproteomic analyses were generally conducted on the LTQ Orbitrap and the HCT Ultra PTM

instrument. Thus, mass spectrometric techniques performed on these thwo instruments are

described in detail, whereas for the other mass spectrometers the relevant parameters are stated

briefly. All mass spectrometers were equipped with the respective nano-electrospray ionization (ESI)

source and distal coated SilicaTips.


32

Q-Star XL (Applied Biosystems, Foster City, CA)

External calibration was routinely performed on a daily basis using reserpine (m/z 609.280) and two

of its fragments (m/z 174.100 and 195.065). The general parameters were as follows: ion spray

voltage: 1,800-2,000 V; curtain gas: 10-14 l/min; gas 1.0; declustering potential: 50 V; focusing

potential: 220 V; declustering potential 2: 15 V; Software for acquisition: Analyst QS 1.1; survey scan:

m/z 400 – 1,200; top 3 enhanced product ion scan: m/z 100 – 2,000; exclude singly charged ions;

rolling collision energy: on; collision gas: nitrogen (purity 5.0); dynamic exclusion for 16 s; peaklists

were generated using the Analyst QS 1.1 software with default parameters.

Q-Trap 4000 (Applied Biosystems, Foster City, CA)

Needle voltage: 2,400 – 3,200 V; interface heated to 150 °C; curtain and collision gas: nitrogen (purity

5.0). Scan cycle: one MS survey scan (EMS, m/z 400-1,400 at 4,000 amu/s) plus enhanced resolution

scan (ER) of the three most intense ions (at a scan rate of 250 amu/s), up to three MS/MS scans (EPI,

m/z 100-1,750 at 4000 amu/s); Peaklists were generated using Analyst v.1.4 with default parameter

settings.

LCQ DECA XP (Thermo Fisher Scientific, Bremen, Germany)

Capillary temperature: 250 °C; spray voltage 2.0 – 2.2 kV; capillary voltage: 42 V; lense offset: 30 V;

maximal fill-time: 200 ms; automatic gain control: 107; MS scan range: m/z 500 – 2,000, MS/MS scan

range: m/z 200 – 2000; dynamic exclusion for 3 min.

HCT plus (Bruker Daltonik, Bremen, Germany)

The instrument was externally calibrated with standard compounds (ESI Tuning Mix, Agilent

Technologies). The general mass spectrometric parameters were as follows: capillary voltage:

1,400 V; plate offset: 500 V; dry gas flow: 10.0 l/min; dry gas temperature: 160 °C; aimed ion charge

control (ICC) 150000; maximal fill-time 500 ms; software: Compass 1.2; fragmentation amplitude

0.6 V; MS spectra were a sum of seven individual scans ranging from m/z 300-1,500 with a scanning

speed of 8,100 (m/z)/s; and (ii) MS2 spectra were a sum of four scans ranging from m/z 100 – 2,200

at a scan rate of 26,000 (m/z)/s.


33

LTQ – Orbitrap XL (Thermo Fisher Scientific)

The LTQ-Orbitrap XL was externally calibrated using standard compounts. To provide very high mass

accuracy, lock masses (derived from a set of distinctive air contaminants) were routinely used for

internal calibration of each MS spectrum acquired. The general mass spectrometric parameters were

as follows: spray voltage: 1.7 - 2.0 kV; capillary voltage: 45 V; capillary temperature: 200 °C; tube lens

voltage: 100 V. For data-dependent MS2 analyses, the software Bioworks 3.3.1 SR 1 (Thermo Fisher

Scientific, Bremen, Germany) was used. MS spectra ranging from m/z 300 to 1,500 were acquired in

the Orbitrap at a resolution of 30,000 (at m/z 400). Automatic gain control (AGC) was set to 5 x 105

ions and a maximum fill time of 750 ms. After a brief survey scan, the four most intense multiply

charged ions were selected for low energy CID in the linear ion trap concomitant with the completion

of the MS scan in the Orbitrap. The AGC of the LTQ was set to 10,000 ions and a maximum fill time of

100 ms. Fragmentation was carried out at a normalized collision energy of 35% with an activation q

of 0.25, an activation time of 30 ms and with the multistage activation option for neutral losses of 98

Da (+2, loss of two H3PO4), 49 (+2, loss of one H3PO4) and 32.3 (+3, loss of one H3PO4). The

fragmentation of previously selected precursor ions was dynamically excluded for the following 45

sec.

HCT Ultra PTM (Bruker Daltonik, Bremen, Germany)

The HCT Ultra PTM Discovery SystemTM (Bruker Daltonics, Bremen Germany) was externally

calibrated using standard compounds. The general mass spectrometric parameters were as follows:

capillary voltage: 1400 V, plate offset: 500 V; dry gas flow: 8.0 l/min; dry gas temperature: 160 °C;

aimed ion charge current (ICC): 200000, maximum fill time: 500 ms. The instrument was operated

using the EsquireControl 6.2 (Bruker Daltonics) software. The three most intense multiply charged

peptide ions were chosen for fragmentation by CID. Exclusion limits were automatically placed on

previously selected m/z ratios for 1.2 min. The precursor masses of peptide ions with a detected

neutral loss of phosphoric acid (m/z 49.0 for doubly charged ions and m/z 32.7 for triply charged

ions) were automatically selected for fragmentation by electron transfer dissociation (ETD).

Parameters for low energy CID and ETD were as follows: scan averaging: 5; scan speed for MS:

26,000 m/z per sec; MS scan range: m/z 300 – 1,500; scan speed for MS/MS: m/z 8,100; scan range

for MS/MS: m/z 100 – 2,800; fragmentation amplitude: 0.5 V; ICC target for ETD reactant: 600,000;

max. accumulation time of ETD reactant: 200 ms; reaction time: 100 ms; smart decomposition for

z = 2.


34

6.7 Cell culture

LNCaP cells were cultured in RPMI 1640 medium (supplemented with 10 % FBS, 1 % Pen/Strep) in 75

cm2 flasks at 37 °C and with 5% carbon dioxide. Cells were passaged once a week and cell culture

medium was changed an additional time during the week. For global phosphoproteomic

experiments, cells were seeded in conventional RPMI medium on 87 mm dishes (Techno Plastic

Products AG, Trasadingen, Switzerland) to a confluency of ~ 90 %. For SILAC experiments, cells were

seeded in 75 cm2 flasks in the respective RPMI-SILAC medium (supplemented with 10 % dialyzed FBS,

1 % Pen/Strep, 1 % glutamine, 800 mg/l proline, 120 mg/l arginine and 120 mg/l lysine). In light SILAC

medium non labeled amino acids were used. For medium SILAC labeling 13C6-arginine and D4-lysine

and for heavy SILAC labeling 13C615N4-arginine and 13C6

15N2-lysine were used. Cells were cultured in

the respective SILAC medium for at least two weeks to ensure complete labeling with medium or

heavy SILAC amino acids. 5 – 6 days before the actual stimulation experiment was performed, cells

were seeded in one 87 mm and one 30 mm dish per SILAC state. Cells were cultured up to ~ 90 %

confluency for the 87 mm dishes and up to ~ 60 % confluency for the 30 mm dishes. The latter were

used for calcium imaging experiments.

6.8 Cell migration assays

Migration assays were performed at the Department of Cell Physiology at the Ruhr-University

Bochum essentially as stated in (Gelis 2009). Briefly, 5 x 103 LNCaP cells were seeded in 200 µl RPMI

medium without FBS on top of non-coated polyethylene terephtalate (PET) membranes of transwell

culture inserts, 8 µm pore size (BD Biosciences, Heidelberg, Germany). The cells were incubated for

24 h under standard conditions. Then, the bottom chamber was filled with either the same medium

as was used in the upper chamber as a negative control or RPMI medium containing 20 % FBS as a

positive control. Furthermore, to test the influence of β-ionone on cell motility, four treatment

regimes were executed (Table 2).

Table 2: Treatment regimes of LNCaP cells for migration assays.

1 2 3 4

Upper chamber 500 mM β-ionone - - 500 mM β-ionone

Bottom chamber

20 % FBS 500 mM β-ionone

+ 20 % FBS 500 mM β-ionone -

Experiment Positive control Positive control +

β-ionone Negative control

Negative control + β-ionone


35

After 24 h cultivation, the cells were fixed in 4 % paraformaldehyde in PBS, stained for 5 min with

Sytox Green (Invitrogen), and washed three times with 2 ml of tap water for 15 min, respectively.

Cells that had remained on top of the membrane were wiped away. Cells that had migrated to the

bottom side of the membrane were visualized under the microscope and quantified by counting the

number of cells in three randomly chosen visual fields. The assay was performed in quadruplicate per

condition and in two independent replicates.

6.9 Phosphoproteomics sample preparation

All steps in phosphoproteomics sample preparation have to be performed swiftly and at 4 °C,

especially before (phospho)proteins are digested

For global phosphoproteomics experiments, cells were treated with 3 mM activated ortho-vanadate

for 10 min to accumulate phosphorylated proteins in the cell and thus, to produce a highly

phosphorylated sample. In order to analyze the effects of prostate-specific G-protein coupled

receptor (PSGR) activation, cells were treated for 0, 2 and 10 min with its known ligand β-ionone

diluted in cell culture medium to a final ration of 1:10000 (v/v). To determine the treatment and

arrest the stimulation effect, cells were placed on ice and washed twice with ice cold PBS. 500µl ice

cold lysis buffer (7 M urea, 2 M thiourea, 1 mM sodium orthovanadate, 10 mM β-glycerophosphate,

9.5 mM sodium fluoride, 10 mM sodium pyrophosphate) were added, cells were scraped from the

dish and further disrupted using a Dounce glass homogenizer. Insoluble material was removed by

centrifugation for 15 min at 13.000 rpm and 4 °C. The protein concentration was estimated using the

Bradford assay (BioRad, Munich, Germany). A lysate volume corresponding to 1 mg of protein was

diluted 1 : 4 with 10 mM ammonium bicarbonate solution and digested with 20 µg of sequencing

grade trypsin (Promega, Mannheim, Germany) for 3 h at 42 °C. The resulting peptide sample was put

on ice for 5 min and acidified with TFA to a final concentration of 1 %. An Oasis® HLB cartridge was

conditioned by applying 2x 2 ml of elution buffer (5 % FA, 90 % ACN) and 2x 2 ml water (puriss.). The

tryptic digest was loaded slowly onto the cartridge and the flow through was loaded onto the

cartridge once more. Bound peptides were washed using 1x 3 ml washing buffer (0.1 % TFA) and 2x

2 ml water (puriss.). Peptides were eluted from the cartridge with 2 ml elution buffer. The eluates

were divided into aliquots, lyophilized and stored at -80 °C. The exact peptide concentration was

most accurately determined by amino acid analysis using the Waters Alliance 2695 HPLC/

Millenium32 software and AccQ-Fluor™ reagent for labeling (Waters Gmbh, Eschborn, Germany)

according to the manufacturer’s protocol.


36

6.10 Titanium dioxide affinity purification

Enrichment of phosphorylated peptides from complex samples was performed essentially as

described before (Mazanek et al. 2007; Schmidt et al. 2008) with slight modifications. Briefly, TiO2

material from a Titansphere column (TiO, 5µm, GL Science Inc., Tokyo, Japan) was resuspended in

equal volumes of ACN and 30µl of the suspension were placed in a 1.5 ml Eppendorf vial. TiO2

material was pelleted by centrifugation for 1 min at 13,000 rpm and the supernatant was discarded.

All centrifugation steps during the enrichment were carried out at 4 °C. The TiO2 material was then

washed twice with 200 µl washing buffer (80 % ACN, 0,1 % TFA) and once with 50 µl of loading buffer

(20 % acetic acid (v/v), 20 mg/ml dihydroxybenzoic acid (DHB), 420 mM octansulfonic acid (OSA),

0.1 % HFBA (v/v)). Peptide fractions from SCX chromatography were mixed 1:1 (v/v) with 2x loading

buffer, added to the prepared titanium dioxide material, vortexed and incubated for 20 min at 4 °C

with slight agitation. Samples were washed twice with 50 µl washing buffer. Subsequently, 50 µl

elution buffer (50 mM ammonium dihydrogen phosphate adjusted to pH 10.5 with ammonium

hydroxid solution) were added to each sample followed by incubation for 10 min. After adding 50 µl

ACN to the samples, the resulting suspensions were loaded onto a 200 µl pipette tip supplied with a

C8 plug (3M, Neuss, Germany) and peptides were eluted by applying pressure with a syringe. Eluates

were put on ice, acidified with 8 µl TFA and solvent was removed in vacuo.

6.11 Bioinformatics

Analyses of MS data derived from protein and peptide standards and from proteins of the

mouse olfactory epithelium.

For MS/MS data accuired on the Q-Star, Q-Trap or the HCT Plus mass instrument, the respective peak

lists were uploaded to ProteinScape 1.3. Protein identification was performed using two search

engines: Mascot v.2.0.04 ((Perkins et al. 1999); www.matrixscience.com) and SEQUEST

(TurboSEQUEST - PVM Slave v.27 (rev. 12),(Eng et al. 1994)). Peaklists were correlated with the IPI

mouse database v.3.15 (68,236 protein entries) for mouse olfactory epithelium samples and the NCBI

database (version 2006) for standard proteins. In general, proteins were considered as being

confidently identified, if at least two peptides were identified by both search engines and with

protein MASCOT scores higher than 90 or Sequest scores higher than 10.


37

Peak lists of standard peptide were searched using Mascot in combination with a small database

containing only the sequences of the available standard peptides. The relative intensities of the

standard peptides identified in each sample were determined using the XCalibur software v1.4.

Qualitative phosphoproteomics data analysis

Protein identification was performed by correlating peaklists to an IPI_human_decoy database using

the Mascot search engine v.2.2. The IPI_human_decoy database (shuffled) was generated from the

IPI human database v.3.61 (82,631 proteins) using an in-house built software (Reidegeld et al. 2008).

Peaks lists from low resolution MS measurements on the HCT instrument were generated using the

Data Analysis 4.0 (Built 234) software (Bruker Daltonics, Bremen Germany) with default settings,

thereby producing separate peaklist files (.mgf format) for CID and ETD spectra. Peaklists were

uploaded to the ProteinScape 1.3 platform (Bruker Daltonics, Bremen Germany) and database

searches were performed using the following settings: tryptic specificity with max. 2 missed

cleavages; variable modifications: oxidized methionine and phosphorylation on serine, threonine and

tyrosine; precursor mass tolerance of 1.2 Da and fragment mass tolerance of 0.3 Da. Searches for

ETD and CID data were performed separately with the specific fragmentation technique marked in

the search parameters. Subsequently proteins were assembled based on peptide sequence

information retrieved from CID and ETD spectra using the Protein Extractor tool within ProteinScape

1.3. For protein identification, only peptides with a Mascot score of 20 or higher were considered.

The resulting protein lists were cropped to give a false discovery rate (FDR) of 5%. In order to provide

a measure for the correctness of phosphorylation site assignment, the SLoMo tool was employed

(Bailey et al. 2009). Mascot result files were exported in pepxml format and all entries with Mascot

Scores smaller than 20 were marked as not identified for further analyses using an in-house modified

export routine. Pepxml files were processed with SLoMo allowing a mass tolerance of 300 ppm.

Simple phosphorylation site assignments were generally classified as confident sites. Phosphorylation

site assignment in peptides with multiple possible localizations was only classified as confident with a

SLoMo Score of at least 19. Both, protein and peptide identification results and results of

phosphorylation site assignment using SLoMo were submitted to the PRIDE database (Vizcaino et al.

2009); Pride accession number 13396-13397).

Peaklists from high resolution measurements on an LTQ-Orbitrap instrument were generated using

the Bioworks software v.3.3.1 for .dta file creation, which were subsequently merged to a single .mgf

file by a pearl script (merge.pl, www.matrixscience.com/help/instruments_xcalibur.html). The

resulting .mgf files were uploaded to ProteinScape 1.3 and searched using Mascot applying with the

same parameters as described abouve for low resolution data, but with a precursor mass tolerance

of 4 ppm and a fragment mass tolerance of 0.4 Da. A FDR of 5 % was applied to resulting protein lists.


38

For the global study of phosphoproteomces, high resolution LTQ-Orbitrap data was evaluated using

the software MaxQuant 1.0.13.8 (Cox and Mann 2008). Data analysis was performed with default

settings unless otherwise stated. For database searches the following settings were applied:

precursor mass tolerance: 4 ppm; fragment mass tolerance: 0.4 Da; tryptic specificity with max. 2

missed cleavages; variable modifications: oxidized methionine and phosphorylation on serine,

threonine and tyrosine. A FDR of 1% was applied at the modified peptide, peptide and protein level

and the “keep low scoring peptides” option was discarded. Only phosphorylated peptides with a PEP

value <0.1 were considered as being confidently identified and the site of phosphorylation was

classified correct, if the Localisation Probability Score was at least 0.75.

Quantitative phosphoproteomics analysis: MaxQuant and XICs

Data evaluation was done using the software MaxQuant 1.0.13.13 , generally with the same settings

as before (this page) , but with slight modifications concerning the quantification of SILAC triplets: In

the Quant module SILAC label was set to “Triplets” and up to 3 labeled amino acids were permitted.

In the Identify module the “use least modified peptide” option was selected for site quantification.

The phosphopeptides, identified and quantified by MaxQuant were classified according to both their

Pep values and regulation factors. Peptides exhibiting a pep value lower than 0.01 and a regulation

factor (M/L and H/L) greater or smaller than the cut-off values determined by Box-plot analysis (see

Chapter 5.4.3) were chosen as candidate peptides.

The MS spectra of phosphopeptides, not been quantified by MaxQuant were checked manually. Non

C-terminal peptides, which seemed to be regulated, were included in the candidate list. Each

regulated phosphopeptides was at least identified in one experiment. The exact monoisotopic mass-

to-charge (m/z) ratios of different charge states observed in this replicate were noted for each light

labled peptide, together with the observed retention time in liquid chromatography and the SCX

fraction in which it was identified.

For all considered m/z values, the exctracted ion chromatograms (XICs) of the first three isotopic

peaks of each SILAc state were calculated with a mass accuracy of m/z 0.05. This was done in the raw

files of up to 5 TiO2 eluates corresponding to the respective SCX fractions for the replicates in which it

has not been identified. If the exact m/z value and an overlay of all nine XICs were observed in a 4

min time window around the formerly observed retention time, the corresponding MS spectrum was

manually inspected to ensure that the picked m/z was indeed the monoisotopic peak of the light

SILAC signal, that the charge state of the cluster was correct and that the distances between the

three SILAC clusters corresponded to the number of arginines and lysines present in the

corresponding peptide. If these criteria were fulfilled, the observed SILAC triplet was considered as


39

being present in the respective raw file, even without MS/MS identification. For these SILAC triplets

quantification was performed by integrating the calculated XICs and adding the values for the three

XICs belonging to the same SILAC state.

For positive hits, XICs were integrated and the area under the curve was summed up for the three

XICs belonging to the same SILAC state. From this, both the medium to light (M/L) and the heavy to

light (H/L) ratios were calculated. To correct for mixing errors, a correction factor was calculated

according to equations (1)-(4):

Exemplary calculation of correction factor for M/L ratios

Assumption: µ =

∑

(1)

The observed average ratio µ for correct 1:1 mixing would be µ = 0. As this is not the case, a

correction factor x was calculated as follows:

0 =

∑

(2)

0 = lnx + µ (3)

x = (4)

µ = average ratio n = number of intensity values available for given experiment M = intensity value for medium labeled peak L = intensity value for light labeled peak x = correction factor

The average M/L and H/L ratio for all three experiments was calculated using the intensities for all

labeling states given in the evidence.txt table generated by MaxQuant (Supplementary table SP 24).

These average ratios (µ) were used to calculate correction factors x and accordingly, to calculate the

normalized XIC ratios. If more than one XIC ratio was determined for one peptide (e.g. for different

charge states), a weighted average was calculated (equation 5 - 7).

wj =

(5)

S = M1 + L1 + M2 + L2 + …. + Mj + Lj (6)

ř = ∑ (7)

wj = weighting factor j = number of XIC values calculated for one peptide r = normalized ratio ř = weighted average ratio S = sum of all M and L XIC values available for one peptide


40

K-means clustering

Normalized XIC profiles were partitioned into k clusters of similar shape using K-means clustering

within the Statistica 9.0 software package (StatSoft Inc., Tulsa, USA). Profiles comprising “missing

values” were excluded from clustering. K as the number of clusters to calculate was set to 9. The

initial cluster centers were distributed equally across all profiles after sorting according to profile

distances. Each profile was assigned to the cluster with smallest distance between itself and the

cluster centre. In subsequent iteration steps, cluster centers were moved according to the previously

assigned cluster members, leading to new assignments of cluster members. Convergence was

reached if cluster membership and thus cluster centers did not change anymore. However,

convergence may not have been reached for all clusters. In this case a maximum number of 20

iterations were allowed.

Kinase motif prediction

For peptides with confidently identified phosphorylation sites, kinase motif prediction was

performed using the freely available tools NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/;

(Blom et al. 2004)) and KinasePhos (http://kinasephos.mbc.nctu.edu.tw/;(Huang et al. 2005)).

NetPhosK was executed using default parameters. The prediction with the highest score above the

threshold of 0.5 was assigned as kinase motif prediction. KinasePhos was also used with default

settings and the best hit for the respective phosphorylation site with a positive Bit Score was

accepted.

IPA analysis

The IPI accession numbers of regulated phosphoproteins have been imported to IPA Pathway

analysis (Ingenuity Systems Inc., Redwood City, CA, USA; http://www.ingenuity.com) and a core

analysis was performed. By that, the imported proteins were classified into five protein networks. For

these networks, the major functions were given by IPA according to literature information.

PhosphoSite database search

All phosphorylation sites identified confidently in the global phosphoproteomics experiment were

listed in a .csv file in the format: XXXXXXS/T/YXXXXXX together with the species used (human). Then

these sequences were subjected to a bulk sequence search using the PhosphoSitePlus (PSP) database

(http://www.phosphosite.org). This search was performed at the 20th of May. All phosphorylation

motifs declared as unknown by this search, were, together with the protein information available for

the respective peptide, doublechecked against the UniProt database (http://www.uniprot.org). If

Results

41

both databases contained no entry for the respective phosphorylation site it was considered

unknown.

Results

42

7 Results

7.1 Pro-Q® Diamond Phosphoprotein Gel Stain

The effects of octanal treatment on the protein composition of the olfactory epithelium (OE) have

been quantitatively studied by two dimensional differential gel electrophoresis (2-D DIGE) (Barbour

et al. 2008). To understand how the observed long term changes in protein abundance are procured

by the activation of olfactory receptor proteins, it is necessary to observe the early events after

receptor activation. Receptor activated signaling cascades are mostly mediated by protein

phosphorylation or dephosphorylation events (Alberts 2002). In this work, 2-D gel electrophoresis

(GE) was employed in combination with a phosphorylation specific fluorescent dye, the Pro-Q®

Diamond (Pro-Q) stain, in order to differentially detect changes in protein phosphorylation upon

olfacory receptor (OR) activation using octanal. To this end, the applicability of the Pro-Q stain was

tested using standard proteins.

Fig. 7: Pro-Q® Diamond stain. 30 µg of each standard protein were separated by 1-D gel electrophoresis and

stained with colloidal Coomassie (A) or Pro-Q® Diamond (B, C). Following Pro-Q staining, the gel was scanned

for phosphospecific fluorescence at a wavelength of 532 nm using the CY3 channel (B) and for total protein

visualization at a wavelength of 633 nm using the CY5 channel (C).

A B C

55 kDa

200 kDa

116 kDa

97 kDa

66 kDa

36 kDa

31 kDa

21 kDa

14 kDa

Glu

co

se

ox

idas

e

My

og

lob

in

Alb

um

in

α-C

as

ein

Ma

rke

r

Glu

co

se

ox

idas

e

My

og

lob

in

Alb

um

in

α-C

as

ein

Ma

rke

r

Glu

co

se

ox

idas

e

My

og

lob

in

Alb

um

in

α-C

as

ein

Ma

rke

r

Results

43

In order to investigate the applicability of the Pro-Q®Diamond stain to the specific detection of

phosphorylated proteins, the four standard proteins glucose oxidase, myoglobin, albumin and α-

casein were separated by 1-D GE and stained either with colloidal Coomassie or with Pro-Q®Diamond

(Figure 7). Pro-Q stained gels were scanned at an excitation wave length of 532 nm (CY3 channel,

Typhoon Trio Scanner, GE Healthcare, Munich, Germany). The Pro-Q®Diamond stain showed very

high specificity for α-casein, representing an highly phosphorylated standard protein, whereas no

significant fluorescence was observed for the non-phosphorylated proteins glucose oxidase,

myoglobin, and albumin (Figure 7 B). As a control, the standard proteins were additionally visualized

by colloidal Coomassie and the Pro-Q stain at a wavelength of 633 nm (CY5 channel). All proteins

were well separated and detected on the 1-D gel.

After visualization with colloidal Coomassie or Pro-Q staining, the protein bands were excised,

digested in the gel matrix using trypsin, and the resulting peptides were analyzed by nano-HPLC/ESI-

MS/MS.

Table 3: Identification of standard proteins. The standard proteins glucose oxidase, myoglobin, albumin and α-

casein were separated by 1-D gel electrophoresis and stained either with colloidal Coomassie or with Pro-Q®.

The individual protein bands were then excised, digested in the gel matrix using trypsin, and the resulting

peptides were analyzed by nano-HPLC/ESI-MS/MS using an ion trap (HCTplus), triple quadrupole (4000 QTRAP),

and quadruple time-of-flight (QSTAR XL) instrument. Protein identification was performed by database

searches using the Mascot and Sequest algorithm.

Mass spec Seq.

Coverage Peptide count

Mascot Score

Sequest Metascore

Glucose oxidase

ProQ HCT 35.7 16 911.1 104.2

Coomassie HCT 39.8 23 1213 155.2

ProQ QTRAP 19.7 11 569.5 100.2

Coomassie QTRAP 35 20 916.4 163.7

ProQ QSTAR 16.5 6 163.2 15.8

Coomassie QSTAR 39.8 17 1213 155.2

Myoglobin

ProQ HCT 45.8 9 370.8 45.6

Coomassie HCT 66 11 614 70.8

ProQ QTRAP 23.4 2 72.8 13.9

Coomassie QTRAP 33.3 5 252.8 49.8

ProQ QSTAR 20.6 2 77.7 7.8

Coomassie QSTAR 17 2 69.1 8

Albumin

ProQ HCT 21.6 16 715.1 64.1

Coomassie HCT 36.4 29 1234.1 139.2

ProQ QTRAP 18.6 12 518.9 86.1


ProQ QSTAR 9.4 6 134.7 12

Coomassie QSTAR 23.2 20 622.5 147.1

Results

44

Mass spec Seq.

Coverage Peptide count

Mascot Score

Sequest Metascore

S1 alpha casein

ProQ HCT 54.7 13 552.4 63

Coomassie HCT 60.7 23 769.8 114.1

ProQ QTRAP 22.9 4 169.7 46


ProQ QSTAR 11.3 --- --- ---

Coomassie QSTAR 42.5 9 296.6 54.3

S2 alpha casein

ProQ HCT 27.5 10 265.1 25

Coomassie HCT 22.5 9 231.5 26.7

ProQ QTRAP 9.2 3 83.9 8.4


ProQ QSTAR 11.2 3 81.6 3.1

Coomassie QSTAR 3.6 1 30.7 ---

Samples were measured on three different mass spectrometers, namely HCT plus, QSTAR and

QTRAP, to compensate for mass spectrometer type specific differences. In general, Coomassie

staining resulted in better identification rates for all proteins for each mass spectrometer (Table 3). In

particular, the identification of phosphorylated peptides by all three instrument types was hampered

by ProQ staining in comparison to Coomassie stainging (Table 4).

Table 4: Identified peptides of -casein. -Casein was separated by 1-D gel electrophoresis followed by

protein staining with colloidal Coomassie or Pro-Q®Diamond. The individual protein bands were then excised,

digested in the gel matrix using trypsin, and the resulting peptides were analyzed by nano-HPLC/ESI-MS/MS

using an ion trap (HCTplus), triple quadrupole (4000 QTRAP), and quadruple time-of-flight (QSTAR XL)

instrument. Phosphorylated peptides are highlighted in blue.

Peptide (-Casein) Position z Mascot Score


Colloidal Coomassie stain (HCTplus) Pro-Q®Diamond stain (HCTplus) HQGLPQEVLNENLLR 23-37 2 37.7 FFVAPFPEVFGK 38-49 2 23.2

FFVAPFPEVFGK 38-49 2 63.7 FFVAPFPEVFGKEK 38-51 2 49.2

FFVAPFPEVFGKEK 38-51 2 39.7 YLGYLEQLLR 106-115 2 53.6

DIGSHPO3ESHPO3TEDQAMOxEKIK 58-73 2 42.8 EPMIGVNQELAYFYPELFR 148-166 2 43.6

DIGSHPO3ESHPO3TEDQAMEKIK 58-73 2 45.9

DIGSHPO3ESTEDQAMOxEKIK 58-73 2 45

HIQKEDVPSER 95-105 2 60

EDVPSER 99-105 2 29.3

YLGYLEQLLR 106-115 2 63

YKVPQLEIVPNSAEER 119-134 2 50.6

VPQLEIVPNSAEER 121-134 2 43.4

EGIHAQQK 140-147 2 37

Colloidal Coomassie stain (Q-Trap) Pro-Q®Diamond stain (Q-Trap) HQGLPQEVLNENLLR 23-37 2 37.7 FFVAPFPEVFGK 38-49 2 23.2

FFVAPFPEVFGK 38-49 2 63.7 FFVAPFPEVFGKEK 38-51 2 49.2

FFVAPFPEVFGKEK 38-51 2 39.7 YLGYLEQLLR 106-115 2 53.6

Results

45



DIGSHPO3ESHPO3TEDQAMOxEDIK 58-73 2 42.8 EPMIGVNQELAYFYPELFR 148-166 2 43.6

DIGSHPO3ESHPO3TEDQAMEDIK 58-73 2 45.9

DIGSHPO3ESTEDQAMOxEDIK 58-73 2 45

HIQKEDVPSER 95-105 2 60

EDVPSER 99-105 2 29.3


YKVPQLEIVPNSAEER 119-134 2 50.6

VPQLEIVPNSAEER 121-134 2 43.4

EGIHAQQIK 140-147 2 29.1

EPMOxIGVNQELAYFYPELFR 148-166 2 37

Colloidal Coomassie stain (Q-Star) Pro-Q®Diamond stain (Q-Star) FFVAPFPEVFGK 38-49 2 42.7 FALPQYLK 189-196 2 31.1

DIGSHPO3ESTEDQAMOxEDIK 58-73 2 36 AMKPWIQPK 204-212 2 18.8

EDVPSER 99-105 2 25.2 TKVIPYVR 213-220 2 31.8


EGIHAQQIK 140-147 2 24.2

Subsequent to the analysis of standard proteins, lysates of mouse OE after the treatment with

octanal or without treatment (control) were analyzed by 2-D PAGE and Pro-Q®Diamond staining. For

this purpose, 2-D gels with the dimensions 9 cm x 6 cm and a capacity of 40 µg protein were used as

described in Materials and Methods (Figure 8).

Fig. 8: Comparative analysis of protein lysates of mouse olfactory epithelium (OE) after treatment

with the odorant octanal or without treatment (control). 40 µg of protein lysates from OE of a control

mouse and a mouse treated with octanal for 10 min were separated by 2-D PAGE and subsequently visualized

with the Pro-Q®Diamond phosphospecific stain. By comparing the staining patterns, octanal-induced changes

in the abundance of some proteins could be observed, presumably indicating protein phosphorylation (C) and

dephosphorylation events (B), whereas other protein spots remained unchanged.

(A)

(B)

(C)

Results

46

Application of 2-D PAGE combined with the Pro-Q phosphospecifc stain allowed the detection of

changes in (phospho)protein spot pattern of control and octanal-treated OE. While comparison of

spot pattern revealed no differences in intensities for the majority of protein spots (Figure 8 A), a

small set of proteins spots, however, showed significantly more intense (Figure 8 C) or weaker

(Figure 8 B) staining after octanal treatment. The experiment was repeated and 28 differentially

stained spots were excised followed by MS analysis for protein identification (Figure 9).

2 pI 11 2 pI 11

Fig. 9: Comparative 2-D gel analysis of proteins from octanal-treated mouse olfactory epithelium (OE) and

control OE. 40 µg of protein lysates from octanal-treated (10 min) and control OE were separated by 2-D PAGE

and subsequently visualized with the Pro-Q®Diamond phosphospecific stain. Subsequently, 28 differentially

stained protein spots were excised and prepared for mass spectrometric analysis.

Protein spots 1 - 20 and 21 – 28 were analyzed by nano-HPLC/ESI-MS/MS on a Q-Trap 4000 and a

HCT plus ion trap instrument, respectively. The protein identification results are summarized in

Table 5, detailed information can be found in Supplemental Table SP 25. Use of the HCTplus ion trap

instrument generally allowed peptide analyses of higher sensitivity, resulting in improved protein

identification rates with both higher Mascot and Sequest scores. In total, 18 protein spots (64 %)

were successfully identified. Amongst these, vimentin (spots 22 and 23) and mortalin (spot 16) were

previously reported in a quantitative 2-D DIGE study of mouse OE treated with octanal on a longer

time scheme (Barbour et al. 2008). In addition, heat shock (spot 2 and 8) and cytoskeletal (spots 11,

19, 20, 24) proteins were found to be differentially stained by the Pro-Q dye, which belong to protein

groups regulated by long term octanal treatment (Barbour et al. 2008). However, no phosphorylation

site could be identified in any of the identified proteins.

1

2 3 4 5

6

7 8

9 10

16 17

12 13 14

11

15 18

19 20 21

25 26

27 28

SDS

- P

AG

E

24

24

21 22

(A) (B)

Results

47

Table 5: Identification results from Pro-Q stained minigels. Identification results of spots excised form Pro-Q

stained 2-D gels are listed in this table. Protein names and NCBI accession numbers are specified as well as

protein scores from Sequest and Mascot database searches.

# Spot NCBI Accession Number

Protein Name Sequest Score

Mascot Score

1 n.i. n.i. - -

2 gi|40556608 heat shock protein 1, beta 21.9 290.2

3 n.i. n.i. - -

4 n.i. n.i. - -

5 gi|63101587 aconitase 2, mitochondrial 29.6 241.3

6 n.i. n.i. - -

7 n.i. n.i. - -

8 gi|32822907 pcx protein 3.7 97

9 gi|20810077 matrin 3 11.6 87.9

10 gi|6678499 UDP-glucose dehydrogenase 25.7 219.3

11 gi|16303309 type II keratin 5 26.6 230.6

12 n.i. n.i. - -

13 n.i. n.i. - -

14 n.i. n.i. - -

15 n.i. n.i. - -

16 gi|14917005 stress-70 protein, mitochondrial precursor (mortalin) 40.9 340.7

17 gi|42542422 heat shock protein 8 39.6 336.4

18 gi|293689 lamin B 7.9 214.5

19 gi|114145561 keratin type 2, cytoskeletal 8 87.4 736

20 gi|114145561 keratin type 2, cytoskeletal 8 51.1 539.4

21 gi|23272966 Atp5b protein 74 1283.9

22 gi|2078001 vimentin 88.2 1012.3

23 gi|2078001 vimentin 101.9 1158.6

24 gi|532610 cytokeratin 96.9 1239.7

25 gi|34395632 long palate, lung and nasal epithelium carcinoma associated protein 3 precursor

36.7 367.8

26 gi|56237988 sec14-like 3 41.4 428.8

27 n.i. n.i. - -

28 gi|6671569 acidic ribosomal phosphoprotein P0 35.8 346.5

n.i.; not identified

Although 2-D PAGE combined with Pro-Q staining appeared to facilitate the specific detection of

phosphoproteins for differential analysis, subsequent MS/MS-based identification of the respective

phosphorylated peptide species was significantly hampered. Moreover, specific information about

the localization of phosphorylation sites could not be obtained. A further major drawback of the

phosphoproteomics approach reported here was the limited reproducibility in terms of spot

intensities and the overall quality of the stain within biological replicates as exemplified in Figure 10.

Such staining variability could not be prevented, although at all times staining was carried out strictly

according to the same protocol.

Results

48

Fig. 10: Reproducibility of Pro-Q diamond staining. Pro-Q staining of OE lysates separated by 2D

electrophoresis was not reproducible. The intensity of visualized spots (A and B) varied in biological replicates

as well as the quality of the staining itself (C).

As the quantification of changes in protein phosphorylation level using the Pro-Q stain is done on the

basis of staining intensity via image analysis, low background staining and reproducible staining

quality is essential to ensure reproducible detection and quantification. Furthermore, protein spots

with observed changes in staining intensity have to be identified by MS to confirm the identity of the

given protein. Moreover, the changes at particular phosphorylation sites within the protein have to

be identified by MS in order to analyse the underlying signaling processes. However, neither the

staining quality of the Pro-Q stain was sufficient to ensure reproducible quantification nor the MS

anaylsis of phosphopeptides from differentially stained proteins spots was possible. Thus, the Pro-Q

staining appeared not to be applicable for a phosphoproteomics study of octanal treated mouse OE.

In addition, some 2-D gels of biological replicates from OE of octanal treated mice, which showed

good quality Pro-Q staining, revealed significant differences in overall staining intensity. Total protein

staining of biological replicates of OE from treated mice however, revealed similar spot patterns

(Supplementary Figure SP 1). Therefore, the sample preparation itself might not have been

reproducible on the phosphorylation level.

A B C

Results

49

7.2 Establishment of a gel free phosphoproteomics workflow

As an alternative phosphoproteomics workflow, a gel-free MS-based strategy, employing strong

cation exhchange (SCX) chromatography and titanium dioxide (TiO2) enrichment, was chosen. In

order to establish this gel-free phosphoproteomics workflow, lysates of LNCaP cells were used as a

complex standard protein mixture. Thereby, the biological system, which is designated for the final

study of signaling pathways following the activation of PSGR, was likewise used for method

establishment. For method development, LNCaP cells were stimulated using sodium orthovanadate

(OV). As OV is a potent tyrosine phosphatase inhibitor, such a treatment leads to continuous

activation of several signaling cascades and to an accumulation of phosphorylated proteins in the cell

(Samet and Tal 2010). Hence, a highly phosphorylated protein sample was provided.

The entire gel-free phosphoproteomics workflow is depicted in Figure 11. In this strategy, lysates of

OV treated LNCaP cells are prepared and proteins are directly digested using trypsin as proteolytic

agent. Following the removal of salts, the complex protein mixture is separated by SCX

chromatography. Subsequently, phosphopeptides are further enriched from SCX fractions applying

TiO2 affinity chromatography and then characterized by LC/MS/MS combined with bioinformatics

analysis.

Fig. 11: Planned gel free phosphoproteomics workflow. LNCaP cells were treated with orthovanadate and

proteins were prepared. Resulting protein mixtures were tryptically digested in solution and desalted.

Generated peptide samples were separated using SCX chromatography with subsequent affinity enrichment for

phosphopeptides. TiO2 eluates were analyzed using mass spectrometry and adequate bioinformatics.

Orthovanadate treatment of LNCaP cells

Cell lysis and tryptic digestion

Desalting and preparation for SCX

SCX chromatography

Titanium dioxide affinity enrichment

Mass spectrometric measurements

Bioinformatic analysis

Results

50

Initial experiment using published protocols for SCX chromatography (Ballif et al. 2004) and TiO2

enrichment (Mazanek et al. 2007) only lead to the identification of 8 phosphopeptides from the first

5 SCX fractions (Supplementary Table SP6). However, none of the steps in the designed

phosphoproteomics workflow was hitherto properly established and thus, had to be thoroughly

optimized prior to addressing biological questions by application of this strategy.

7.2.1 Ortho-vanadate treatment of LNCaP cells

Orthovanadate treatment is commonly used to accumulate protein

phosphorylation in cultured cells including LNCaP cells

(Giorgianni et al. 2007; Chen, L. et al. 2010) for phosphoproteomics analysis. The accumulation of

phosphorylated proteins is generally counter regulated to restore homeostasis (Samet and Tal 2010).

Accordingly, the amount of protein phosphorylation is likely to decrease after a certain time of

activation. Thus, stimulation was carried out using 3 mM orthovanadate and a treatment duration of

10 min, as was determined to result in maximum phosphorylation (Tschapek 2008).

7.2.2 Cell lysis and tryptic digestion

For cell lysis, a 9 M urea buffer was used, containing the tyrosine

phosphatase inhibitor sodium orthovanadate, the serine and threonine

phosphatase inhibitor sodium fluoride and two competitive phosphatase

inhibitors, namely β-glycerophosphate and pyrophosphate. One the one hand, this buffer contains

no detergents, which would be difficult to remove and potentially interfere with subsequent

chromatographic or mass spectrometric analyses. On the other hand, 9 M urea has a sufficiently

denaturing effect to promote unfolding of proteins and thus, inhibition of kinases and phosphatases

in addition to the inhibitors used. To allow efficient protein digestion using trypsin, the urea buffer is

diluted 1:4. Protein digestion with trypsin is typically carried out using protein to trypsin ratios of

1:50 – 1:100, with digestion over night at 37 °C and pH 7-8. In order to preserve as many of the labile

phosphorylation sites in proteins as possible, a shorter digestion protocol would be preferable.

According to Finehout et al. (Finehout et al. 2005), the temperature optimum for trypsin activity is in

the range of 50 to 55 °C. At that temperature, however, the autolysis activity of trypsin is likewise

high. Therefore, the authors recommended to tryptically digest proteins at a temperature below

Ortho-vanadate treatment of LNCaP cells

Cell lysis and typtic digestion

Results

51

48 °C. In this work, protein digestion using trypsin was performed at 42 °C for different durations

ranging from 0.5 h to 5 h.

In Figure 12, a colloidal Coomassie stained gel is depicted, on which a lysate before digestion, after 3

hours of digestion at 42 °C and after digestion over night at 37 °C was separated. After three hours of

digestion at 42 °C no distinct protein bands are visible anymore. Therefore, this digestion regime was

chosen for further experiments.

7.2.3 Desalting and preparation for SCX

After protein digestion, the resulting tryptic peptides resided in a 2.2 M

urea buffer. Thus, peptides had to be desalted before separation by SCX

chromatography. For that purpose, an Oasis HLB Cartridge, which was

used in similar studies before (Gruhler et al. 2005; Boja et al. 2009), was employed according to the

manufacturer´s protocol. 1 mg of cell lysate was digested with trypsin for 3 h at 42 °C and

subsequently desalted. Each step of the desalting procedure was monitored by taking 1/100 of the

sample amount for amino acid analysis (ASA) and another 1/100 of the sample for HPLC analysis. The

samples collected from the desalting procedure carried out according to the manufacturer´s protocol

were analyzed using nano-HPLC. The intensity of the UV trace of the complete digest before

desalting was high, confirming high peptide content in the digest (Figure 13 A, black UV trace). Some

peptides were eluted from the cartridge during washing steps (Figure 13 A, red UV trace), but nearly

no peptides were detectable in the UV trace after elution (Figure 13 A, blue UV trace), demonstrating

immense sample loss during the desalting procedure. In addition, in the original protocol, methanol

was used as organic solvent for the elution of the peptides from the cartridge. However, since

methanol is difficult to remove in vacuo leading to prolonged desiccation of the sample and thus, loss

of labile phosphorylation sites in proteins, alternative solvents were tested.

M L 3h M ON

Fig. 12: Efficiency of the short digestion protocol. To

the lanes from left to right, the following was applied:

M = protein marker, L = 10µg of an LNCaP lysate, 3h

tryptic digest of LNCaP cells carried out for 3 hours,

M = protein marker, ON = tryptic digest of LNCaP

lysate, which was carried out over night. The 1D PAGE

gel was stained with colloidal Coomassie staining.

116 kDa 97 kDa 66 kDa

55 kDa

36 kDa

31 kDa

21 kDa

14 kDa

Desalting and preparation for SCX

Results

52

Fig. 13: Optimization of the desalting protocol prior to SCX chromatography. Samples were

collected throughout the desalting procedure, using the protocol suggested by the manufacturer (A) and

the refined protocol (B) and analyzed by nano-HPLC. Using the manufacturer´s protocol, high UV absorbance

and thereby peptide content could only be detected in complete digests before desalting (black trace) and to

some extend in the washing step, but not in the eluates. Using the refined protocol, peptide intensities

detected before (blue trace) and after (red trace) desalting were nearly the same and no peptide signals could

be detected in wash and flow-through.

The refined desalting protocol using acetonitrile (ACN) instead of methanole and trifluoroacetic acid

(TFA) instead of formic acid (FA) in washing buffers was also inspected using nano-HPLC (Figure 13 B).

Here flow-through and wash fractions showed no detectable peptide content, whereas the elution

sample showed similar UV intensities (Figure 13 B, red trace) than the complete digest before

desalting (Figure 13 B, blue trace), indicating nearly complete recovery after desalting.

The use of TFA instead of FA in all but the elution solution prevented sample loss during loading and

washing. Methanol was replaced by acetonitrile which resulted in much better recovery (Figure 13 B)

and shorter desiccation duration. The total recovery by the optimized protocol according to ASA

measurements was 60-70 %. In addition, the refinement of digestion and desalting steps lead to an

increase in phosphopeptides, which could be detected by MS after sample preparation

(Supplementary Table SP 7).

A B

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53

7.2.4 SCX chromatography

For the identification of a high number of phosphorylated peptides,

efficient prefractionation of complex peptide samples is required.

To this end, SCX chromatography was employed prior to further phosphopeptide enrichment

followed by mass spectrometric analysis. First experiments with a two solvent system (solvent A: 10

mM KH2PO4, pH 2.7; solvent B: 10 mM KH2PO4, 1 M KCl, pH 2.7) running a linear gradient from 0-100

% B, showed inefficient separation, as most peptides eluted in the first half of the gradient. On this

account, 0.5 M KCl was used in solvent B in combination with a two step gradient (0-50 % in 60 min,

50-100 % in 10 min) for further experiments. As a result, resolution was significantly improved.

Beyond that, other buffer systems using ammonium acetate, for example, were tested. Nevertheless,

the phosphate buffer system with 0.5 M KCl in solvent system B was found to provide highest

resolution of SCX chromatography for peptide separation. For identification results and peptide

distribution in SCX chromatography for 1 M and 0.5 M KCl in solvent B, please refer to

Supplementary Tables SP 12 and SP 13, respectively. Tryptic peptides generated from an LNCaP cell

lysate were typically loaded at isocratic conditions for 20 min prior to the gradient. This allowed

isochratic separation of the flow-through and thus, separation of a high number of phosphopeptide

species present in the flow-through. The gradient was then followed by a reequilibration step of at

least 30 min in order to provide high performance of SCX separation in the next run. Furthermore, in

order to prevent memory effects, a blank was run between two samples (Supplementary Figure SP

2).

Next, phosphopeptide separation characteristics of the gradient were tested. For that purpose, a

mixture of standard peptides, consisting of phosphorylated and non-phosphorylated peptides

(Table 1, Chapter 4), were separated by SCX chromatography and 20 fractions a 6 min were collected

during the gradient. Each fraction was then analyzed by LC/MS/MS on an LCQ deca XP ion trap

instrument. The relative intensities of phosphorylated and non-phosphorylated peptides as

determined by MS analysis were summed for each fraction. As depicted in Figure 14, highest

intensity of phosphorylated standard peptides was observed in the first fractions (fraction 2-7;

gradient time 6 – 42 min) except for the histidine-containing peptide SSRTpSHEYGRK. This is in

accordance with the fact that peptides with additional basic residues elute in later fractions due to

their higher net charge state. In contrast, the summed relative intensities of non-phosphorylated

peptides measured in MS survey scans were highest in later fractions (fraction 10 -13, gradient time

SCX chromatography

Results

54

60 – 78 min). These data confirmed an overall good separation between phosphorylated and non-

phosphorylated peptide species in the gradient. 5

Fig. 14: SCX separation of standard peptides. 20x 6 min fractions of a SCX separation of standard peptides

were analyzed on the LCQ deca XP. The summed intensity of phosphorylated (dark blue) and non-

phosphorylated peptides (light blue) in each fraction is shown here. The separation of phosphorylated and non-

phosphorylated standard peptides by the optimized SCX conditions showed the expected distribution with

phosphorylated peptides eluting predominantly in early fractions (2 – 7). The histidine containing

phosphopeptide SSTRpSHEYGRK eluted with high intensities in later fractions due to its higher net charge state.

The distribution of non-phosphorylated peptides was focused on later fractions (10 - 13).

In addition, 100 µg of tryptic peptides derived from an LNCaP lysate were separated via SCX

chromatography. 20 fractions a 6 min were collected throughout the gradient (120 min with

subsequent 30 min reequilibration) and the resulting peptide fractions were analyzed by LC/MS/MS

employing CID for peptide fragmentation on an HCT Ultra PTM ion trap instrument. In Figure 15, the

number of peptides identified in a single fraction as well as in two and three individual fractions is

shown. Since most peptides were only identified in a single fraction, good resolution of the optimized

SCX gradient was demonstrated.

Fig. 15.: Separation performance of SCX chromatography. 20 fractions of a SCX run were analyzed using

the HCT Ultra PTM mass spectrometer. Most peptides were only identified in a single fraction, therefore a

sufficient resolution of the SCX setup can be assumed.

0

50

100

one fraction two fractions three fractions

# o

f p

epti

des

fraction number

Results

55

7.2.5 Titanium dioxide affinity enrichment

To further improve the detection and identification of phosphorylated

Peptides by mass spectrometry, SCX fractions were subsequently enriched

for phosphopeptides using titanium dioxide (TiO2) affinity chromatography.

Initially, TiO2-based enrichment of phosphopeptides was carried out according to the protocol

published by Mazanec et al. (Mazanek et al. 2007) and tested with a standard mixture of

phosphorylated and non-phosphorylated peptides (Table 1, Chapter 4). Samples were taken from the

flow-trough, wash steps and eluates and analyzed by LC/MS/MS on an LCQ XP ion trap instrument.

From this relatively simple mixture, phosphopeptides could be selectively enriched and subsequently

detected, even when using minute sample amounts of 0.1 fmol per peptide (Table 6). However, the

enrichment of phosphopeptides from SCX fractions of tryptic digests of LNCaP cell lysates resulted in

the identification of only 34 phosphopeptides (Supplementary Table SP 8). Presumably, the

phosphate buffer used in SCX chromatography interfered with TiO2 affinity chromatography for

phosphopeptide enrichment.

Table 6: Peptide idenfied in samples for different steps of the TiO2 enrichment procedure. TiO2 affinity

enrichment was conducted for different concentrations of a standard peptide mixture and samples were taken

from the flow-through, wash and eluate steps. Phosphopeptides (red) could be selectively enriched and

subsequently detected by LC/MS/MS for all concentrations down to 0.1 fmol per peptide.

Thus, different commercially available desalting 10 µl pipette tips were tested: PerfectPure-C18

(Eppendorf, Hamburg, Germany), C18 ZipTip® (Millipore, Billerica, MA, USA) and Omix® (Varian, Inc.

Santa Clara, CA, USA) the peptide recovery was determined using amino acid analysis (Aminosäure

Analyse (ASA)). Due to low reproducibility of peptide recovery after sample desalting using

commercially available pipette tips with C18-material, the published TiO2 protocol (Mazanek et al.

Peptide concentration

Peptides identified in Eluates

Peptides identified in wash step

Peptides identified in Flow-through

40 fmol 1212, 1461, 1462 1544 1211

20 fmol 1212, 1544, 1464, 1461,

1462, 1448 1544 1211

10 fmol 1212, 1544, 1464, 1461,

1462 1544, 1211 ---

5 fmol 1212,1544, 1461, 1462 --- ---

1 fmol 1212 --- ---

0.1 fmol 1212, 1461, 1462 --- ---

Titanium dioxide affinity enrichment

Results

56

2007) was refined to allow for efficient enrichment of phosphopeptides from SCX fractions. In the

refined method reported in this work, SCX fractions were diluted 1:1 with doubly concentrated

loading buffer, thereby decreasing phosphate concentration to 2.5 mM, nearly a tenth of the

concentration used before. Beyond that, a batch protocol was established and tested, in which the

TiO2 material was separated from the solutions by pelleting the material by centrifugation rather

than using self packed TiO2 coulmns as in the original protocol. For the refined batch protocol, a

refined loading solution composition (Schmidt et al. 2008) was used. When this protocol was used for

the phosphopeptide enrichment from 5 SCX fractions, 93 unique phosphopeptides were identified

(Supplementary Table SP 9). Thus, with the refined protocol over 10-times more phosphopeptide

could be identified than with the initial protocol.

7.2.6 Mass spectrometric analysis

Application of conventional CID techniques for fragmentation of

phosphorylated peptides provides MS/MS spectra, which typically

contain many fragment ions from neutral losses and only a small

number of sequence-informative fragment ions ((Boersema et al. 2009), chapter 2.5.3). Therefore,

for efficient MS-based phosphopeptide analysis, alternative fragmentation techniques were

established in this work. To this end, electron transfer dissociation (ETD) of (phospho)peptides on the

HCT Ultra PTM ion trap instrument was used.

Fig. 16: CID versus ETD fragmentation. SCX fractions of an LNCaP peptide separation were measured using the

HCT Ultra instrument with CID and neutral loss triggered ETD fragmentation. The CID spectrum of the

phosphorylated peptides (ASHSESPSLQSK) x HPO3 (A) showed dominant neutral loss fragments and little

fragments with sequence information. Furthermore, the phosphorylation site could not be determined. ETD

fragmentation of the same peptide (B) resulted in far more complete fragmentation allowing for sequence

determination and correct localization of the phosphorylation site.

Mass spectrometric analysis

(A) (B) [ ]HPO3

Results

57

In order to evaluate CID and ETD fragmentation of phosphopeptides, one half of the first 5 SCX

fractions of a separation of 200 µg of peptides from LNCaP lysate were analyzed using the HCT Ultra

instrument with CID and neutral loss triggered ETD fragmentation (Supplementary Table SP16).

Peptides which resulted in dominant neutral losses in CID fragmentation were therefore triggered for

ETD fragmentation, which resulted in better identified by ETD through more complete sequence

coverage as exemplified in Figure 16. However, as ETD fragmentation works best for highly charged

long peptides (Wiesner et al. 2008), smaller doubly charged peptides resulted mainly in poor ETD

fragmentation spectra. In these cases, CID fragmentation spectra still resulted in better identification

results.

The other alternative fragmentation technique is multistage activation (MSA) on a LTQ Orbitrap

instrument. To evaluate MSA fragmentation, the other half of SCX fractions was analyzed using MSA

fragmentation on a LTQ Orbitrap instrument. The resulting spectra were compared to CID spectra

from the same peptide measured previously on the HCT Ultra instrument.

Fig. 17: CID versus MSA fragmentation. The second half of the previously used SCX fractions was analyzed

using the LTQ Orbitrap instrument with MSA fragmentation. Resulting spectra were compared to CID spectra of

the same peptide measured using the HCT Ultra (A). Peptides, which provided CID spectra with dominant

neural losses and little sequence information (A), resulted in MSA spectra, which show complete fragmentation

and which allowed for the exact determination of phosphorylation site localization (B).

MSA fragmentation also led to more complete fragmentation of phosphopeptides, whose CID

spectra were dominated by neutral loss peaks, thereby leading to more confident identification of

the peptide and the phosphorylation site within the peptide Figure 17. With the MSA technique, the

neutral loss fragments resulting from the first CID fragmentation are once more activated to facilitate

further fragmentation. However, not all possible neutral losses should be considered for MSA, as the

application of a high number of neutral loss mass selected for MSA, results in the fragmentation of

sequence containing fragments as well (Supplementary Figure SP 3). For that reason, a measurement

strategy was used on the LTQ Orbitrap, which employed MSA fragmentation using neutral loss

masses of 98, 49 and 32.6 m/z, resembling the loss of one and two times phosphoric acid for doubly

charged and the loss of one time phosphoric acid for triply charged precursors.

(A) (B) [ ]HPO3

Results

58

When the phosphopeptides identified from the two mass spectrometric anaylsis regimes were

compared, the overall number of identified phosphopeptides was in the same range for both

methods (Figure 18). For further details on identification results please refer to Supplementary Table

SP 16.

Fig. 18: Comparison of the combination of CID with neutral loss triggered ETD using the HCT Ultra PTM versus

MSA fragmentation on the LTQ Orbitrap. Both analysis regimes identify a similar amount of peptides but with

surprisingly low overlap, demonstrating that these techniques are highly complementary.

The overlap between the identified phosphopeptides however, was very small with only 7

phosphopeptides, which were identified by both methods. This demonstrated that both

fragmentation techniques are highly complementary.

7.2.7 Bioinformatics analysis

In order to not only identify a high number of phosphorylated

peptides but also to accurately determine phosphorylation sites

in proteins, bioinformatic tools specifically tailored to the analysis of large-scale MS/MS datasets

have to be applied. Employing the multistep MS-based phosphoproteomic strategy reported in this

work, large MS/MS data sets from two different instruments with complementary fragmentation

techniques (i.e. NL-triggered ETD and MSA) are produced. At the same time, no suitable

bioinformatics strategy was available to allow both reliable phosphopeptide identification and

accurate phosphorylation site localization from such datasets. To address this shortcoming, two

different software platforms were adopted. For the analysis of LTQ-Orbitrap-MSA data, the

MaxQuant software (Cox and Mann 2008) was applied, while for HCT Ultra CID/ETD data,

ProteinScape 1.3 was used. Using the MaxQuant software, (phospho)peptides were automatically

identified based on database searches using the Mascot algorithm, which eventually enables protein

identification. In addition, Stable Isotope Labeling by Amino acids in Cell culture SILAC-labeled

proteins can readily be quantified using this software package. A further unique feature is the

Bioinformatics analysis

Results

59

calculation of a phosphosite-specific score, which provides a quantitative measure for the accuracy of

phosphorylation site localization in proteins. For the HCT data set comprising low resolution CID and

ETD spectra, no such advanced software solution providing a phosphosite-specific score was

available. Generally, sequence-specific information from CID and ETD spectra is highly

complementary, likely resulting in higher peptide identification rates. However, using common

search engines, CID and ETD spectra cannot be searched against a database in a single run. In this

work, peptide identification results from CID and ETD spectra were therefore combined using the

Labour and Information Management System (LIMS) ProteinScape 1.3. Furthermore, a MASCOT cut-

off score of 20 was set for peptide identifications based on low-resolution CID and ETD spectra. As a

result, the accuracy of peptide and thus, protein identification was increased.

To evaluate the correlation between the Mascot score and the quality of the acquired fragmentation

spectra, 14 fractions of an SCX separation of 200µg of LNCaP cells were analyzed on the HCT Ultra ion

trap instrument alternately subjecting each precursor to CID and ETD experiments. Subsequently,

both data sets were searched against the IPI_human database (v 3.61) using Mascot (for

identification results see Supplementary Table SP 11). As shown in Figure 19, the obtained Mascot

scores increased with peptide length. This effect was even more pronounced for ETD than for CID

spectra. Furthermore, Mascot scores were usually lower for ETD than for CID spectra, although the

quality of spectra was comparable. At this point, it has to be pointed out that both precursor ions

and the respective charge-reduced precursor species are highly abundant in ETD spectra and thus,

largely account for the total intensity of the spectrum. As the Mascot score is partially dependent on

how much of the overall intensity can be explained by assigned fragment ions (while precursor

species are disregarded), the intensity coverage for ETD spectra is typically lower than for CID

spectra, providing a likely explanation for the lower scores assigned.

Results

60

Fig. 19: Mascot Scores of CID and ETD spectra. Peptides from a LNCaP cell lysates were separated using SCX

chromatography. 14 fractions were analyzed on the HCT Ultra mass spectrometer using alternating CID and

ETD fragmentation. After database searching, MASCOT Scores of CID and corresponding ETD spectra were

plotted against the number of amino acids in the respective peptide. Scores tend to be higher for CID than of

for ETD spectra, although this effect levels out with increasing peptide length. In general, scores for ETD spectra

were observed to be lower than that of corresponding CID spectra.

Using ProteinScape 1.3, only one cut-off score could be set for a given protein list. As the individual

cut-off score typically differed for CID and ETD spectra, the score applied in this work was carefully

evaluated. For that purpose the dataset produced for CID and ETD MASCOT score evaluation was

subjected to protein assembly via the ProteinExtraktor tool in ProteinScape 1.3 with different cut-off

scores (Supplementary Tables SP 2 - SP 5) and a false discovery rate (fdr) of 5 % was applied. In

Table 7, the number of identified proteins and phosphopeptides for different score cut-off values is

listed. A low cut-off score limit of 15 resulted in a low number of identified proteins, which increased,

when the score cut-off was set to higher values. However, the number of identified phosphopeptides

as well as the percentage of phosphopeptides identified via ETD spectra is decreasing, when higher

score cut-off values are applied. Therefore, a global score cut-off of 20 was used in further

experiments for both CID and ETD data to ensure a high identification rate for proteins as well as

phosphopeptides. In addition, the percentage of phosphopeptides identified via ETD spectra and

therefore potential high quality spectra was highest.

Results

61

Table 7: Consequence of different ProteinExtraktor cut-off scores. The dataset of CID and ETD spectra

produced for the MASCOT score evaluation was subjected to protein assembly using the ProteinExtractor tool

in ProteinScape 1.3. For that purpose different score cut-off values were tested. The number of subsequently

identified proteins (5 % fdr) and phosphopeptides is listed as well as the percentage of phosphopeptides, which

were identified via ETD spectra.

ProteinExtraktor score cut-off

# of proteins identified (5 % fdr)

# of phosphopeptides within protein list

% of phosphopeptides identified via ETD spectra

15 476 90 19 %

20 555 36 24 %

25 623 22 7 %

30 655 11 9 %

To evaluate the assignment of phosphorylation sites in proteins, low resolution NL-triggered ETD

data acquired on the HCT Ultra PTM ion trap instrument was further analyzed using the algorithm

SLoMo (Bailey et al. 2009).This algorithm is a refinement of the AScore algorithm (Beausoleil et al.

2006) applicable to the evaluation of low resolution CID and ETD spectra. Application of a SLoMo cut-

off score higher 19 is reported to allow for phosphorylation site localization with a FDR of 5% (Bailey

et al. 2009). In this work, also a cut-off of score of 19 was applied.

Results

62

7.3 Global phosphoproteomic analysis of orthovanadate treated

LNCaP cells

The phosphoproteomics strategy established in this work was next applied to the analysis of LNCaP

cell lysates in two independent replicates. To this end, LNCaP cells were treated in vivo with

orthovanadate, providing a highly phosphorylated protein sample. In addition to the evaluation of

the established workflow for global phosphoproteomics analysis, this study aimed at improving our

current knowledge of the phosphoproteome of the human prostate carcinoma cell line LNCaP by

identifying new phosphorylations sites in protein and thus, new targets for follow-up studies.

7.3.1 Strategy for the phosphoproteome analysis of LNCaP cells

Figure 19 depicts the entire workflow for the MS-based analysis of the phosphoproteome of LNCaP

cells. At first, cells were treated with 3 mM orthovanadate for 10 min in order to inhibit tyrosine

phosphatases and therefore, to accumulate protein phosphorylation (Samet and Tal 2010).

Subsequently, cells were lysed, proteins were in-solution digested using trypsin and the resulting

peptide mixtures desalted with C18-cartridges. For each sample, the total peptide concentration was

determind using ASA and 200 µg were subjected to SCX chromatography. In total, 32 fractions à 4

min (200 ml each) were collected throughout the gradient. Of these, 15 µl were directly analyzed

either on the LTQ-Orbitrap system using MSA or the HCT Ultra PTM system with CID and NL-triggered

ETD. The remaining 185 µl of the sample were subjected to TiO2 affinity enrichment employing the

batch protocol established in this work. From the resulting TiO2 eluates, one third was then analysed

by LC/MS/MS on an LTQ-Orbitrap instrument with MSA, while the remaining two-thirds were

analysed on a HCT Ultra PTM system using both CID and NL-triggered ETD for peptide fragmentation.

High resolution data acquired with the LTQ-Orbitrap instrument were examined using the MaxQuant

software. With MaxQuant, separate false discovery rates (fdr) of 1% were applied on the protein-,

peptide- and modified peptide level to report peptide and protein identification data of high

accuracy. For phosphopeptides, a probability score was calculated, providing a quantitative measure

for the accuracy of the phosphorylation site assignment. In the work reported here, phosphorylation

sites with a score better than 0.75 were deemed confident.

Results

63

Fig. 20: Workflow used for the global phosphoproteomics study: LNCaP cells were treated with 3 mM

orthovanadate for 10 min, lysed, digested with trypsin and desalted. 200µg of tryptic peptides were separated

via SCX chromatography. 32 fractions (4min) were collected throughout the gradient. 15 µl were taken from

each fraction and measured either with the HCT Ultra PTM (Experiment 1) using CID and NL-triggered ETD

fragmentation or the LTQ-Orbitrap instrument with MSA (Experiment 2). Residual fractions were further

enriched for phosphopeptides by TiO2 enrichment and eluates were splitted: two thirds of each fraction were

measured with the HCT Ultra PTM employing CID and NL-triggered ETD and one third was measured with the

LTQ-Orbitrap instrument with MSA. High resolution data was analysed using the MaxQuant algorithm whereas

for low resolution data protein identification was achieved via ProteinScape with subsequent phosphorylation

site evaluation using the SLoMo algorithm.

For low resolution ETD data, ProteinScape 1.3 and the ProteinExtractor Tool implemented in

ProteinScape 1.3 were used to generate non-redundant protein lists, to which a FDR of 5% was

applied. In order to evaluate the correctness of phosphorylation site localization, the data was

reevaluated using the SLoMo algorithm (Bailey et al. 2009). Phosphorylation sites to which a SLoMo

score of at least 19 was assigned were deemed confident and accordingly, reported in this work.

Results

64

7.3.2 MS-based analysis of LNCaP cell lysates without phosphopeptide

enrichment

As previously described, 200 µg of tryptic digest from orthovanadate-treated LNCaP cells were

separated by SCX chromatography in two independent replicates. UV traces from SCX

chromatography are shown in Supplementary Figure 4. Due to higher backpressure, the UV trace of

experiment 2 is slightly shifted. In general, UV traces of both experiments showed a similar course

and intensity.

In the first experiment, 15 µl of each SCX fraction were then analyzed using CID and NL-triggered ETD

on the ion trap instrument, resulting in the identification of 3185 unmodified peptides and 177

(5.3 %) phosphopeptides. Based on peptide identifications, 579 proteins including 121

phosphoproteins (21 %) were assembled (Table 8, Supplementary Table SP 17). The respective

aliquots of SCX fractions from the second replicate were analysed using MSA on the LTQ-Orbitrap

instrument. As a result, 5330 unmodified peptides and 181 (3.3 %) phosphopeptides were identified,

enabling the assembly of 1709 proteins including 202 phosphoproteins (12 %) (Table 8,

Supplementary Tables SP 19 and SP 20).

Table 8: Number of identified proteins and peptides in SCX fractions

all proteins phosphoproteins all peptides phosphopeptides

Experiment 1 (HCT Ultra PTM)

579 121 (21%) 3335 177 (5.3%)

Experiment 2 (LTQ-Orbitrap)

1709 202 (12%) 5511 181 (3.3%)

While the LTQ-Orbitrap instrument using MSA as fragmentation technique generally provided

superior sensitivity in peptide and protein identification, both experimental approaches resulted in a

relatively low number of phosphopeptide identifications. Since the percentage of identified

phosphopeptides was approx. 10 % higher for the HCT ultra PTM instrument, CID and NL-triggered

ETD are presumably more effective for the identification of phosphorylated peptides from complex

samples, as this method selectively triggers additional ETD fragmentation for low quality CID spectra.

In order to evaluate the SCX performace, the charge state distribution as well as phosphopepitde

distribution was analyzed. The general SCX separation exhibited good performance in terms of

separation of different peptide charge states (Figure 20), with predominantly doubly charged

peptides in early fractions (1-8) and higher charged peptides late in the gradient (14-24).

Results

65

0

50

100

150

200

250

300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

nu

mb

er o

f p

epti

des

fractions

doubly charged

triply charged

higher charged ( >3)

Fig. 21: Distribution of different charge states over SCX fractions. For all peptides identified in experiment 1

and 2, the charge state was determined. The number of peptides observed as doubly, triply or higher charged

species are depicted for each fraction. In fractions 1-7 proportionally more doubly charged peptides are

observed, whereas the proportion of higher charged peptides increases in later fractions (14-24).

At this point, it has to be noted, that charge states shown in Figure 20 are based on observations in

MS spectra and do not necessarily reflect peptide charge state in SCX chromatography. However, as

both SCX and HPLC buffers have pH values below 3.0, namely 2.7 and 2.0 respectively, all basic

aminoacids as well as the N-terminal amino acid are protonated resulting in the same positive charge

state for each peptide in both buffers. Therefore, observations from mass spectrometry data may be

a good approximation.

Although peptides were shown to be well separated according to their charge state, both separation

of phosphopeptides according to the number of phosphorylation sites and enrichment of

phosphopeptides in definite SCX fractions was barely observed (Supplementary Figure SP 5).

Nevertheless, since more than 73 % of the identified phosphopeptides were only found in one or two

adjacent fractions (Supplementary Table SP 14), SCX chromatography was deemed reasonably

effective for prefractionation of such complex (phospho)peptide mixtures.

7.3.3 MS-based anaylsis of LNCaP cell with phosphopeptide enrichment

To enrich for phosphopeptides, 185 µl of each SCX fraction from the two replicates were subjected to

TiO2 affinity chromatography. Of the resulting eluates, one third each were analysed with MSA (LTQ-

Orbitrap) and two-thirds each were analysed with CID and NL-triggered ETD (HCT Ultra PTM). The

Results

66

results are summarize in Table 9. All together, CID in combination with NL-triggered ETD

fragmentation on an ion trap led to the identification of 959 non-phosphorylated and 891

phosphorylated peptides and thereby to the identification of 633 unique proteins containing 335

phosphoproteins. The overlap between both experiments was here low with only 15 % for

non-phosphorylated and 21 % for phosphorylated peptides. With MSA on an LTQ- Orbitrap

instrument 3071 non-phosphorylated and 1310 phosphorylated peptides were identified leading to

the identification of 2084 proteins containing 726 phosphoproteins. Therefore, the MS analysis

regime employing the LTQ-Orbitrap with MSA fragmentation identified approximately twice as many

phosphopeptides and resulting phosphoproteins as the analysis regime using the HCT Ultra PTM with

CID and NL-triggered ETD. However, the overlap of the phosphopeptide sets identified by both

approaches is only 33 %, reflecting again that both methods are highly complementary. For detailed

information on identification results, please refer to Supplementary Tables SP 17 – SP 20.

Table 9: Number of proteins and peptides identified in titanium dioxide eluates. In this table the numbers of

identified non-phosphorylated peptides, phosphopeptides and the resulting numbers of identified

phosphoproteins and proteins are listed for each fragmentation technique and experiment separately as well

as the respective overlaps.

CID ETD unique in CID/ETD MSA unique all

non-phosphorylated

peptides

Experiment 1 331 47 351 1549 1830

Experiment 2 729 114 752 2104 2645

unique (overlap)

921 (15%)

151 (7%)

959 (15%)

3071 (19%)

3349 (34%)

phospho peptides

Experiment 1 384 206 488 1119 1461

Experiment 2 486 224 590 862 1320

unique (overlap)

742 (17%)

346 (24%)

891 (21%)

1310 (51%)

2095 (33%)

all proteins

Experiment 1 564 1194 1350

Experiment 2 417 1095 1105

unique (overlap)

633 (55%)

1464 (56%)

2084 (18%)

phosphoproteins

Experiment 1 173 585 648

Experiment 2 217 432 398

unique (overlap)

335 (16%)

667 (52%)

726 (44%)

Results

67

7.3.4 Comparison of mass spectrometric and bioinformatic analysis

platforms

The performance of the two different measurement regimes and the subsequent bioinformatic

analysis strategies were evaluated based on the number of the reported phosphopeptide

identifications in the two replicates (Figure 22). In experiment 1, 1119 unique phosphopeptides have

been identified using MSA on the LTQ-Orbitrap instrument and 488 with the HCT Ultra setup using

CID and NL-triggered ETD. The overlap between the sets of phosphopeptides identified by the data

derived form the two different MS analysis platforms small with only 147 phosphopeptides, which

were identified by both approaches. Within the data set generated using the HCT Ultra instrument

more phosphopeptides were identified using CID (384) fragmentation. By ETD fragmentation 206

phosphopeptides were identified, including 101 phosphopeptides have been identified by both

fragmentation techniques. In the second experiment, a similar number of phosphopeptides was

identified by the different fragmentation techniques showed and the observed overlap was in the

same range.

Fig. 22: Overlap of phosphopeptides identified by application of different fragmentation techniques in two

independent experiments. In both experiments, a large proportion of phosphopeptides was identified by MSA

on an LTQ-Orbitrap instrument. The use of CID in combination with NL-triggered ETD on an HCT Ultra ion trap

enabled the identifcation of a significantly smaller but complementary set of phosphopeptides.

,

These results again demonstrated high complementarity between all three fragmentation techniques

used in this work. In addition the analysis regime employing the LTQ-Orbitrap instrument in

Results

68

combination with MSA fragmentation provided significantly higher identification rates, than the HCT

Ultra instrument using NL-triggered ETD.

The differences between the three fragmentation techniques became more evident by examining

not the number of peptides but the number of confirmed and unconfirmed phosphorylation sites

(Figure 23). By the MSA/Orbitrap/MaxQuant workflow far more confident (1248) than unconfirmed

(320) site assignments were identified. In contrast, the workflow using CID and ETD on the HCT Ultra

instrument in combination with ProteinScape and SLoMo resulted in similar numbers of confident

and unconfirmed phosphorylation site assignments. Thus, the MSA/Orbitrap/MaxQuant

measurement and analysis workflow turned out to provide, in addition to higher identification rates,

more confident phosphorylation site assignment.

Fig. 23: Identification rates of confident versus unconfirmed phosphorylation sites for different

fragmentation techniques. By MSA fragmentation 1248 unique phosphorylation sites could be indentified. By

CID in combination with NL-triggered ETD altogether 616 confident sites could be identified (A), resembling

approximatedly half of the number identified by MSA. The distribution for the unconfirmed sites was

significantly differend (B), with 676 unconfirmed sites identified by CID/NL-triggered ETD and with only 320

unconfirmed sites found with MSA fragmentation.

In the presented dataset 164 unique hitherto unknown phosphorylation sites could be confidently

identified in 101 different proteins. To determine, if a site is known, the PhosphoSite database

(http://www.phosphosite.org) was searched for previously reported phosphorylation sites on the

peptide level on the 20th of May 2010. 58 of these new phosphorylation sites were uniquely found

with the MSA and 93 with the HCT Ultra workflow, demonstrating the complemantarity of both

approaches (Supplementary Table SP 1). New phosphorylation sites were identified for different

Results

69

proteins with diverse functions, including kinases, transcription factors and members of the

proteasome. The distribution of proteins with newly identified phosphorylation sites into functional

categories is depicted in Figure 24.

Fig. 24: Functional categories of proteins, for which new phosphorylation sites could be identified. In the

global phosphoproteomics anaylsis of LNCaP cells, 164 hitherto unknown phosphorylation sites could be

identified in protein of diverse functions. The number of sites identified for certain functional protein

categories is depicted in this diagramm.

8

11

10

19

6 11 4

16

79

Proteasome

Ribosome

RNA-processing

Transcription/Nucleus

Protein synthesis

Kinase/Signaling

Cancer related

Cytoskeleton-associated

others

Results

70

7.4 Quantitative time-resolved phosphoproteomics to study

receptor-mediated signaling pathways in LNCaP cells

The elaborated phosphoproteomic strategy enabled the global analysis of the phosphoproteome of

LNCaP cells following treatment with orthovandate. In the part of the work presented here, a

quantitative strategy was designed in order to allow for the time-resolved study of signaling

pathways in LNCaP cells upon the activation of PSGR with β-ionon, which is a known ligand of the

PSGR (Neuhaus et al. 2009). To this end, SILAC labeling of LNCaP cells was employed followed by MS-

based phosphoproteomic analysis using the LTQ-Orbitrap system in combination with the MaxQuant

algorithm for quantitative data analysis.

7.4.1 SILAC labeling of LNCaP cells

For SILAC labeling of the proteome of LNCaP cells, the amino acids arginine and lysine were replaced

in the cell culture medium by isotopically labeled counterparts. As trypsin cleaves C-terminally after

arginine and lysine, all tryptic peptides generated from SILAC labeled proteins comprised one labeled

amino acid, except for the respective C-terminal peptide of a given protein. In this work, triple SILAC

labeling of LNCaP cells was performed using arginine and lysine in the "light" (no heavy isotopes), the

"medium" (13C6-arginine, D4-lysine) or "heavy" (13C615N4-arginine, 13C6

15N2-lysine) form. For SILAC

labeling experiments, LNCaP cells were generally passaged two times, which corresponds to two

weeks of cell culturing or approximately 8 cell doublings.

In human cell lines, arginine-to-proline conversion has been reported as predominant side reaction,

which eventually hampers accurate peptide quantification (Ong et al. 2003).

To address this issue, titration experiments using different concentrations of labeled amino acids and

unlabeled proline were performed. In Figure 24, the course of titration experiments is illustrated

taking the tryptic peptides LAVNMVPFPR (A-C) and AAVPSGASTGIYEALELR (D) from a culture labeled

with 13C6-arginine and D4-lysine as an example. Complete incorporation of the heavy labeled amino

acid in proteins was achieved at concentrations from 240 to 60 mg/l 13C6-arginine (Figure 24, A-C).

13C6-arginine is converted to 13C5-proline, resulting in satellite peaks, which are in this case m/z 2.5

appart from the doubly charged peptide peak as indicated by red arrows. Although the concentration

of labeled amino acids was reduced 4-fold in such experiments, arginine-to-proline conversion could

not be abolished.

Results

71

Fig. 25: Medium optimization to eliminate arginine to proline conversion. Reduction of arginine concentration

(A-C) lead to a decrease in heavy proline peaks. Arginine to proline conversion could, however, not be

eliminated completely. In contrast, the addition of a high concentration of proline (D) eliminated 13

C6 arginine

to 13

C5 proline conversion (D).

Therefore and due to a reduced proliferation rate of LNCaP cells observed at 60 mg/l arginine and

lysine concentration, SILAC labeling of LNCaP cells was eventually performed with an arginine and

lysine concentration of 120 mg/l and an high excess of unlabeled proline of 800 mg/l in the cell

culture medium. As a result, complete incorporation of heavy amino acids into proteins was

obtained, while omitting arginine-to-proline conversion during growth and proliferation of LNCaP

cells (Figure 24 D).

7.4.2 Quantitative time-resolved phosphoproteomic strategy

For the quantitative time-resolved phosphoproteomic study as outlined in Figure 25, triple SILAC-

labeled LNCaP cells were differentially treated with β-ionone (dilution 1:10000). To provide an

adequate control (i.e. no treatment with β-ionone; 0 min), cells labeled with "light" SILAC were mock

treated with culture medium in the incubator for 10 min. In contrast, cells labeled with "medium"

and "heavy" SILAC were treated for 2 min and 10 min with β-ionone in the incubator, respectively. To

end the treatment, cells were placed on ice and immediately washed twice with ice cold PBS buffer.

Cells were then lysed and the respective protein samples were mixed in equal ratios (1:1:1) according

(A) (B)

(C) (D)

Results

72

to protein concentration. Finally, 1 mg of total protein was in-solution digested using trypsin and 200

µg of the resulting peptide mixtures were subjected to the SCX-TiO2 workflow for phosphopeptide

enrichment established in this work followed by MSA scans on the LTQ-Orbitrap instrument and

MaxQuant data analysis. The entire experiment was independently performed three times. Prior to

quantitative phosphoproteomic experiments, the differentially labeled cells were subjected to

calcium imaging in order to ensure that such cells responded comparably to β-ionone treatment.

Fig. 26: Workflow for quantitative time resolved phosphoproteomics analysis of β-ionone treated LNCaP

cells. To study PSGR-mediated signaling, triple SILAC labelling was used to label LNCaP cells, which were then

treated for 0, 2 and 10 min with β-ionone (1:10000). Cell lysates were digested in solution using trypsin. Tryptic

peptides were separated via SCX and enriched for phosphopeptides with TiO2 affinity enrichment. Eluates were

analyzed using the Orbitrap/MaxQuant workflow. In order to ensure the responsiveness of the LNCaP cells to

β-ionone, cells of each condition were subjected to calcium imaging in every replicate.

Prior to phosphoproteomic analyses, the responsiveness of SILAC-labeled LNCaP cells to β-ionone

was confirmed by calcium imaging experiments performed by M. Osterloh at the Department for Cell

Physiology (Head Prof. Hatt; Ruhr-Universität Bochum). In Figure 26 the results of the calcium

imaging experiments are shown. In Figure 26 A a representative fluorescence trace of one singe

LNCaP cell is show. The fluorescence of cells was first recorded for 2 min. Then the cells were

stimulated with 500mM β-ionone. The magnitude of the response was measured as amplitude

between the mean fluorescence of the first 2 min and the mean fluorescence between 2.5 – 10 min.

For each replicate all SILAC states were tested prior to phosphoproteomics experiments. In

Figure 26 B, the fluorescence amplitudes for all SILAC states in all replicates are shown.

Results

73

Fig. 27: Calcium imaging results for the three replicates. In the upper panel (A), a representative fluorescence

trace for one single LNCaP cell is shown. Fluorescence was recorded for 2 min, before cells were stimulated

with 500 mM β-ionone. Stimulation start is indicated by an arrow. The response of the cell is determined by

calculating the amplitude between fluorescence before and after stimulation. In the lower panel (B), the

amplitudes of fluorescence response for all SILAC states in all three replicates are shown. Error bars indicated

the standard deviation and the number of recorded cells is stated in the respective column.

A

B

f34

0/f

38

0

f34

0/f

38

0st

imu

late

d -

f3

40

/f3

80

co

ntr

ol

Results

74

7.4.3 Phosphoproteome data of β-ionone-treated LNCaP cells

Following the completion of the treatment regime using β-ionone, LNCaP cells were processed for

the relative quantitative study of the respective phosphoproteomes applying the established

SCX/TiO2 method combined with high resolution MS using MSA scans followed by MaxQuant data

analysis. In all the three replicates, approx. 1900 proteins were identified on average, while the total

number of unique protein identifications was 3054 as summarized in Table 10. For a complete list of

data comprising detailed information on protein identification please refer to Supplementary Table

SP 21. In contrast, the number of identified phosphopeptides was about two-fold higher in replicate

3 (1062) than in the other two replicates (568 and 654).

Table 10: Overview of proteins and phosphopeptides identified in all three replicates

Replicate 1 Replicate 2 Replicate 3 Total

Protein 2051 1922 1805 3054

Phosphopeptides 568 654 1062 1154

To eventually determine significantly regulated phosphopeptides with high accuracy, the calculated

peptide intensity ratios were normalized to an average ration of one. This procedure allows

compensating for inevitable errors during mixing of the three differentially SILAC-labeled samples.

Peptide ratios and normalized peptide ratios were given by MaxQuant. In general, the normalization

procedure within the MaxQuant software is applied to each LC-MS run separately by distinguishing

between lysine- and arginine-containing peptides and using intensity bins for calculating different

normalization factors depending on peptide abundance (Cox et al. 2009). For the normalization over

all peptide ratios, the assumption is made, that the majority of peptides does not change in

abundance during treatment. Therefore, the ratio distribution for both "medium"-to-"light" (M/L)

and "heavy"-to-"light" (H/L) should center around 1. Following normalization, the data was shifted

such that the ratio distribution of M/L and H/L centered around 1 as is exemplarily shown for M/L

rations of replicate 2 in Figure 27.

Results

75

Fig. 28: Phosphopeptide ratios from experiment 2 show systematic over representation of light peptides.

Non normalized M/L ratios show an overrepresentation of light peptides, resulting in maximum in ratio

distribution at a value smaller than 1. Normalization produced led to a ratio distribution, which centered

around 1.

As expected for the treatment of LNCaP cells with β-ionone, the majority of phosphopeptides did not

change in abundance as illustrated in a binary logarithm (log2) plot (Figure 28, A). To determine the

cut-off values of intensity ratios for classifying phosphopeptides as significantly regulated, outlier

analysis using box plots was performed based on the normalized log2 ratios. The M/L ratios,

resembling the regulation of peptides after 2 min of treatment with β-ionone, were slightly less

scattered, than the H/L ratios (10 min treatment). Accordingly, ratio values were defined as outliers

by box plot analysis for values >1.54 and <0.66 for M/L ratios and >1.69 and <0.59 for H/L ratios

(Figure 28, B).

Fig. 29: Log2 ratio distribution of phosphopeptides (A) and outlier analysis using box plots (B). Ratio cut-off

values for significantly regulated phosphopeptides were determined using box plot analysis of the normalized

log2 intensity ratios (B).

0

50

100

150

200

# o

f ra

tio

s in

re

spe

ctiv

e

inte

rval

Ration M/L Rep 1

Rep 2

Rep 3

Rep 1 normalized

Rep 2 normalized

Rep 3 normalized

(A) (B)

log 2

of

rati

os

phosphopeptides

Results

76

Peptides with regulation factors above or below these cut-off scores and with a posterior error

probability (PEP) value <0.05 were considered as being both confidently identified and significantly

regulated. In addition to these 97 phosphopeptides (see Table 11), 206 phosphopeptides were

identified but not quantified by MaxQuant. Of these, 24 were C-terminal peptides comprising neither

lysine nor arginine in their sequence and thus, rendering them not quantifiable. MS spectra of the

remaining 182 phosphopeptides were manually inspected and for several of these, overlapping

isotopic peak patterns or low peak intensities was observed, thereby impeding quantification.

However, based on manual inspection of MS spectra, evidence for the presence of further

phosphopeptides with changes in abundance was obtained. These phosphopeptides likely evaded

quantification using MaxQuant due to their low abundance or coeluting peptides of similar m/z.

It has to be pointed out here that only three regulated phosphopeptides were identified and

quantified in all the three biological replicates. The majority of the remaining 94 phosphopetides

with significant changes in abundance was identified and quantified in a single replicate only.

Possible explanations for this observation could be the absence of such peptides in the respective

replicates or the absence of MS/MS data, e.g. due to the low abundance of these peptides. To

address this issue, an MS Excel-based VBA script was developed enabling to automatically search MS

raw data of all the three replicates for the accurate masses of those peptides which exhibit missing

values. In this reevaluation process, different charge states and potential missed cleavages were

considered for each peptide as well as the retention time of the given peptide as observed in the

replicate it was identified in. The extracted ion chromatograms (XICs) of each peptide were

calculated based on the first three isotopes of each of the three SILAC states applying a mass

accuracy of m/z 0.01. The presence of a peptide was confirmed, if (1) an overlay of all the nine XICs

was observed, (2) the retention time was in the range of ±2 min of the previously observed retention

time and (3) the observed and expected isotopic clusters were congruent. Confirmed peptide species

were then quantified based on the integration of the XICs. As a result, the number of peptides, which

could be quantified in all three replicates, could be increased significantly. In Figure 29 the number of

significantly regulated phosphopeptides, which could be quantified in 1, 2 or 3 replicates by

MaxQuant is depicted in dark blue. With the XIC script, a higher number of phosphopeptides could

be quantified in two or three replicates, raising the number of three times quantified

phosphopeptides from 3 to 30. For detailed information please refer to Supplementary Table SP 23,

in which all determined XIC ratios as well as all ratios calculated by MaxQuant are stated for the

regulated phosphopeptides.

Results

77

Fig. 30: Replicate quantification of peptides by MaxQuant and after XIC evaluation. Through the use of the

XIC evaluation the number of peptides, which have been quantified in all three replicates could be elevated

from 3 to 30.

In addition, the peptides suggested to be regulated by manual inspection were quantified. Thereby,

all but two peptides were eliminated from the list as not being considerably regulated. Finally 99

regulated phosphopeptides from 73 proteins were identified as being significantly regulated. All

these peptides are listed in Table 11. For each peptide the phosphopeptide ID (refers to MaxQuant

table PhosphoSTY (STY sites), Supplementary Table SP 22), the protein group ID (refers to MaxQuant

table proteinGroups, Supplementary Table SP 21) and the protein name as well as the normalized

MaxQuant and XIC-based ration for both time points are listed.

Table 11.: Significantly regulated phosphopeptides. All phosphopetides, which were found to be significantly

regulated are listed in this table. MaxQuant IDs for each phosphopeptide and the dedicated protein are listed

as well as MaxQuant and XIC-based ratios for both time points. Furthemore, the number of the cluster is given,

in which the respective peptide was sorted using K-means clustering. Additional phosphopeptides, which were

found for a given protein, but are not significantly regulated, are marked in yellow. Peptides, which have not

been quantified at all by MaxQuant but were found to be regulated by XIC analysis, are marked in blue.

Phosphopeptides, for which the protein was found to be regulated rather than the phosphorylation sites, are

marked in pink.

Phospho Peptide ID

Protein Group

Protein Names ø MQ Ratio

2 min

ø XIC ratio 2 min

ø MQ Ratio 10 min

ø XIC ratio

10 min

Cluster

14, 15 42 Uncharacterized protein KIAA1522 0.7269 0.729 0.36864 0.335 6

58 144 Gamma-adducin 0.49226 0.395

0.596 8

70 171 Ephrin-B2 0.43235 0.399 0.60178 0.764 8

83 average

192 NDRG family member 3 1.3234 1.296 1.8098 1.119 3

108 271 Cordon-bleu protein-like 1 1.63 1.425 1.4734 1.438 3

122 332 ATP-dependent helicase 1 0.8905 0.800 0.5495 0.685 2

1454 353 Ribosomal L1 domain-containing protein 1 0.56322 0.780 1.0401 0.932 1

0

20

40

60

80

100

1 2 3

Nu

mb

er

of

pe

pti

de

s

Number of replicates the peptide was quantified in

MaxQuant results

XIC evaluation

Results

78

Phospho Peptide ID

Protein Group


2 min

ø XIC ratio 2 min

ø MQ Ratio 10 min

ø XIC ratio

10 min Cluster

155

358 Microtubule-associated protein 1B

0.49318 0.538 0.67488 0.827 8

151 0.53762 0.526 0.62866 0.799 8

156 other z

0.347

0.663 8

193 average

479 Ribonucleoside-diphosphate reductase

subunit M2 1.7198 1.484 1.8272 1.984 3

198, 199, 200

505 Elongation factor 2 kinase 0.85323 0.839 0.1762 0.299 6

230, 231 556 Growth factor receptor-bound protein 10 0.628 0.766 0.23898 0.196 6

247, 248, 249

594 Restin 1.6032 1.392 0.98818 0.973 2

266, 268 626 Desmoplakin 0.70428 0.752 0.53907 0.572 5

283, 284

674 Protein Shroom2

0.25502 0.924 0.37892 0.304 6

285 0.81926

0.8153

288 0.86763

0.92183

291 680 Formin-binding protein 1-like 1.5433 1.923 1.3621 1.970 3

1485 694 Eukaryotic translation initiation factor 4

gamma, 2 0.96347 1.008 0.53267 0.760 1

308 average

733 Solute carrier family 4 member 4

2.5718 2.975 1.961 2.086 7

310, 311, 1489

3.38 3.780 1.8668 13.512 7

312, 1490 3.7378 5.313 6.0838 10.992 7

322 751 Splicing factor 3 subunit 1 1.0296 0.762 0.30501 0.633 2

337

854 Sodium/hydrogen exchanger 1

0.9766

0.98518

335, 336, 1494

1.377 1.258 4.8495 4.654 7

351 887 Sodium bicarbonate cotransporter 3

1.296 1.630 3.6885 4.970 7

352, 353 1.9614

7.4879

-

376 924 Desmoyokin 0.70065 0.978 0.54608 0.846 1

385, 1499

937 Protein NDRG1

0.30428 0.374 0.52055 0.670 8

382, 383, 1498

0.49816 0.562 0.86751 0.797 8

397 954 Homeobox protein cut-like 2 0.072919

10.941

2

405, 406 971 Microfibrillar-associated protein 1 0.86546 0.968 0.54639 0.711 2

1509 1070 Oxysterol-binding protein 1 0.99574 1.061 1.6298 0.500 2

434, 435, 436, 1510

1093 Microtubule-associated protein tau 1.7823 1.420 1.4405 1.335 3

458

1216 Specifically androgen-regulated gene

protein

0.75082 0.714 0.45613 0.510 5

457 average

0.84778 0.760 0.43814 0.560 5

456 0.86254 0.705 0.41692 0.429 5

523 1378 Oxysterol-binding protein-related protein

11 2.4142 2.559 3.7158 3.920 7

Results

79

Phospho Peptide ID

Protein Group


2 min

ø XIC ratio 2 min

ø MQ Ratio 10 min

ø XIC ratio

10 min Cluster

599 1575 Protein Shroom3 1.9723 1.624 0.75722 0.983 3

606 1591 DNA repair protein complementing XP-C

cells 0.61455 0.989 0.96208 0.967 1

619 1607 Oxysterol-binding protein-related protein 8 0.72935 0.795 0.59206 0.546 5

623, 1559 average

1611 mRNA-decapping enzyme 1A 0.60635 0.728 0.82668 0.792 1

650 1646 Echinoderm microtubule-associated

protein-like 3 0.46296 0.790 0.19637 0.741 1

663 1681 NFATC2-interacting protein 0.60598 0.510 0.56536 0.484 5

683 1717 E3 ubiquitin-protein ligase HUWE1 1.5682 1.054 1.4328 1.280 3

684, 1570

1718 Insulin receptor tyrosine kinase substrate

0.75009 1.041 0.33179 0.680 2

685 average

0.81894 1.009 0.54054 0.478 2

714 1767 La-related protein 1 0.82226 1.115 0.53477 1.245 3

752 1821 Serine/threonine-protein phosphatase 4

regulatory subunit 3A 0.63043 1.314 0.86224 1.023 3

811 1988 E3 SUMO-protein ligase RanBP2 0.57606 1.020 0.54521 0.909 1

821, 1609 1997 Uncharacterized protein C9orf25 0.8935 1.053 0.55104 0.528 2

823 2005 Neprilysin

1.6189 1.574 1.2163 1.344 3

824 2.4429 1.337 1.4294 1.393 3

825 2008 E3 ubiquitin-protein ligase BRE1A 0.24792 0.503 0.56697 0.6230

71 8

894 2188 Serine/threonine-protein phosphatase 1

regulatory subunit 10 0.62514 0.757 0.94695 1.013 1

895 2200 Stromal interaction molecule 1

0.76106 0.714 0.35868 0.406 5

896, 897 0.98022

0.99922

913 2242 Trimethyllysine dioxygenase, mitochondrial 234.48

-

1648 2253 Liprin-beta-2 0.70637 0.472 0.27994 0.182 6

950 2254 Uncharacterized protein C3orf20 4.503

0.91871

-

1651 2264 AT-rich interactive domain-containing

protein 4B 0.89109 0.911 0.57142 0.833 1

973, 1653 2297 Nucleoporin-like protein RIP 0.76733 0.705 0.33271 0.264 6

983 2315 Scavenger receptor class F member 2

0.4186 0.666 0.47497 0.604 5

985 0.68778

0.80021

993

2327 Pyruvate dehydrogenase E1 component

subunit alpha, testis-specific form, mitochondrial

0.80001 0.974 0.47291 0.610 2

994, 1786 average

0.82302 0.877 0.36881 0.449 5

1011 2354 Zinc finger CCCH domain-containing

protein 11A 0.903

1.465 3

1021 2383 Choline-phosphate cytidylyltransferase A 0.3329 0.625 0.42215 0.574 5

1031 2426 Tight junction protein ZO-1 0.63017 0.621 0.43347 0.748 8

1037, 1038

2433 Tetratricopeptide repeat protein 7B 0.59678 0.647 0.56616 0.586 5

1071 2497 Sperm-specific antigen 2 0.47826 0.584 0.44768 0.444 5

Results

80

Phospho Peptide ID

Protein Group


2 min

ø XIC ratio 2 min

ø MQ Ratio 10 min

ø XIC ratio

10 min Cluster

1103 2565 Fibrous sheath-interacting protein 2 7.8342 358.703

0.398 4

1135, 1136, 1137

2628 Cleavage and polyadenylation specificity

factor subunit 2 1.0137 0.994 0.54333 0.672 2

1172 average 2708 Myocyte nuclear factor

0.89936 1.067 2.9563 1.099 3

1170 1.1171 0.961 1.3167 1.170

1190

2772 Stathmin

0.2546 6.377 1.5502 1.508 7

1192 1.0663

1.217

1191 1.3043

1.2142

1203 average

2804 Rho guanine nucleotide exchange factor 16 1.4136 1.739 1.6786 1.416 3

1698 2825 Nuclear factor with BRCT domains 1 1.599 1.129 1.571 1.043 3

1237, 1238, 1239, 1707

average

2870 Bcl-2-associated athanogene 3 0.93734 0.982 0.43142 0.768 1

1258 2927 Nucleoprotein TPR 1.1564 1.137 1.7757 2.135 3

1720 average

2953 Serine/arginine repetitive matrix protein 2

0.1856 1.091 0.93931 1.049 3

1791 0.8267

0.80681

1272 0.86135

0.80147

1276 0.91799

0.88166

1307 0.92361

0.77127

1721 0.94366

1299 0.97494

0.9428

1284 0.97505

0.69208

1291 0.98411

0.80845

1311 1.0061

1.0029

1302 1.0334

1.0302

1285 1.0555

0.9425

1308 1.0708

0.93442

1286 1.0735

0.92703

1281 1.091

0.86751

1292 1.0997

1714 1.1023

0.99185

1280 1.1297

1298 1.1952

1.0356

1722 1.1997

1.1414

1289 1.2544

1.1269

1301 1.2705

0.95957

1346, 1347, 1348

2996 25 kDa protein 14.234

-

Results

81

Phospho Peptide ID

Protein Group


2 min

ø XIC ratio 2 min

ø MQ Ratio 10 min

ø XIC ratio

10 min Cluster

1349 2997 Cytoplasmic linker-associated protein 2 0.76266 0.783 0.54823 0.564 5

1733 3021 Prune homolog 2 (Drosophila) 0.60562 0.814 0.80224 0.864 1

1734, 1735, 1736 3037 TBC1 domain family member 10B

0.68777 0.687 0.39897 0.374 5

1375 1.3216

1.2657

1379 3056 cDNA FLJ51169 0.25724

5.1751

-

1751 3067 cDNA FLJ53530 0.18618 0.028 0.062739 0.052 8

1524 1274;1801;2986

Lamina-associated polypeptide 2 1.5352 1.049 1.5746 0.832 1

490 1.569 1.340 1.7811 1.121 3

1672

2523;3038

Microtubule-associated protein 4

0.75454 0.850 0.47926 0.654 2

1673 0.90128

0.96096

1082 0.95377

0.83942

1671 1.0548

1.3068

1086 2528 RNA-binding protein MEX3C

1.052

0.590 2

765, 766 1844 Probable G-protein coupled receptor 126 2.6047 2.814 0.79713 0.967

148, 149

358 Microtubule-associated protein 1B

0.30781 0.404 0.54825 0.567

142, 144 0.32032 0.326 0.52971 0.562

157, 158, 1458

0.37659 0.379 0.70171 0.653

156 0.38342 0.328 0.5904 0.605

153, 154 0.39001 0.342 0.52132 0.500

141, 140 0.401 0.436 0.63584 0.704

145 0.40927 0.303 0.51695 0.493

1456 0.43918 0.377 0.50824 0.554

146 0.44361 0.403 0.50465 0.403

147 0.46908 0.378 0.64051 0.602

150, 1455 0.47963 0.389 0.49219 0.488

40, 42, 1434

119 Microtubule-associated protein 2

2.5178 2.777 1.9314 1.785

39, 41 2.9963 3.360 1.7499 1.889

1432, 1433

3.5302 3.884 0.41175 3.000

7.4.3.1 K-means clustering

In order to classify phosphopeptides with significant changes in abundance (i.e. peptides marked in

green and blue in Table 11) K-means clustering was performed, while phosphopeptides originating

from regulated proteins (marked in red in Table 11) were generally excluded. For cluster analysis,

normalized XIC ratios were log2 transformed in order to achieve a symmetrical distribution of ratios

Results

82

around zero. For the "control", ratio values were set to zero. In order to optimize clustering

efficiency, different K values were tested. A K value of nine was eventually applied as higher K values

did not result in the formation of new principle clusters and lower K values failed to efficiently

distinguish between peptide profiles. The application of a K value of nine however, also did not lead

to a separation of peptides into cluster showing a distinct trend like for example early responding

peptides and late responding peptides. In addition, the peptides of the same protein are sometimes

associated to different cluster and no functional group of proteins is dominating a certain cluster.

The respective cluster distribution of all the regulated phosphopeptides identified in this work

applying a K value of nine is shown in Supplementary Figure 6.

7.4.3.2 Kinase motif prediction

For all confidently identified phosphorylation sites in regulated phosphopeptides, a kinase motif

prediction was conducted in order to evaluate which kinases might be involved in mediating β-

ionone stimulation effects. In addition to the prediction tool of the MaxQuant software, two further

prediction tools, namely NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) and KinasePhos

(http://kinasephos.mbc.nctu.edu.tw/), were employed. Interestingly, the three approaches resulted

in rather inconsistent data (e.g. for only three peptides the same kinase was predicted),

demonstrating that these prediction tool merely give a hint as to which kinase might be involved, but

predictions cannot be considered as proof for the involvement of a predicted kinase. In Table 12, the

numbers of motifs predicted for each kinase by the three tools are listed.

Table 12: Number of kinase motifs predicted for regulated phosphorylation sites identified in this work by

use of three different algorithms.

Kinase MaxQuant NetPhosK KinasePhos CK1 23 3 1

PKA 11 15 4

CK2 7 7 4

AKT/PKB 6 5 1

GSK3 5 5

CDK1 5

13 CDK2 4

cdk5

18

CHK1 4

WW GroupIV 3

CAMK2 3

2

NEK6 3

Polo box 2

FHA1 Rad53p 2

ERK/MAPK/p38 MAPK 2 5 1

Polo box 2

PKD 2

Results

83

Kinase MaxQuant NetPhosK KinasePhos PKC 1 10 4

FHA KAPP 1

NIMA 1

CHK2 1

RSK

3

ATM

1 1

DNAPK

1

cdc2

1 1

other_1

5

other_4

1

In order to learn more about the potential signaling mechanism and effects triggered by β-ionone,

the 73 regulated phosphoproteins identified in this work were imported into Ingenuity Pathway

Analysis software (INGENUITY Systems, Inc, Redwood City, CA, USA). This software predicts networks

and interconnections between proteins on the basis of known protein-protein interactions.

Regulated phosphoproteins were automatically assigned by the software in four functional networks

leaving only 8 proteins without a classification (See networks in Supplementary Figures SP 8 – SP 11).

The majority of proteins could be assigned to one of the four following categories:

(1) Cancer, Cellular Growth and Proliferation;

(2) Infection Mechanisms, Dermatological Diseases and Conditions;

(3) Cell Morphology and Molecular Transport and

(4) Cellular Assembly, Organization and Tissue Development

The remaining 8 proteins were grouped into the category "Others".

Protein classification further revealed that many of the regulated phosphoproteins are involved in

processes like cell morphology changes, outgrowth, polarization and cytoskeleton dynamics (e.g.

protein IDs 1171, 119, 2927, 358, 2772, 594, 854, and 2997). All these processes are involved in the

migration of cells (Ridley et al. 2003). Therefore, a migration assay was established by Dr. Gelis at the

Department of Cell Physiology (Head: Prof. Hatt; Ruhr-Universität Bochum) and the potential

influence of β-ionone on the migration behavior of LNCaP cells was investigated (Figure 30).

Using the migration assay, it was tested how many cells migrate from the upper chamber of a

Boyden chamber apparatus through a membrane when different stimuli were applied on both sides

of the membrane. When the same conditions are implemented on both sides of the membrane

(negative control, Figure 30 A), a rather low number of cells migrated through the membrane. By

adding 20 % FBS to the lower chamber, this migration rate was significantly increased (positive

control, Figure 30 B). This effect is abolished, when β- ionone was added simultaneously to the upper

chamber (Figure 30 C). Addition of both β- ionone and 20% FBS led to enhanced migration in

comparison with the control (D). When β- ionone was solely added to the lower (E) or upper

Results

84

chamber (F), no significant changes in cell migration were observed. Therefore, β-ionone did not

function as a chemoattractive or repellent cue in this migration assay, but instead was able to

effectively inhibited serum-directed migration.

Fig. 31: Results of Boyden chamber migration assay. LNCaP cells are seeded in the upper chamber of a

Boyden chamber apparatus. Different stimuli are applied to the upper (stated above bars) or lower chamber

(stated below bars). Bars inciate the number of cells migrated from the upper chamber through the membrane

per field of view. As a negative control, no stimulus was applied (A) to either of the chamber, resulting in a low

number of migrating cells. As a positive control 20 % FBS was applied to the lower chamber (B), which

increased the number of cells, which migrated through the membrane significantly. β-ionone neither appeared

to have attractive nor repellent function when applied alone ore in one chamber with FBS (D-F), but it

decreased significantly the FBS (lower chamber) induced migration when applied simultaneously to the upper

chamber (C).

(A) (B) (C) (D) (E) (F)

Discussion

85

8 Discussion

8.1 Phosphoproteomics applied to the mouse olfactory epithelium

The olfactory system is able to distinguish a vast variety of odorants. In order to be able to respond

to vital odorant cues emanating from, for example, food or mating partners, the olfactory system has

to filter for redundant background stimuli. This process is called habituation. The mechanisms

underlying this habituation have extensively been studied in the olfactory bulb, the olfactory cortex

and in higher brain regions (Sanchez-Andrade et al. 2005; Wilson 2009). About effects of short- and

long-term odorant exposure on the olfactory epithelium (OE), however, little is known to date. To

gain new insights into the molecular events triggered in the OE by continuous or pulsed odorant

exposure, Barbour et al. (Barbour et al. 2008) conducted a quantitative proteomics study of OEs from

mice exposed to the odorant octanal following two different long-term treatment regimes. Resulting

differences in protein expression levels were subsequently analyzed using the differential in gel

electrophoresis (DIGE) technology in combination with two-dimensional (2D) gel electrophoresis and

mass spectrometry (MS) for protein identification.

The initial events leading to the formation of the observed long-term changes in protein abundance

are signal transduction cascades triggered by the activation of G-protein coupled receptors upon

odorant binding, which also lead to the activation of transcription of certain target genes. Since

signaling cascades are largely mediated by phosphorylation and dephosphorylation of signaling

proteins, it is of high importance to study these events in detail. Such studies may eventually result in

a better understanding of the mechanisms by which the observed long-term changes in protein

abundance were induced by odorant treatment. Analogous to the 2D DIGE study performed by

Barbour et al., 2D gel electrophoresis in combination with the fluorescent phosphospecific dye Pro-

Q®Diamond (Pro-Q) was employed in this work to study phosphorylation events occurring in the

mouse OE upon short-term odorant treatment. The use of the commercially available Pro-Q stain in

gel-based quantitative phosphoproteomics was first published in 2003 (Steinberg et al. 2003). As it is

specific for serine and threonine as well as tyrosine phosphorylation and since, according to the

manufacturer, it is supposed to have a dynamic range of three orders of magnitude, this stain

promised to be a valuable tool for comparative, quantitative phosphoproteomics. In fact, this dye has

successfully been employed in diverse phosphoproteomics studies using plants (Agrawal and Thelen

2006; Chitteti and Peng 2007), microorganisms (e.g. Dimina et al. 2009), mammalian cells and tissue

(Gannon et al. 2008), body fluids (You et al. 2010) and for kinase target screens (Orsatti et al. 2009).

Discussion

86

In this work, the applicability of the Pro-Q stain was first assessed using standard proteins. These

experiments showed that phosphorylated standard proteins were specifically stained with the dye

while non-phosphorylated standard proteins were not (Fig. 7, Chapter 5.1). However, MS/MS

analyses of tryptic peptides generated from Pro-Q- in comparison to Coomassie-stained standard

proteins showed severe limitations in the reliable identification of proteins following Pro-Q-staining

(Table 3, Chapter 5.1). To compensate for potential bias of different MS instruments towards either

of the stains, three different types of instruments were employed, namely an ion trap (HCT plus), a

triple quadrupole (Q-Trap 4000) and a quadrupole-TOF-MS (Q-Star XL). The results were similar for

all the three instruments, thus pointing to its limited applicability to MS-based phosphoprotein

analysis. In fact, both phosphorylated and non-phosphorylated proteins were generally identified

with significantly lower scores as well as lower sequence coverage following Pro-Q staining.

Therefore, it can be assumed that the Pro-Q stain itself or the staining procedure interfered with

subsequent sample preparation steps (e.g. tryptic in-gel digestion, peptide extraction from the gel

matrix) or efficient ionization and MS(/MS)-based peptide analysis. The latter may be caused by

unknown modifications added to the proteins during the staining procedure and thus, preventing

their identification by database searches. However, reevaluation of the MS/MS data using PTM

Explorer, a tool implicated in ProteinScape 1.3 which allows screening for unknown modifications,

did not reveal any unknown modification or mass difference frequently occurring in the data set.

Thus, a low efficiency in tryptic digestion or the extraction of tryptic peptides generated from Pro-Q-

stained proteins is assumed.

The ability to mass spectrometrically identify phosphorylation sites in proteins was further examined.

Evaluation of the results achieved for S1-casein upon staining with Pro-Q revealed that only four

peptides and not a single phosphorylated peptide were identified. In contrast, a significantly higher

number of peptides (10) including also a phosphopeptide in two different phosphorylation states

were identified in Coomassie-stained S1-casein (Table 4, Chapter 5.1). These results underscore the

limited applicability of the Pro-Q stain to the identification of phosphorylation sites in proteins by

MS.

The Pro-Q stain was further tested for the phosphospecific staining of 2D gels of OE lysates. This

allowed the staining of potentially phosphorylated proteins and the visualization of differences

between odorant-treated versus non-treated ("control") mouse OE. However, in agreement with

results obtained for standard proteins described in this work, MS analyses of differentially stained gel

spots resulted in low identification rates and no phosphorylation sites were detected (Figure 9 and

Table 5, Chapter 5.1, for further details, refer to Supplementary Table SP 25).

In addition to the impaired detectability of tryptic (phospho)peptides from Pro-Q-stained proteins by

MS, two reproducibility issues emerged (Figure 10, Chapter 5.1):

Discussion

87

The quality of the Pro-Q staining itself was found to be not reproducible. Although it was

carried out strictly according to the protocol, in some cases, the staining revealed distinct

spot patterns, in other cases, it resulted in high background staining, was blurry, and no spot

pattern was visible.

If gels exhibited low background and clearly discernible spots, the observed spot patterns

varied in intensity as well as in the pattern itself between replicates suggesting poor

reproducibility of sample preparation.

With regard to the latter issue, the variability in spot patterns was most likely due to the variable

duration of the OE sample preparation. Mice were exposed to the odorant, dissected and the OE was

subsequently prepared from the septal bone and turbinates before it was placed in Ringer´s buffer

containing phosphatase and protease inhibitors. This procedure was time-consuming, varied in

length (5-15 min) and efforts to develop a more reproducible protocol did not lead to an

improvement. In summary, limited reproducibility of the Pro-Q staining as well as the length of time

needed for the multi-step preparation of mouse OE prohibited comparative phosphoproteomic

analysis of such biological samples and thus, alternative approaches had to be sought. A further

major drawback of this approach was the observed interference of the Pro-Q stain with the MS-

based identification of (phospho)proteins, rendering it inapplicable to phosphoprotein analyses on a

larger scale.

Discussion

88

8.2 Establishment of a global phosphoproteomics strategy to study

olfactory receptor-mediated signaling events in LNCaP cells

8.2.1 Establisment and refinement of the phosphoproteomics strategy

Based on the results of the initial work on mouse OE applying 2-D gel methodology combined with

Pro-Q staining, a new concept for the study of olfactory receptor (OR)-mediated signaling events was

designed. Major aspects of this new concept were the use of a biological system which allowed for a

sample preparation under easier to control conditions as well as the establishment of a gel-free

phosphoproteomics strategy comprising modern phosphopeptide enrichment and MS/MS

technologies combined with adequate bioinformatics methods for the efficient identification and

relative quantification of several hundreds of phosphoproteins including the reliable determination

of their phosphorylation sites.

To this end, the use of cell culture systems is favorable as cells can be grown and treated at well-

controlled conditions and protein samples can reproducibly be prepared. Thus, the issues of

technical and biological variability are attenuated in cell culture systems. Olfactory receptor neurons

(ORN) however, can only be cultivated as primary culture, for which many animals are required

(Ronnett et al. 1991). In addition, although expression of olfactory receptors (ORs) in heterologous

cell culture systems has been shown (Neuhaus et al. 2009), it is mostly not possible to express ORs in

heterologous mammalian cell culture systems as they exhibit poor trafficking to the plasma

membrane because of retention in the endoplasmatic reticulum (McClintock and Sammeta 2003;

Bush and Hall 2008). Most interestingly, a member of the olfactory receptor family, the prostate

specific G-protein coupled receptor (PSGR), is endogeneously expressed in the prostate and

especially in prostate cancer cells (Xu, L. L. et al. 2000). Furthermore, the ligands activating this

receptor were recently identified using the prostate carcinoma cell line LNCaP (Neuhaus et al. 2009).

As the function of the PSGR in prostate and LNCaP cells still remains largely elusive, it was of great

interest to study PSGR-mediated signaling cascades via quantitative phosphoproteomics in this work.

Although signaling of the PSGR cannot be conveyed to signaling of olfactory receptors in the OE, it is

expected to provide valuable new insight into the mechanisms underlying the development of

prostate cancer.

Discussion

89

Phosphopeptide sample preparation

Major challenges of global phosphoproteomics studies are, on the one hand, based on the fact that

protein phosphorylation usually occurs in low amounts and at substoechiometric level (Aebersold

and Goodlett 2001) and, on the other hand, that phosphorylated peptides typically exhibit lower

ionization efficiencies than their unphosphorylated counterparts (Craig et al. 1994; Hunter and

Games 1994). In this work, strong cation exchange chromatography (SCX) for prefractionation in

combination with titanium dioxide (TiO2) affinity capture for the efficient enrichment of

phosphorylated peptides from complex samples was used. This methodological setup was reported

to be applicable to the MS-based analysis of phosphoproteins on a larger scale (Gruhler et al. 2005;

Wu et al. 2007; Thingholm and Larsen 2009).

To refine and evaluate distinct steps of this multidimensional strategy, a highly phosphorylated

protein sample was generated by treatment of LNCaP cells with the typrosine phosphatases inhibitor

sodium orthovanadate (OV). OV treatment of cells leads to accumulation of phosphorylation in

general (Samet and Tal 2010). Since initial experiments according to published protocols (Ballif et al.

2004; Mazanek et al. 2007) resulted in the identification of only a few phosphopeptides

(Supplementary Table 6, Figure 32) each step of the entire workflow was reevaluated and refined

(see Figure 11 in Chapter 5.2). The optimization of digestion, desalting, SCX chromatography and TiO2

buffers lead to an approx. 4-fold higher number of identified phosphopeptide. A further approx. 3-

fold increase in phosphopeptide identification rates was achieved by the development of a TiO2 batch

process (Figure 32). By applying a refined NL-triggered ETD method and improved bioinformatic

parameters, the number of identified phosphopeptides was further increased more than 1.5-fold.

Thus, by thorough refinement of this multistep strategy, phosphopeptide identification rates were

increased 18-fold.

Discussion

90

Fig. 32: Number of unique phosphopeptides identified in five SCX fractions during method development and

refinement. For method development, the entire workflow was independently repeated using lysates of OV-

treated LNCaP cells. In order to compare the phosphopeptide enrichment efficiancies, eluates from the first

five SCX fractions of the respective experiment were analyzed using the HCT instrument. In the initial

experiment, only eight phosphopeptides were identified. The number of identified phosphopeptides was

increased through subsequent optimization of each step in the workflow to 145 (18-fold).

MS analysis of phosphorylated peptides

For the analysis of phosphopeptide-enriched samples from LNCaP cell lysates, two different MS

analysis regimes combined with appropriate bioinformatics tools for data interpretation were

established and evaluated. In this work, two alternative fragmentation techniques using two types of

MS instruments were employed for phosphopeptide analysis: (1) electron transfer dissociation (ETD)

on a HCT Ultra ion trap instrument and (2) multistage activation (MSA) on an LTQ-Orbitrap

instrument. Data generated by both instruments differ in the accuracy of precursor mass

determination and, as both instruments are from different manufacturers, in different raw data

formats. A bioinformatics platform allowing for the combined analysis of both kinds of raw data is

not available yet. Accordingly, an individual bioinformatics workflow had to be implemented for each

set of MS data. Data generated by the LTQ-Orbitrap system were processed with MaxQuant (Cox and

Mann 2008), an algorithm specifically developed for high resolution MS data which includes peptide

and protein identification as well as phosphorylation site assignment. For data derived from the HCT

instrument, a software solution which combines identification and PTM site determination was not

available. Therefore, the analysis workflow using two subsequent processing steps, namely the

identification via ProteinScape 1.3 with Mascot 2.2 and site determination using the SLoMo

algorithm, was established. As a result of data analyses, different optimum parameter sets were

8 21

93

34

145 #

iden

tifi

ed p

ho

sph

op

epti

des

Discussion

91

determined for CID and ETD spectra (Figure 19, Chapter 5.2.7). However, since only one distinct set

of parameters can be set in ProteinScape for a respective data set, the parameters used in this work

resemble a compromise. This underscores the need for the development of new algorithms enabling

to apply distinct parameter settings for CID and ETD data within ProteinScape.

Application of the two distinct MS analysis strategies resulted in the identification of a similar

number of phosphopeptides, while both data sets were highly complementary (Figure 18, Chapter

5.2.6). This result emphazises the need for the application of different MS analysis strategies for

improved coverage of the phosphoproteome. Hence, both strategies were subesequently employed

for the study of the phosphoproteome of OV-treated LNCaP cells.

8.2.2 Global phosphoproteomics analysis of orthovanadate-treated

LNCaP cells

Following the refinement and the establishment of the MS-based phosphoproteomics strategy in this

work, the phosphoproteome of OV-treated LNCaP cells was studied in detail. To this end, each

strategic track was conducted in two independent experiments (for workflow see Figure 20, Chapter

5.3.1). Overall, more than 2000 unique phosphopeptides containing 2569 unique phosphorylations

sites were identified using only 200 µg starting material per experiment. The localization of 1639

phosphorylations sites were confirmed. Therefore, these results present the most comprehensive

phosphoproteomic study reported for LNCaP cells to date (Giorgianni et al. 2007; Chen, L. et al.

2010). Regarding the number of identified phosphopeptides and determined phosphorylation sites,

the data reported here are in agreement with the recent literature (Table 13).

Table 13.: Number of phosphopeptides identified in state-of-the-art phosphoproteomics studies. In addition,

the principal method and sample amount used in the respective work are listed.

Publication Method Sample amount # Identified

phosphopeptides

Mäusbacher et al. (Mausbacher et al.

2010)

Lectin affinity purification, 1D gel or in solution digestion followed by

TiO2 enrichment 6 x 4 mg 1700

Nakagami et al. (Nakagami et al. 2010)

Ti/Zr metal oxide affininty purification, Fe-IMAC

0.2 g wet cell weight

6919

Ye et al.(Ye et al. 2010) IMAC/IMAC 20/50 µg 1000

Nie et al. (Nie et al. 2010)

SCX and SAX followed by TiO2 enrichment

500 µg 2705

Olsen et al. (Olsen et al. 2010)

1D Gel followed by TiO2 enrichment, SCX followed by TiO2 enrichment

and direct TiO2 enrichment

10-15 mg per condition

∑50-75 mg 20443

Discussion

92

Phosphopeptides identified from SCX fractions

In both strategic tracks, a small aliquot of each SCX fraction was analyzed without further

phosphopeptide enrichment. In experiment 1, SCX fractions were analyzed by NL-triggered ETD

measurements using the HCT Ultra instrument, whereas in experiment 2, samples were analyzed on

the LTQ-Orbitrap instrument. Both analysis setups provided similar rates of identified

phosphopeptides from SCX fractions. However, applying the LTQ-Orbitrap/MaxQuant platform, far

more peptides and proteins were identified, demonstrating the enhanced sensititvity of this track. It

is of interest to note, however, that the number of identified phosphopeptides was similar for both

tracks employing different MS technologies. This suggests, that NL-triggered ETD analysis using the

HCT ion trap instrument is generally suited for the identification of phosphopeptides from complex

samples. Nevertheless, the overall number of identified phosphopeptides was far lower than what

could be expected regarding current literature. Hence, further enrichment for phosphopeptides was

necessary.

By comparing the number of phosphopeptides identified before and after TiO2 enrichment, a median

enrichment factor of about 10-fold was determined (Tables 8 and 9, Chapters 5.3.2 and 5.3.3). This

factor cannot be compared to other studies, as to the best of the author's knowlegde no statement is

given on enrichment factors in the literature. However, since the number of phosphorylated peptides

identified in this work was at the level of current high quality research publications, similiar

phosphopeptide enrichment efficiencies are assumed.

Performance of different analysis platforms

When evaluating data generated from TiO2-enriched SCX fractions, the phosphoproteomics platform

comprising the LTQ-Orbitrap-MSA setup combined with automated data processing employing the

MaxQuant software performed best in respect to the number of identified phosphorylation sites

(1568 sites for Orbitrap/MaxQuant, 1284 sites for HCT/ProteinScape/SLoMo, Figure 23, Chapter

5.3.4) and particularly to the number of confirmed sites (1248 for Orbitrap/MaxQuant, 612 sites for

HCT/ProteinScape/SLoMo, Figure 23, Chapter 5.3.4). Although the second platform composed of the

HCT Ultra PTM system with CID and NL-triggered ETD followed by ProteinScape/SLoMo data

evaluation resulted in a lower number of both phosphorylation sites and confirmed sites, the

obtained phosphoproteomic data set was highly complementary (see Figures 22 and 23, Chapter

5.3.4). Thus, combination of both data sets resulted in an higher coverage of the phosphoproteome

of OV-trated LNCaP cells.

The reasons for this disparate performance of the two different analysis platforms are likely to be

manifold. The fact, that both techniques are highly complementary indicates that only a part of the

Discussion

93

phosphoproteome has been covered. The LTQ-Oribtrap system providing shorter duty cycle times

and therefore, more fragment spectra was shown to led to the identification of a higher number of

(phospho)peptides and (phospho)proteins. Beyond that, the high peptide mass accuracy of 3 ppm

Orbitrap data has been shown to significantly improve phosphopeptide identification rates (Olsen

and Macek 2009; Timm et al. 2010).

The Orbitrap/MaxQuant workflow also performed best regarding the number of confirmed

phosphorlyation sites. This result was unexpected, as ETD fragmentation should allow for confident

site assignments, as the phosphate moiety remains at the respective amino acid residue. Whether

the gain in site localization accuracy in the Orbitrap/MaxQuant workflow is due to the mass accuracy

and sensitifity of the instrument, the fragmentation technique employed or the bioinformatic

methods applied can not be exhaustively answered in this work and needs to be further investigated.

A main reason is likely to be the use of the MaxQuant software, an established software specifically

developed to analyze LTQ/Orbitrap-MS/MS data. The observed inferiority of the

HCT/ProteinScape/SLoMo platform may be due to limited applicability of ProteinScape for the

concerted analysis of CID and ETD MS/MS data since no separate parameter sets for CID and ETD

spectra could be set. Furthermore, with the applied methods on the HCT ion trap, the precursor mass

could only be determined with a mass accuracy of about 300 ppm. This might be an additional factor

contributing to inferior identification results.

When comparing the reprocucibility of both fragmentation techniques in experiment 1 and 2 (Table

9, Chapter 5.3.3), datasets derived from CID/NL-triggered ETD scans on the HCT instrument showed a

significantly smaller overlap (7 - 24 %) than the respective data sets obtained by MSA on the LTQ-

Orbitrap instrument (19 - 51 %). As the HCT is supposed to have a 2 - 5 times lower sensitivity

(empirical value) than the Orbitrap instrument, mainly abundant proteins should be identified and

therefore, an higher overlap in data was expected. However, the NL-triggered ETD measurement

regime applied in this work was attended by rather long duty cylce times. For three CID scans

followed by three subsequent ETD scans, duty cycle times were in the range of six to eight seconds,

depending on the abundance of precursor ions. In contrast, the four MSA scans required a duty cycle

time of only two seconds. One can therefore hypothesize that the comparably small overlap between

CID/NL-triggered ETD data sets was based on considerable undersampling rather than sensitivity

issues.

Identified Phosphoproteins and new phosphorylation sites

In addition to the establishment and evaluation of an effective MS-based phosphoproteomics

strategy, this work aimed at extending our current knowledge of the phosphoproteome of the

human prostate carcinoma cell line LNCaP. As a result of this effort, 164 new phosphorylation sites in

Discussion

94

101 proteins were confidently identified (Supplementary Table SP 1). Amongst them were

phosphorylation sites in proteins which are relevant in the context of prostate cancer. Early

androgene-dependent stages of prostate cancer are well treatable with high 5-year survival rates.

One of the leading players in the progression of these early stages to androgene-independent cancer,

for which no cure exists today, is the androgene receptor (AR) (Bonkhoff and Berges 2010; Ferlay et

al. 2010). Proteins which are involved in the regulation of AR activity or are in turn regulated by AR,

are therefore highly relevant targets for improving our understanding of mechanisms underlying

prostate cancer progression. In this work, several new phosphorylaton sites in AR and cancer

associated proteins were identified. In Table 14, example proteins containing newly identified

phosphorylation sites are listed and subsequently discussed in brief. For a complete list, please refer

to Supplementary Table SP 1.

Table 14: Selected proteins containing newly identified phosphorylatio sites. In this table, proteins are listed

exemplarily for which hitherto unknown phosphorylation sites could confidently be identified in this work. For

a complete list, please refer Supplementary Table SP 1.

SwissProt accession

IPI accession phospho-peptide ID

identified by MSA/MaxQuant

identified by CID/ETD/SLoMo

protein name

Q9BW04 IPI00028392 502 X

Specifically androgen-regulated gene protein 532 X

P49792 IPI00221325 422

X E3 SUMO-protein ligase

RanBP2

P55327 IPI00619958 40 X

Tumor protein D52

Q5JWU6 IPI00399265 189

X Tumor protein D52-like 2,

evidence at transcript level

P31751 IPI00012870 478 X

RAC-beta serine/threonine-protein kinase

P49006 IPI00641181 36 X X MARCKS-related protein

P78527 IPI00296337 492

X DNA-dependent protein kinase catalytic subunit

Q1KKQ2 IPI00782950

207 X

Protein kinase D1 210 X

251 X

Q32Q12 IPI00604590 260 X

Nucleoside diphosphate kinase

Q6IQ55 IPI00217437 160

X Tau-tubulin kinase 2

Q9H4A3 IPI00397590 215

X Serine/threonine-protein

kinase WNK1

Q9BUB1 IPI00063234 78 X

PRKAR2A protein 80 X

Discussion

95

Two newly identified phosphorylation sites were found in the specifically androgene

regulates gene protein (SARG, Supplementary Table SP 20). This protein is upregulated by

androgen receptor activation and has recently been reported to be a putative androgen

receptor itself (Lin et al. 2000; Steketee et al. 2004). The role of this potential androgene

receptor however, is completely unknown to date.

Activation of the AR is mediated by phosphorylation (Guo et al. 2006), ubiquitination (Xu et

al. 2009) and sumoylation status (Poukka et al. 2000). Sumoylation has also been implicated

in several diseases including prostate cancer (Wu and Mo 2007). In this work, a hitherto

unknown phosphorylation site for a E3 SUMO protein ligase has been identified

(Supplementary Tables SP 17 and 18).

10 new phosphorylations sites in 7 different protein kinases could be detected

(Supplemental Tables SP 17, 18 and 20). Kinases play a critical role in cell signaling and have

emerged as the protein family implicated most in cancer (Manning 2009; Lopez-Otin and

Hunter 2010). For the Protein Kinase D1 (PKD1) no phosphorylation site has been known so

far, but in this work three new phosphorylation sites for this kinase have been confidently

identified (Supplementary Table SP 20). PKD1 has been reported to be associated with a

transcriptional complex containing the AR and the promotor sequence of the Prostate

Specific Antigen (PSA). In addition PKD has also been reported to influence AR function (Mak

et al. 2008).

Amongst many other interesting proteins, for which we identified new phosphorylation sites,

two members of the tumor protein D52 family (TPD52 and TPD52L2) might be additionally

noteworthy (Supplementary Tables SP 17, 18 and 20). Gene expression of TPD52

(alternatively PrLZ4) is reported to be regulated by androgene. TPD52 is involved in

migration, progression and survival of protate cancer cells (Rubin et al. 2004; Ummanni et al.

2008; Li et al. 2009; Wang, R. et al. 2009). TPD52L2 has not been as extensively studied as

TPD52, but recently its relative expression level has been found to be useful in the prediction

of prostate cancer progression after radical prostatectomy (Zhao et al. 2010).

8.2.3 Concluding remarks

In this work, a phosphoproteomics analysis workflow was established, which allows for the efficient

enrichment of phosphopeptides from LNCaP cells. Thereby 2569 unique phosphorylation sites, from

2095 phosphopeptides and 726 phosphoproteins could be identified from orthovanadate treated

LNCaP cell lysates. This work therefore presents the most comprehensive phosphoproteomics study

Discussion

96

in LNCaP cells so far (Giorgianni et al. 2007: 137 phosphorylation sites; Chen et al. 2010: 600

phosphorylation sites). Amonst the identified phosphorylation sites 164 hitherto unknown sites could

be identified providing new target proteins for further studies on prostate cancer.

Beyond that, the refined phosphopeptide sample preparation and bioinformatic analysis established

in this work provide the basis for a quantitative phosphoproteomics study in order to elucidate PSGR

mediated signaling networks.

Discussion

97

8.3 Quantitative and time-resolved phosphoproteomic study of

β-ionone-treated LNCaP cells

In order to not only find random new sites, but to elucidate new PSGR mediated signaling pathways,

the successfully established phosphoproteomics workflow was combined with quantitative

approaches in the following experiments. Due to the remaining problems with bioinformatic analysis

and the low mass accuracy of HCT ion trap data, only the Orbitrap/MaxQuant workflow was

employed for quantitative experiments.

8.3.1 SILAC labeling

For the quantitative and time-resolved study of PSGR-mediated signaling networks, stable isotope

labeling by amino acids in cell culture (SILAC) was employed (Ong et al. 2002; Ong et al. 2003). LNCaP

cells were metabolically labeled with heavy isotope-containing arginine and lysine using three

different combinations of labeled amino acids. Non-labeled arginine and lysine were used for control

samples (light state) and cells labeled with D4-lysine and 13C6-arginine constituted the medium state

with mass differences of 4 Da and 6 Da, respectively. For heavy labeling, 13C615N2-lysine (mass

difference: 8 Da) and 13C615N4-arginine (mass difference of 10 Da) were incorporated. Thereby, three

distinguishable cell populations were generated, which could be treated differently. Resulting

samples were mixed in a 1:1:1 ratio, with respect to total protein concentration, directly after lysis

and subsequently processed together. This is an immense advantage, as multiple processing steps

would introduce escalating variance in protein abundance. By mixing the samples not after but prior

to further processing, protein ratios originating from the biological experiment are preserved and

artificial differences introduced by handling errors are prevented.

One problem when using SILAC labeling is the conversion of isotopically labeled arginine to other

amino acids, in mammalian cells predominantly to proline (Ong et al. 2003). Additional isotopic peaks

significantly hamper the quantification of SILAC isotopic clusters, especially when not only two but

three different labeling states are implied, as it was in this study. To eliminate arginine to proline

conversion in advance, the composition of labeling media has generally to be optimized.

By reducing the concentration of added arginine in SILAC media to 120 mg/l (conventional RPMI

medium contains 240 mg/l) and by adding an excess of proline (800 mg/l), conversion of arginine to

proline could be reduced to a great extend. Therefore, optimum media conditions were determined,

which were further used for the biological experiment.

Discussion

98

8.3.2 Time resolved and quantitative β-ionone treatment experiment

The PSGR-mediated signal transduction was analyzed at three different time points after stimulation

of the receptor with its specific agonist β-ionone. In a previous study (Neuhaus et al. 2009), the

effects of stimulation of the PSGR receptor with β-ionone in LNCaP cells were monitored with

calcium imaging. Stimulation led to a slow increase in fluorescence over 3-4 min and subsequent high

stationary fluorescence for several more minutes. Therefore, for phosphoproteomic experiments on

PSGR mediated signaling, therefore, samples were collected 2 min and 10 min after stimulation,

resembling time points during ongoing intracellular calcium release and in the stationary

fluorescence phase, respectively.

The combination with triple SILAC labeling allows for the relative quantification of each labeled

peptide. Thereby, changes in the abundance of phosphorylated peptides can be monitored. For

phosphopeptides containing more than one phosphorylation site however, only the abundance

change of the respective phosphopeptide, but not of each single phosphorylation sites can be

monitored by this method.

8.3.2.1 The data set

LNCaP cells with incorporated light amino acids were mock-treated with medium and used as 0 min

treated reference time point. Medium SILAC-labeled cells were treated for 2 min and heavy labeled

cells were treated for 10 min. This experiment was repeated in three independent biological

replicates, resulting in a total identification of 3054 unique proteins and 1154 unique

phosphopeptides. This number of identified phosphopeptides is slightly lower than the number of

phosphopeptides identified in orthovanadate-treated LNCaP cells by the Orbitrap/MaxQuant

workflow, especially as β-ionone treatment experiments were conducted in three rather than in two

replicates. However, as a specific biological stimulus was employed here, rather than an unspecific

maximum accumulation of phosphorlyation in the cell by orthovanadate treatment, this lower

number was to be expected.

8.3.2.2 Reproducibility of observed regulation factors

947 of the identified phosphorylated peptides could be quantified in at least one replicate. Of these,

only high abundant and predominantly not regulated phosphopeptides were quantified in all three

Discussion

99

replicates. But in order to ensure that observed changes in phosphorylation are indeed caused by the

biological stimulus, a triplicate quantification especially of regulated phosphopeptides is desirable.

In search of the reasons why 207 identified phosphopeptides could not be quantified in any replicate

and why candidate peptides were quantified in only one or two replicates, the spectra of these

peptides were inspected manually. Thereby different reasons for the missing values could be

exposed. On the one hand, MaxQuant only quantifys peaks which are baseline-separated (Cox et al.

2009). That means that the intensity has to return to zero between two peaks. Many peptides did

coelute with other peptide species of similar mass, which resulted in overlapping isotopic clusters

and thereby peptides were, in parts, not baseline-separated. Consequently, these peptides were not

quantified by MaxQaunt, although they were present in the sample. On the other hand, MaxQuant

only quantifies peptides for a given replicate, when they are identified by database searches in this

replicate. But low abundant peptides were in some cases not selected for fragmentation and

therefore no fragment spectrum was available for database searching. In these cases, peptides are

also not quantified, although they might be present in the sample. Moreover, for some peptides no

obvious reason could be observed for the missing quantification. If these not quantified peptides

seemed to be regulated judged by the manually inspected spectra, they were considered for further

validation.

Of the quantified phosphopeptides, 99 were found to be significantly up- or downregulated by β-

ionone stimulation according to box plot analysis of the normalized average ratios. Only three

phosphopeptides in the resulting candidate list were identified and quantified in all three replicate

experiments. These three phosphopeptides showed a good reproducibility in all three replicates as

demonstrated in Figure 32 using the example of peptide 122.

Discussion

100

Fig. 33: Reproducibility of phosphopeptide 122 ratios in all three replicate experiments. The ratios of

peptide intensity from control and treated regimes observed after stimulation for 2 and 10 min showed the

same tendency in all three replicate experiments.

Nevertheless, replicated quantification was required for the other candidate peptides in order to

evaluate the regulation significance. For that purpose, an identification-independent method based

on the high mass accuracy and liquid chromatography retention time was established.

By the application of this method, the number of candidate phosphopeptides identified in all three

replicates, could be elevated from three to thirty unique peptides. In addition, the number of

peptides, which have been quantified in two replicates, could also be elevated. An example for the

correlation between Extracted ion chromatograms (XIC) and MaxQaunt ratios is shown in Figure 33.

The single MaxQaunt ratio (replicate 2) and the three XIC ratios calculated for peptide 199 (EEF2K)

are shown. Most of the multiply quantified phosphopeptides showed very similar regulations in all

replicates as shown in Figure 33 for peptide 199. Moreover, the ratios calculated through the XIC

method correlated well with the ratios calculated by MaxQuant, although the method for XIC value

normalization was far less sophisticated as the normalization algorithms used in MaxQuant (for

detailes please refer to Supplementary Table SP 23).

Discussion

101

Fig. 34: Correlation between MaxQuant and XIC ratios. The ratios for peptide 199 (MaxQuant ID,

Supplementary Table SP 23) are depicted. This peptide was quantified by MaxQuant in only one replicate.

Using the XIC quantification method, this peptide could be quantified in all three replicates. The trend observed

for the respective ratios was the similar both amoungst the three XIC quantified replicates as well as between

the MaxQuant ratios and the XIC ratios.

8.3.2.3 K-means clustering

As the set of average XIC values contained more replicates than the MaxQuant set of ratios including

the two manually chosen peptides, and was therefore considered more complete, this set was used

for K-means clustering of the regulation profiles of all candidate phosphopeptides. By clustering the

regulation trends of candidate peptides, a grouping was obtained. For example, there could be

different populations of peptides, like early responders with high regulation factors after 2 min and

low regulation factors after 10 min. This analysis was intended to provide a functional and time-

related grouping of peptides in order to reconstruct the course of signaling events after PSGR

stimulation. The clustering resulted in nine clusters. These clusteres, however, did not deviate

significantly. In addition, there was no correlation observable between the calculated clusters and

the functional protein grouping according to literature information. This could be due to the fact that

many of the candidate peptides were only regulated slightly, whereas some peptides showed very

high regulation factors. This and the relatively low number of candidate peptides could have

hampered a reasonable K-means clustering.

Discussion

102

8.3.2.4 Kinase motifs

According to the global phosphoproteomics data set, phosphorylation sites were considered as being

confidently localized, when the localization score was 0.75 or higher. In order to get a hint of which

kinases might be involved in the signal transduction of PSGR signaling, all confidently identified

phosphorylation sites where searched for known kinase recognition motives. One such prediction

tool is already implicated in the MaxQuant algorithm. Beyond that, two additional online prediction

tools, namely NetPhosK (Blom et al. 2004) and KinasePhos (Huang et al. 2005) were implemented.

The kinase predictions made by these two tools, however, deviated considerably from the

predictions made by the MaxQuant software with only three phosphorylation sites, for which the

same kinase was predicted by every algorithm (Supplementary Table SP 23). These varying results

may be in part due to the fact, that the two web-based prediction tools include only 17 (NetPhosK)

and 11 (KinasePhos) kinases in their predictions. On the exact algorithm underlying predictions from

MaxQuant, no information could be found. The observed discrepancies between motif predictions,

diminish the significance of the predictions reported by the respective tools. However, the most

frequently occuring kinases (Table 12, Chapter 5.4.3) also appeared in interaction networks of

candidate proteins assigned by the IPA software. Therefore, one might speculate, that these three

kinases, namely, CDK1/2, CK2 and PKA might be involved in the transcuction of β-ionone-triggered

PSGR signaling. The Src kinase, which has also been assigned to a network of candidate proteins by

IPA, was recently found to be involved in PSGR-mediated signaling (Spehr 2010). Peptides

phosphorylated by the Src kinase, however, are not likely to be detected in the experimental setup

employed here. Src is a tyrosine kinase whereas the experimental setup is biased to serine and

threonine phosphorylation. As TiO2 enrichment works equally well for serine, threonine and tyrosine

phosphorylated peptides, the resulting set of phosphopeptides, resembles the naturally occurring

composition of the phosphoproteome, which is approximately 1800 : 200 : 1 for serine : threonine :

tyrosine phosphorylation (Mann et al. 2002). In fact, 36 out of 1154 identified phosphopeptides

contained potentially phosphorylated tyrosine residues, of which only eight phoshorylation sites

could confidently be assigned to tyrosine. In order to monitor changes in tyrosine phosphorylation

upon β-ionone treatment, a specific enrichment for tyrosine phosphorylated peptides would be

necessary.

Discussion

103

8.3.2.5 Functional categories of regulated phosphoproteins

According to IPA, proteins determined to be significantly regulated upon β-ionone treatement

appear to be involved in different cellular functions such as proliferation, cell motility, response to

infection, cancer, cell morphology, regulation of cytoskeleton and others. The functional

classification and description of the networks generated by IPA, however, greatly overlapped.

Therefore, proteins with regulated phosphorylation sites were grouped into six distinct functional

categories based on published data: a) Signal transduction, b) Adhesion, Junctions, Cell-cell contact,

c) Cytoskeleton proteins, d) Nuclear factors, DNA repair, Cell cycle progression, e)

Protein/Biomolecule synthesis, RNA processing and f) Others. The number of candidate proteins

assigned to the respective categories is depicted in Figure 34. However, although some functional

information was available for most proteins, the function of the phosphorylation sites which were

found to be regulated in this work is unknow for almost each detected site.

Fig. 35: Functional categories of proteins exhibiting regulated phosphorylation sites. All proteins exhibiting

phosphorylation sites, found to be regulated upon β-ionone treatment were classified in six categories

according to published data. The distribution of candidate proteins to the respective categories is depicted

here as well as the concrete number of assigned proteins

8.3.2.6 Functional category: Signal transduction

In category a), proteins are grouped which are involved in the transduction of external signals to

different target loctions in the cell. In total, 12 phosphoproteins containing 16 significantly regulated

phosphopeptides were grouped in this category termed “Signal transduction” (table 15).

Discussion

104

Table 15: List of phosphoproteins containing significantly regulated phosphopeptides grouped together in

the “signal transduction” category, including gene and proteins name, protein information, phosphopeptide ID

and regulation factors. For further details please refer to Supplementary Table SP 23.

Signal transduction

gene name

protein name protein information phospho-peptide ID

regulation factor 2 min


GRB10 Growth factor receptor-

bound protein 10

negative regulator of insulin and Wnt signaling; negative regulator of proliferation; highly overexpressed in tumours; positive regulator of Akt downstream of PI3 kinase; found phosphorylation was shown to be necessary for 14-3-3 binding

230, 231 -1.59 -4.18

MME Neprilysin

can slow down proliferation and surpress tumorigenicity of prostate cancer cells; phosphorylation shown on S and T in N-terminus; phosphorylated by CKII, interacts with LYN-SRC related kinase and FAK

823 1.62 1.22

824 2.44 1.43

SLC9A1 Sodium/hydrogen

exchanger 1 scaffold for signaling complexes; Akt substate (pS648); binds do

Ca2+Calmodulin 335, 336,

1494 1.38 4.85

RSL1D1 Ribosomal L1 domain-containing protein 1

negatively regulates PTEN, thereby promoting cell proliferation 1454 -1.78 -0.96

SERPINF1 25 kDa protein potential splice isoform of Pigment epithelium derived factor

(PEDF) 1346, 1347,

1348 14.23 ---

BAG3 Bcl-2-associated

athanogene 3 forms complex with HSP70; anti-apoptotic; involved in

migration 1237, 1238, 1239, 1707

-1.07 -2.32

PRUNE2 Protein prune homolog 2 regulator of Rho signaling; targets RhoA/RhoC 1733 -1.65 -1.25

SMEK1 Serine/threonine-protein phosphatase 4 regulatory

subunit 3A Suppressor of MEK, involved in chemotaxis and cell polarity 752 -1.59 -1.16

IRTKS Insulin receptor tyrosine

kinase substrate

Belongs to IRSp53-like family, substrate of insulin receptor, binds to Rac; promotes actin assembly; role in filopodia

dynamics; CDC42 effector; function and binding to 14-3-3 influenced by phosphorylation at T340/360

684, 1570 -1.33 -3.01

685 average -1.22 -1.85

C1ORF116 Specifically androgen-regulated gene protein

SARG, potential androgene receptor

458 -1.33 -2.19

457 average -1.18 -2.28

456 -1.16 -2.40

STIM1 Stromal interaction

molecule 1 senses calcium depletion from intracellular stores and initiates

uptake 895 -1.31 -2.79

Growth factor receptor-bound protein 10 (GRB10)

The Growth factor receptor-bound protein 10 (GRB10) belongs to the Grb7 family of adaptor

proteins lacking intrinsic enzymatic activity. These adaptor proteins have been shown to connect

various cell surface receptors to downstream signaling pathways (Han et al. 2001). GRB10 has been

extensively studied in the context of insulin signaling and growth, e.g., in cancer (O'Connor 2003).

Apart from its binding affinity to various receptors, GRB10 binds different intracellular target

proteins. For example, it recruits Nedd4 to the insulin receptor, leading to polyubiquitination and

subsequent internalization of the receptor (Monami et al. 2008). Furthermore, it interacts with the

kinases Raf and MEK, thereby transducing external signals, which activate the insulin receptor

(Nantel et al. 1998). The functions of GRB10 as inhibitor or activator of insulin and other signaling

pathways are discussed controversially. This conflict may be due to the existence of at least four

Discussion

105

isoforms of GRB10, which might have opposing effects (Jahn et al. 2002). In addition, GRB10 is known

to be phosphorylated on multiple serine and tyrosine residues, of which only some have been

located so far. Tyrosine phosphorylation of GRB10 is mediated by Tec, Src and Fyn kinases (Mano et

al. 1998; Langlais et al. 2000), whereas the serine residues S150, S418 and S476 are phosphorylated by

p42/44 MAPK (Langlais et al. 2005). Liu and coworkers also reported as well that Grb10 is

phosphorylated on a number of further serine residues, which could not be mapped to distinct sites

in this study.

Following β-ionone treatment, S426 or S428 of GRB10 were identified as exhibiting decreasing

phosphorylation upon treatment. Both serine residues are located in the 14-3-3 protein binding

motif *RSVSEN* and, by Duyster and coworkers phosphorylation of S428 was found to be

phosphorylated in vivo and necessary for the binding of GRB10 to 14-3-3 (Urschel et al. 2005). In

addition, GRB10 was described to be associated with RAC-alpha serine/threonine-protein kinase

(Akt) activity and involved in PI3K-mediated signaling events. Figure 35 illustrates the regulatory

circuit involving phosphorylation of S428 of GRB10 proposed by Urschel et al. (2005). Phosphorylated

GRB10 binds to 14-3-3. Upon dephosphorylation, 14-3-3 dissociates from GRB10 and GRB10 is free to

bind to Akt kinase. The GRB10-Akt complex is recruited to the membrane, where Akt is

phosphorylated by PDK1/2. The phosphorylated Akt in turn phosphorylates GRB10. Thereby GRB10

again dissociates from Akt and can again be bound to 14-3-3. The observed decrease in Grb10

phosphorylation on S428 through β-ionone stimulation thus may be the consequence of reduced Akt

and/or PKD1/2 activity.

Fig. 36: Regulatory mechanism proposed by Urschel et al. (2005) involving the phosphorylation of GRB10 on

S428

. According to the proposed mechanism Grb10 phosphorylated on S428

is associates with 14-3-3 in the

cytoplasm. GRB10 can be dephosphorylated by PP2A, which leads to a release from 14-3-3. Free GRB10 binds

to Akt kinase and recruits it to the membrane. There, Akt is phosphorylated by PDK1/2 and activated Akt kinase

in turn phosphorylates Grb10. Thereby 14-3-3 can again bind to Grb10, which releases the interaction with Akt.

Adapted fromUrschel et al. 2005

Discussion

106

Membrane metalloendopeptidase (MME)

The membrane metalloendopeptidase (MME), also termed CD10, Nephrilysin or neutral

endopeptidase 24.11 (NEP), is a cell surface peptidase expressed in numerous tissues. MME

mediates its tumor suppressor effect by two ways, firstly by cleaving physiologically active peptide

ligands such as bradykinin, oxytocin, Leu- and Met-enkephalins, neurotensin, bombesin or

endothelin-1 (Checler et al. 1984; Shipp et al. 1991; Kenny 1993) and secondly by other mechanisms

mediated by the intracellular domain, which are summarized in Figure 36:

a) Recruitment of signaling proteins to the plasma membrane

b) Scaffold for the formation of larger protein complexes, it binds, for example, tyrosine protein

kinase Lyn, which binds to the p85, which binds to the p110 subunits of PI3K

c) Recruits phosphatase PTEN to the plasma membrane leading to prolonged phosphatase

activity

Fig. 37: Intracellular signaling effects mediated by MME. Apart from cleaving signaling peptides, MME alters

signal transduction events by prolonging PTEN phosphatase activity and forming MME-Lyn-p85 complexes,

which hamper p85 downstream signaling.

Loss or decrease in MME expression has been linked to several cancers including prostate cancer

(Papandreou et al. 1998). Moreover, restored expression of MME was shown to exhibit tumor

suppressor effects on androgene-independent prostate cancer cells (Dai et al. 2001). MME can be

phosphorylated by casein kinase II (CKII) on serine and threonine residues located in the N-terminal

region (Ganju et al. 1996). In the β-ionone stimulation experiment, phosphorylations on S4 and S6

have been identified. While the phosphorylation at S4 was slightly elevated, phosphorylation at S6

was upregulated 2.4 fold after 2 min of exposure to β-ionone. The specific effects of this

phosphorylation on the functionality of MME have not been investigated yet.

Discussion

107

Sodium/hydrogen exchanger 1 (SLC9A1)

The solute carrier family 9 member 1 protein is a Na+/H+ exchanger (termed SLC9A1 or NHE1) which

is essential for the regulation of intra- and extracellular pH. Its activity is required for migration,

invadopodia formation in metastasis and cell survival (Cardone et al. 2005; Schelling and Abu Jawdeh

2008). Apart from its function as Na+/H+ exchanger, it has also been described to provide a scaffold

for the assembly of signaling complexes. It binds to phosphatidylinositol 4,5-bisphosphate (PIP2)-rich

membrane regions, and in turn, binds to numerous other proteins such as Ca2+ Calmodulin (CaM),

Nck interacting kinase (NIK) and proteins of the ezrin/radixin/moesin (ERM) family. In complex with

ERM proteins, NHE1 provides a membrane-bound structural anchor for actin filaments promoting

actin bundle formation and adhesion (Baumgartner et al. 2004).

NHE1 is phosphorylated at diverse serine and threonine residues by different kinases.

Phosphorylation of S648, for example, is mediated by Akt and is involved in actin filament

reorganization upon growth factor signaling (Snabaitis et al. 2008). Phosphorylations at positions

693, 770, 771, 766, 785 and 779 of the amino acid sequence are catalyzed by ERK (Liu et al. 2004).

S785 was found to be phosphorylated in this work, too, but it was not regulated by β-ionone

treatment. The three phosphorylation sites found to be highly up-regulated after 10 min of β-ionone

treatment (S602, T603 and S605), have not been functionally characterized yet. However, they reside in a

stretch in the amino acid sequence, which is associated with NIK binding. NIK also phosphorylates

NHE1, but at a different not exactly mapped site (Yan et al. 2001). But NIK is brought in close

proximity to ERM proteins, when bound to NHE1 and phosphoylates and thereby activates the ERM

proteins and, thus, leads to F-actin stabilization.

Ribosomal L1 domain-containing protein 1 (RSL1D1)

Ribosomal L1 domain-containing protein 1 (RSL1D1) was found to be a negative regulator of PTEN.

Whether this effect is based on direct or indirect interaction, however, has not been sufficiently

studied yet (Ma et al. 2008). In human fibroblast cells, active PTEN leads to cell cycle arrest and

initiates senescence. RSL1D1 surpresses this effect (Ma et al. 2008). In accordance with this

observations, expression levels of this protein are high in young and low in senescent cells (Guo et al.

2004). Consequently, decreased levels of RSL1D1 and subsequent acticity of PTEN may be a potential

reason for senescense. In this work, four phosphorylation sites of RSL1D1 have been identified (S361,

S649, T464, T465). Of these, only S361 was regulated. It showed significant down-regulation after 10 min

of β-ionone stimulation. Although no data about phosphorylations of RSL1D1 have been reported

yet, it is tempting to speculate, that reduced phosphorylation also leads to less PTEN inhibition.

Discussion

108

25kDa Protein

For the 25 kDa Protein no information is available. However, it is very homologous to the Pigment

epithelium derived factor (PEDF) protein (also termed SERPINF1 protein) and may be an alternatively

spliced, shortened version with a different N-terminus (Figure 37). This unique N-terminal peptide of

the 25 kDa Protein (amino acid residues 3-18) was found to be differentially phosphorylated upon β-

ionone treatment on serine residues at positions 3, 6 and 8 of the amino acid sequence.

PEDF MQALVLLLCIGALLGHSSCQNPASPPEEGSPDPDSTGALVEEEDPFFKVP 50

25kDa Protein --------------------------------------------------

PEDF VNKLAAAVSNFGYDLYRVRSSMSPTTNVLLSPLSVATALSALSLGAEQRT 100

25kDa Protein --------------------------------------------------

PEDF ESIIHRALYYDLISSPDIHGTYKELLDTVTAPQKNLKSASRIVFEKKLRI 150

25kDa Protein -------------------------------------------------M 1

:

PEDF KSSFVAPLEKSYGTRPRVLTGNPRLDLQEINNWVQAQMKGKLARSTKEIP 200

25kDa Protein RSAFSFSVWRTS-------------------------------------- 13

:*:* .: ::

PEDF DEISILLLGVAHFKGQWVTKFDSRKTSLEDFYLDEERTVRVPMMSDPKAV 250

25kDa Protein ---------------RWVTKFDSRKTSLEDFYLDEERTVRVPMMSDPKAV 48

:**********************************

PEDF LRYGLDSDLSCKIAQLPLTGSMSIIFFLPLKVTQNLTLIEESLTSEFIHD 300

25kDa Protein LRYGLDSDLSCKIAQLPLTGSMSIIFFLPLKVTQNLTLIEESLTSEFIHD 98

**************************************************

PEDF IDRELKTVQAVLTVPKLKLSYEGEVTKSLQEMKLQSLFDSPDFSKITGKP 350

25kDa Protein IDRELKTVQAVLTVPKLKLSYEGEVTKSLQEMKLQSLFDSPDFSKITGKP 148

**************************************************

PEDF IKLTQVEHRAGFEWNEDGAGTTPSPGLQPAHLTFPLDYHLNQPFIFVLRD 400

25kDa Protein IKLTQVEHRAGFEWNEDGAGTTPSPGLQPAHLTFPLDYHLNQPFIFVLRD 198

**************************************************

PEDF TDTGALLFIGKILDPRGP 418

25kDa Protein TDTGALLFIGKILDPRGP 216

******************

Fig. 38: Amino acid sequence alignment of the 25kDa Protein and the human PEDF protein. Peptides

identified by database searching are colored. Peptides shared by both proteins are marked in green. Unique

peptides are highlighted in yellow. Identified phosphosites are marked in red. Both proteins have 100 %

homology in the C-terminus, whereas the the N-terminus is different.

Neither the phosphorylation sites nor their functions are known so far. For its homologue PEDF,

three phosphorylation sites are known at positions 24, 114 and 227 of the PEDF amino acid

sequence, which are essential for the determination if the protein exhibits neurotrophic or

antiangiogenic function (Maik-Rachline et al. 2005). The neurotrophic effects of PEDF are reported to

be mediated by its N-terminus (Alberdi et al. 1999; Aymerich et al. 2001). In contrast to this, PEDF

was reported to be one of the most potent antiangiogenesis factors, inhibiting endothelial cell

Discussion

109

migration and neovascularization (Dawson et al. 1999). However, the phosphorylation sites found to

be regulated upon β-ionone treatment correspond to none of the previously reported sites.

Bcl-2-associated athanogene 3 (BAG3)

BAG3 belongs to the Bag family of co-chaperones that regulate the ATPase activity of heat-shock

protein 70 chaperones. It has a general anti-apoptotic function and has been shown to be involved in

migration, adhesion and invasion in various cancer model systems (Doong et al. 2000; Iwasaki et al.

2007; McCollum et al. 2010). In cells undergoing apoptosis, BAG3 is cleaved by caspases (Wang et al.

2010). S285 and T289 were found to be down-regulated upon β-ionone treatment. Neither on the

function of these phosphorylation sites nor on the function of the domain they are located in,

information are available.

Protein prune homolog 2 (PRUNE2)

PRUNE2 is a RhoA- and RhoC-interacting protein, which is up-regulated in prostate cancer tissue and

metastases (Clarke et al. 2009). It was shown to inhibit proliferation (Soh and Low 2008), potentially

via RhoA and RhoC, which are involved in oncogenic transformation and metastasis, respectively

(Ridley 2004). The two phosphorylation sites (S595, S597), which have been found to be slightly down-

regulated upon PSGR activation, are hitherto unknown and no functional characterization has been

reported yet.

Serine/threonine-protein phosphatase 4 regulatory subunit 3A (SMEK1)

The serine/threonine-protein phosphatase 4 regulatory subunit 3A (SMEK homologue 1) is a

homologue of SMEK. SMEK is a suppressor of MEK1, which is a MAP kinase kinase acting upstream of

ERK1/2 and which is involved in the establishment and maintenance of cell polarity, chemotaxis and

cancer cell proliferation (Fremin and Meloche 2010). The phosphorylation site identified in SMEK1

(S741) was down-regulated upon β-ionone treatment.

Insulin receptor tyrosine kinase substrate (IRTKS)

IRTKS is a protein involved in actin bundling (Millard et al. 2007). It is homologous to the IRSp53

protein, which is involved in filopodia generation (Gupton and Gertler 2007). It was discovered as

insulin receptor substrate and has been described to interact with Rac1 (Miki et al. 2000) and Cdc42

(Gupton and Gertler 2007), thereby promoting cell motility and invasiveness in cancer (Funato et al.

Discussion

110

2004). In contrast to IRSp53, IRTKS only interacts with Rac1 and may be implicated in lamellopodia

rather than filopodia generation (Millard et al. 2007).

Phosphorylation of the threonine residues 340 and 360 of IRSp53 was shown to be required for

binding of IRSp53 to 14-3-3 (Robens et al. 2010). Phosphorylation sites identified in this work for

IRTKS (T257 and S266) are not in a region homologous to the 14-3-3 binding region in IRSp53. The sites

in IRTKS were found to be down-regulated upon treatment with β-ionone.

Specifically androgen-regulated gene protein (SARG)

C1ORF116 transcription is regulated by androgen and, therefore, the resulting gene product is

termed specifically androgen-regulated gene (SARG) (Cleutjens et al. 1997) which has been shown to

be implicated in prostate cancerogenesis (Lin et al. 2000). It has high expression levels in both

prostate tissue and in LNCaP-1F5 cells. In 2004, Trapman and coworkers identified SARG as potential

androgen receptor (Steketee et al. 2004). They found homology in the domain structure of SARG and

the classical AR. Of the three SARG phosphorylation sites found to be regulated in β-ionone

stimulation experiments (S502, S508, S532), two have already been found in the global

phosphoproteomics analysis (S502, S532) conducted for method evaluation previously in this work. All

three were found to be downregulated upon treatment by β-ionone. These three phosphorylation

sites have not been reported previously and nothing is known about their function. Phosphorylation

of AR occurs on the one hand on tyrosine residues by Src kinase, which is associated with prostate

cancer progression (Guo et al. 2006). Alternatively, AR can be phosphorylated by further kinases such

as Ack1, which regulates its DNA binding activity (Mahajan et al. 2007). A number of phosphorylated

serine residues in AR have been identified in large-scale phosphoproteomics experiments

(Oppermann et al. 2009). However, they have not been functionally characterized yet. In addition to

phosphorylation, AR functionality is also regulated by sumoylation (Poukka et al. 2000). Interestingly,

in the β-ionone treatment study in this work (Chapter 6.3.2.9) the SUMO ligase RANBP2 was found to

be phosphorylated on multiple sites. Further factors known to be involved in the regulation of AR

activity are coactivators such as TMF1. Phosphorylated peptides of this protein were also found to be

downregulated, but with a factor just outside the score cut-off for significant regulation (0.75/0.595).

Stromal interaction molecule 1 (STIM1)

Upon depletion of intracellular calcium stores, the endoplasmatic reticulum protein stromal

interaction molecule 1 (STIM1) associates with the ORAI protein in the plasma membrane, thereby

initiating store-operated calcium entry (SOCE). A number of other proteins like TRPCs and SERCA are

recruited to this protein complex and are involved in the intricate multistep process of SOCE (Vaca

Discussion

111

2010). Two phosphorylation sites of this protein have been identified in the β-ionone study in this

work. Phosphorylation of S618 was not regulated by β-ionone, whereas S575 phosphorylation was

significantly down-regulated upon stimulation. These distinct phosphorylation sites have not been

identified in STIM1 before. However, phosphorylation of S486 and S668 was reported to suppress SOCE

by inhibiting STIM1 localization to the plasma membrane (Smyth et al. 2010). Putney and coworkers

also suggested other phosphorylation sites in the region C-terminal to amino acid residue 482 may

be involved in this inhibition. Hence, the downregulation of S575 phosphorylation upon stimulation

may lead to an activation of SOCE.

8.3.2.7 Functional category: Adhesion, Junctions and Cell-cell contact

Nine proteins found to be regulated in this work have been described in association with different

kinds of junctions, which are involved in cell-cell contacts and adhesion. Apart from cytoskeletal

proteins (category c), junction proteins are essential for the tissue integrity, matrix adhesion and cell

polarity. Inversely, the disassembly of junctions is required for migration, motility and metastasis

(Friedl and Gilmour 2009; Friedl and Wolf 2010; Herath and Boyd 2010). Therefore, they are of

special interest in the context of prostate cancer.

Table 16: List of proteins which contain differentially regulated phosphopeptides and which are involved in

the formation of differend kinds of junctions. Proteins exhibiting altered expression levels are marked in

purple. Gene names and protein names are listed, as well as additional protein information, the

phosphopeptide ID of the regulated peptides and corresponding regulation factors. For further details refer to

Supplementary Table SP 23.

Adhesion, Junctions, Cell-cell contact gene name




GPR126 Probable G-protein

coupled receptor 126 receptor of adhesion family; essential for Schwann cell myelination 765, 766 2.60 -1.25

DSP Desmoplakin phosphorylation in C-terminal domain negatively regulates

interaction with intermediate filaments 266, 268 -1.42 -1.86

EFNB2 Ephrin-B2 Tumor angiogenesis; tissue organization 70 -2.31 -1.66

NDRG1 N-myc downstream

regulated 1

pro-apoptotic tumor suppressor; enhances recycling and stability of E-cadherin; phosphorylation does not interrupt binding to E-

cadherin/beta-catenin

385, 1499 -3.29 -1.92

382, 383, 1498

-2.01 -1.15

NDRG3 NDRG family member 3 overexpression leads to enhanced migration and tumor growth 83 1.32 1.81

TJP1 Tight junction protein ZO-

1

directly interacts with Shroom2 at tight junctions; translocates from cytoplasm to membrane to form cell-cell-contacts, forms complex with catenins; membrane associated guanylate kinase homologue

1031 -1.59 -2.31

PPFIBP2 Liprin-beta-2 Involved in cell matrix-interaction; involved in focal adhesions 1648 -1.42 -3.57

Discussion

112

gene name




SHROOM3 Protein Shroom3 recruits proteins to cell junctions; involved in changes in cell shape 599 1.97 -1.32

SCARF2 Scavenger receptor class

F member 2 SCARF homologue; role in differentiation and aggregation 983 -2.39 -2.11

SLC4A4 solute carrier family 4,

sodium bicarbonate cotransporter, member 4

phosphorylated by PKA; interacts with ATPase; involved in acid-base homeostasis; colocalizes with E-cadherins but not with ZO1,

potential role in cancer

308 average 2.57 1.96

310, 311, 1489

3.38 1.87

312, 1490 3.74 6.08

G-protein coupled receptor 126 (GPR126)

The G-protein coupled receptor 126 (also termed VIGR or DREG) is an orphan receptor and member

of the LNB-TM7 subfamily of class B GPCR (Moriguchi et al. 2004; Stehlik et al. 2004). LNB-TM7

receptors all comprise an unusually large extracellular domain; their physiological function is not well

characterized yet. The characteristics of the complex extracellular domains, though, suggest that

these proteins play a role in cell migration and adhesion. Furthermore they may possess similar

signaling properties of class B peptide hormon GPCRs (Stacey et al. 2000). GPR126 was

independently identified and characterized in 2004 by two groups. Stehlik et al. found that cell

surface expression is inducible in endothelial cells and therefore termed it vascular inducible G-

protein coupled receptor (VIGR, Stehlik et al. 2004). Moriguchi et al. found that expression of

GPR126 is developmentally regulated and that it can be proteolytically cleaved at two sites in the

extracellular N-terminus, indicating that it may have a dual role as receptor and, regarding the

proteolytic products, as ligand or signaling molecule. They coined the term “develpomentally

regulated G-protein coupled receptor“ for GPR126 (DREG, Moriguchi et al. 2004). In 2009, Monk et

al. reported that GPR126 functions as a receptor to initiate myelination of Schwann cells via transient

elevation of cAMP (Monk et al. 2009).

A GPR126 peptide comprising two phosphorylation sites, S1165 and S1168, was found to be significantly

up-regulated (2.6 fold) after 2 min. These phosphorylation sites, of which one has been identified in a

global phosphoproteomics study of mitotic phosphorylation in HeLa cells before (Dephoure et al.

2008), reside within the cytoplasmic C-terminus. So far, information about their functional

importance are not available.

A closer look at the abundance ratios of all three unique peptides assigned to GPR126 revealed a

consistent up-regulation in the same range of both phosphorylated and non-phosphorylated

peptides indicating that not only the phosphopeptide but the abundance of the entire protein was

up-reguated. This could be due to an up-regulation of total protein abundance within 2 min

(Dieterich et al. 2010). Beyond that, the reason for the higher abundance of GPR126 following

stimulation of LNCaP cells with β-ionone may be a change in subcellular location. It is conceivable

Discussion

113

that the receptor is internalized upon stimulation and is thereupon enriched in the cytosolic fraction,

which was analyzed in the phosphoproteomics study. Stimulus-induced receptor internalization via

arrestin binding is known to require the phosphorylation at the receptors cytoplasmic tail (Doherty

and McMahon 2009). Since the GPR126 was found to be phosphorylated at the cytoplasmic tail in

this work, this may indicate internalization.

In summary, the GPR126 may be an interesting candidate for further investigations to analyze a

potential involvement in PSGR-mediating signaling.

Desmoplakin (DSP)

Desmoplakin is one of the main components of desmosomes. Desmosomes are adherent junctions,

which interconnect cells, thereby preventing tissue from being damaged by mechanic stress.

Desmoplakin is an intermediate filament (IF) binding protein, which links IFs to the plasma

membrane at desmosomes (Godsel et al. 2004). In differentiated cells, it also recruits microtubili to

desmosomes via ninein (Lechler and Fuchs 2007). By linking desmosomes to the cytoskeleton,

desmoplakin contributes to the stability and adhesion in tissue. The loss of desmosomes or

desmosomal proteins results in reduced cell-cell contact as well as increased motility and

invasiveness of cancer cells (Depondt et al. 1999). Desmoplakin and other desmosomal proteins such

as plakophilin-I were recently suggested as potential biomarker for early stages and progression of

squamous cell carcinoma, since their abundance at cell-cell junctions decreases during cancer

progression (Narayana et al. 2010). A recent proteomics study found desmoplakin, amongst others,

to be down-regulated in the transition of epithelial cells with restricted migration to mesenchymal

cells with increased motility and invasiveness (Chen, Y. S. et al. 2010).

A potential PKA phosphorylation site has been described at the C-terminal S2849 of DSP. Its

phosphorylation has been shown to inhibit binding between DSP and IFs (Stappenbeck et al. 1994).

A phosphopeptide containing this serine residue was identified. However, the phosphorylation could

not confidently be assigned to a specific site and the peptide was not quantifiable. Instead, another

phosphopeptide containing phosphorylated S2820 and S2825 was found to be significantly

downregulated during β-ionone treatment. Both phosphorylation sites have been identified

previously, but only in large-scale phosphoproteomic studies without further functional

characterization.

Since S2820 and S2825 are also located as well in the C-terminus proximate to the characterized S2849 ,

one might speculate that phosphorylation of S2820 and S2825 may also be implicated in IF binding and

that the observed down-regulation could strenghten DSP to IF binding, thereby contributing to the

rigidity of the cell and inhibition of migration.

Discussion

114

Ephirn-B2 (EFNB2)

Ephrins are membrane-bound ligands of Ephrin tyrosine receptor kinases. Via the ephrin

receptor/ephrin system, cells “sense” their cell-to-cell density, resulting in repulsion, adhesion or

migration and eventually leading to self-organization of cells into tissues via complex bi-directional

phosphotyrosine-mediated signaling cascades (Xu, Q. et al. 2000; Jorgensen et al. 2009). Increased

expression of ephrins and their receptors have been observed in many types of cancer (Campbell and

Robbins 2008). The ligand ephrin-B2, which is an integral membrane protein, has been reported to

be involved in tumor growth and tumor angiogenesis (Noren et al. 2004; Hainaud et al. 2006).

Maekawa et al. (Maekawa et al. 2003) demonstrated that soluble ephrin-B2 induced migration in

endothelial cells via ephrin B receptor signaling in targeted cells. In cells expressing ephrin-B2 ligands,

its reverse signaling was shown to mediate endothelial tip cell guidance, thereby promoting tumor

angiogenesis (Sawamiphak et al. 2010). Moreover, Bochenek et al. (Bochenek et al. 2010)

demonstrated, that ephrin-B2 can also modulate endothelial cell motility in a receptor independent

way, since constituitive overexpression of ephrin-B2 alone was sufficient to induce cell morphology

changes.

Tyrosine phosphorylation in the extracellular domain of EFNB2 promotes cell migration and invasion

(Nakada et al. 2010), whereas phosphorylation at a serine residue at the C-terminus regulates AMPA

receptor trafficking (Essmann et al. 2008). The phosphorylation site S260, identified in this work, was

found to be downregulated by β-ionone treatment. Its functional relevance, however, remains to be

elucidated.

N-myc downstream regulated proteins 1 and 3 (NDRG1/NDRG3)

The N-myc downstream regulated protein family is comprised of the four proteins, namely NDRG1-4.

All members contain a conserved α/β hydrolase-fold region but lack hydrolyse enzyme function.

NDRG proteins are involved in differentiation and show increasing expression levels from birth to

adulthood. They have a tumor-suppressive function and are frequently found to be differentially

regulated in different cancers (Melotte et al. 2010). NDRG1 expression, for example, is reversely

correlated with survival in prostate cancer, since overexpression of NDRG1 leads to reduced

invasiveness. According to Kachhap et al. (Kachhap et al. 2007), NDRG1 is involved the vesicular

transport and recycling of E-cadherin in prostate cancer, thereby stabilizing E-cadherin and leading to

reduced motility. In contrast, overexpression of NDRG3 in prostate cancer leads to increased

proliferation and migration (Wang, W. et al. 2009; Melotte et al. 2010).

In 2000, Miyata and coworkers found that NDRG1 is phosphorylated and predicted at least seven

phosphorylation sites (Agarwala et al. 2000). In addition, they showed, that the phosphorylation level

Discussion

115

of NDRG1 was lowest, when PKA and CaMKII kinases were blocked, suggesting that these kinases are

involved in phosphorylating NDRG1. In a proteomics study of the interactome of NDRG1 (Tu et al.

2007), proteins of both protein synthesis and the proteasomal degradation pathways have also been

identified as interaction partners of NDRG1 in LNCaP cells. Furthermore, they also found MME (see

chapter 3.3.4.2) to interact with NDRG1, as well as the cell adhsion molecules β-catenin and E-

cadherin. In addition, they reported phosphorylation of NDRG1 at S330 and T366 , which was

prominently mediated by PKA. The phosphorylation level at these two sites did not influence the

binding of NDRG1 to β-catenin and E-cadherin. In the β-ionone study with LNCaP cells,

phosphorylation of S330 was found to be downregulated. Another peptide containing three

phosphorylation sites at S362, S364 and additional phosphorylation site in close proximity to both

serines, that could not be specified, was also found to be downregulated after β-ionone treatment.

Since T366 has already been reported to be phosphorylated by Tu et al. (2007), it may be the

unidentified phosphorylated residue in this peptide. So far, nothing is known about the functional

importance of these phosphorylation sites.

For NDRG3, phosphorylations were found to be regulated at S331 and at S334/335. For the latter

localization different peptide have been observed, which support one or the other localization. These

sites have been reported previously, but in large scale proteomics studies (Dephoure et al. 2008;

Mayya et al. 2009), without functional characterization. The phosphopeptide containing these sites

was upregulated following β-ionone treatment.

Amino acid sequence alignment of NDRG1 and NDRG3 shows that the regions in both proteins

containing the observed phosphorylation sites are highly conserved among these proteins but not

identical (Figure 38). The converse regulation in phosphorylation level of both proteins, might reflect

their opposing functions.

Discussion

116

NDRG3 -MDELQDVQLTEIKPLLND-KNGTRNFQDFDCQEHDIETTHGVVHVTIRG 48

NDRG1 MSREMQDVDLAEVKPLVEKGETITGLLQEFDVQEQDIETLHGSVHVTLCG 50

*:***:*:*:***::. :. * :*:** **:**** ** ****: *

NDRG3 LPKGNRPVILTYHDIGLNHKSCFNAFFNFEDMQEITQHFAVCHVDAPGQQ 98

NDRG1 TPKGNRPVILTYHDIGMNHKTCYNPLFNYEDMQEITQHFAVCHVDAPGQQ 100

***************:***:*:*.:**:*********************

NDRG3 EGAPSFPTGYQYPTMDELAEMLPPVLTHLSLKSIIGIGVGAGAYILSRFA 148

NDRG1 DGAASFPAGYMYPSMDQLAEMLPGVLQQFGLKSIIGMGTGAGAYILTRFA 150

:**.***:** **:**:****** ** ::.******:*.*******:***

NDRG3 LNHPELVEGLVLINVDPCAKGWIDWAASKLSGLTTNVVDIILAHHFGQEE 198

NDRG1 LNNPEMVEGLVLINVNPCAEGWMDWAASKISGWTQALPDMVVSHLFGKEE 200

**:**:*********:***:**:******:** * : *::::* **:**

NDRG3 LQANLDLIQTYRMHIAQDINQDNLQLFLNSYNGRRDLEIERPILGQNDNK 248

NDRG1 MQSNVEVVHTYRQHIVNDMNPGNLHLFINAYNSRRDLEIERPMPG---TH 247

:*:*:::::*** **.:*:* .**:**:*:**.*********: * .:

NDRG3 SKTLKCSTLLVVGDNSPAVEAVVECNSRLNPINTTLLKMADCGGLPQVVQ 298

NDRG1 TVTLQCPALLVVGDSSPAVDAVVECNSKLDPTKTTLLKMADCGGLPQISQ 297

: **:*.:******.****:*******:*:* :**************: *

NDRG3 PGKLTEAFKYFLQGMGYIPSASMTRLARSRTHSTSSSLGSGESPFSRSVT 348

NDRG1 PAKLAEAFKYFVQGMGYMPSASMTRLMRSRTAS-GSSVTSLDGTRSRSHT 346

*.**:******:*****:******** **** * .**: * :.. *** *

NDRG3 SN-------QSDGTQESCESPD--VLDRH------------QTMEVSC 375

NDRG1 SEGTRSRSHTSEGTRSRSHTSEGAHLDITPNSGAAGNSAGPKSMEVSC 394

*: *:**:. ..:.: ** ::*****

Fig. 39: Amino acid sequence alignment of the proteins NDRG1 and NDRG3 by ClustalW. Unique peptides

identified by database searching are coloured in yellow. Identified phosphosites are marked in red. For both

proteins, phosphorylated peptides have been identified and found to be regulated by β-ionone treatment.

Tight junction protein ZO- 1 (TJP1)

Tight junction protein ZO-1 (briefly ZO-1) belongs to the family of membrane-associated guanylate

kinases-like proteins. On the one hand, it has been reported to interact with different junctional

transmembrane proteins such as occludins and catenins as well as with cytoskeletal proteins like

actin filaments (Itoh et al. 1997) or Shroom2 ((Etournay et al. 2007), chapter 3.3.6), thereby cross-

linking the cytoskeleton to the membrane and stabilizing the respective junctions, which leads to a

decrease in cell motility (Bauer et al. 2010). On the other hand, ZO-1 has been shown to act as a

scaffolding protein which recruits other proteins such as signaling molecules, adapters and

transcriptional regulators to the cytoplasmic surface of different kinds of junctions. In addition, ZO-1

is able to associate with regulatory molecules and shuttles between the cytoplasm and the nucleus.

There is evidence that, apart from its role in stabilizing junctions, ZO-1 is implicated in cell signaling

and cell cycle progression. Furthermore, ZO-1 is downregulated in many cancers (Bauer et al. 2010).

PKC-mediated tyrosine phosphorylation of ZO-1 has been shown to disrupt occludin-ZO-1 and

catenin/cadherin-ZO-1 complexes, but the exact site of phosphorylation was not determined (Rao et

al. 2002). The phosphorylation of S168 was found to be involved in the targeting of ZO-1 to either

junctions or lamellae and thereby in the regulation of lung cancer cell migration (Tuomi et al. 2009).

Discussion

117

Following β-ionone treatment of LNCaP cells, the phosphorylation of S912 was gradually

downregulated. This phosphorylation site has been identified before but has not been functionally

studied yet (Cantin et al. 2008; Dephoure et al. 2008).

Protein tyrosine phosphatase receptor type f polypeptide-interacting protein-binding protein 2

(PPFIBP2)

Protein tyrosine phosphatase receptor type f polypeptide-interacting protein-binding protein 2

(PPFIBP2 or Liprin-beta 2) binds to alpha-liprins, which in turn bind to LAR family protein tyrosine

phosphatases. Liprins are proposed to be involved in the disassembly of focal adhesions and in

axonal guidance (Serra-Pages et al. 1998). Since their first characterization, liprins and especially

beta-liprins have not been further investigated. Liprin-beta 1 was found to be essential for lymphatic

vessel integrity in Xenopus species (Norrmen et al. 2010).

One phosphopeptide of liprin-beta 2 was found to be downregulated after 10 min of β-ionone

treatment. Although the phosphorylation site could not be localized confidently, it is most likely at

T510 or S512. Both sites have not been described yet; however, a phosphorylation at S512 is proposed by

similarity prediction in the uniprot database (http://www.uniprot.org/uniprot/Q8ND30).

Shroom3

Shroom3 was first described in 1999 as an actin binding protein required for neural tube

morphogenesis in mice. It was named after the “mushroom”-like phenotype, which characterizes

embryos carrying mutations in this gene (Hildebrand and Soriano 1999). In 2006, the four related

proteins Shroom, APX, APXL, and KIAA1202 were combined to a protein family and named Shroom3,

Shroom1, Shroom2 and Shroom4, respectively. All shroom proteins are involved in the

morphogenesis of epithelial cells. Shroom3 has been suggested to be required for apical constriction

and apico-basal elongation during embryo morphogenesis, regulating this process via interaction

with myosin II (Hildebrand 2005). Shroom3 physically binds Rho kinases and recruits them to apical

junctions (Nishimura and Takeichi 2008).

In the β-ionone study, two phosphorylation sites have been found in Shroom3, namely S910 and S1221.

While the S910 phosphorylation level remains constant during β-ionone treatment, phosphorylation at

S1221 was first upregulated after 2 min and then downregulated after 10 min. Although these

phosphorylation sites have been reported previously, they have not been functionally characterized

yet (Dephoure et al. 2008).

Discussion

118

Scavenger receptor class F member 2 (SCARF2)

SCARF2 is a homologue of the endothelial cells scavenger receptor (Scarf), which mediates the

binding and degradation of acetylated low density lipoprotein. Beyond its potential role in cell

aggregation (Ishii et al. 2002), nothing is known of the functions of SCARF2. In SCARF2, four

phosphorylations sites have been identified, of which two (S687, S698) were downregulated upon

treatment.

Solute carrier family 4, sodium bicarbonate cotransporter, member 4 ( SLC4A4)

The electrogenic sodium bicarbonate cotransporter 1 (SLC4A4, NCB, NCB1 or NCBE1) is co-localized

with E-cadherins in the basolateral membrane of polarized cells. It is constituitively internalized,

which is prevented by PKC inhibitors (Perry et al. 2008; Perry et al. 2009). In renal epithelia cells, the

activity of NBC1 isoform, also identified in the β-ionone study, was shown to be regulated via Src

family kinases, Ras, and the classical MAPK-pathway (Robey et al. 2001). NBC1 mediates the

transport of Na+ and HCO3- across the plasma membrane in a stoichiometry of 3:1. Phosphorylation

at S982 shifts this stoichiomietry to 2:1 (Gross and Kurtz 2002).

In this work, five phosphorylation sites of SLC4A4 have been newly identified. They all show dramatic

up-regulation following treatment with β-ionone.

8.3.2.8 Functional category: Cytoskeleton-associated proteins

The mode of cell migration is determined by cell type and their molecular as well as physical

environment. All modes of migration, however, require a certain degree of deformation of the cell.

These mechanical properties are essential for a cell´s ability to migrate and are mediated by

cytoskeletal proteins (Kumar et al. 2009b; Friedl and Wolf 2010). In addition, transport along

cytosceletal structures is involved in numerous essential cellular processes, for example the

separation of chromosomes during mitosis. Cytoskeletal proteins or proteins associated with the

cytoskeleton, which have been regulated by β-ionone are listed below in Table 17.

Discussion

119

Table 17: Cytosceleton-associated proteins, containing phosphopeptides significantly up- or down-regulated

upon treatment with β-ionone, are listed in this table. Gene and protein names are given as well as some

additional information, the respective MaxQuant ID of the regulated phosphopeptide and their regulation

factors. For detailed information on the respective peptides, please refer to Supplementary Table SP 23.

Proteins, whose abundance is regulated rather than their phosphorylation levels, are coloured in green.

Cytoskeleton-associated Proteins

gene name

protein name protein identification phospho-peptide ID



MAP1B Microtubule-associated

protein 1B

Map1B light chain negatively regulates TP53; it is phosphorylated in phase 1 by GSK3 and CDK5 and in phase 2 by CKII; it is required for netrin signaling; the interaction of Map1B light chain and Pes1

induces a cytoplasmic sequestration of Pes1, which results in a reduction of cell proliferation; Map1B light chain binds to

serotonin typ 3 receptors and accelerates receptor desensitization and reduce steady-state receptor density at the cell membrane.

148, 149 -3.25 -1.82

MAP2 Microtubule-associated

protein 2

links PKA to microtubules; MAP2 stabilizes microtubuli, thereby leading to cell cycle arrest and growth inhibition, microtubuli

binding activity decreases after differentiation, phosphorylation by MAPK and cdc2 weakens tubulin interaction

40, 42, 1434 2.52 1.93

39, 41 3.00 1.75

1432, 1433 3.53 -2.43

151 -1.86 -1.59

MAPT Microtubule-associated

protein tau neurodegeneration; phosphorylation isoforms found in prostate

cancer are similar to alzheimer forms 434, 435, 436, 1510

1.78 1.44

MAP4 Microtubule-associated

protein 4

mitotic machinery, receptor recycling/trafficking, opposing effects to stathmin, phosphorylated by MAP kinase, involved in M-phase

microtubule dynamic 1672 -1.33 -2.09

STMN1 Stathmin phosphorylation mediate entry and exit of mitosis; growth

suppression; accumulation in G2/M phase 1190 -3.93 1.55

CLASP2 Cytoplasmic linker-

associated protein 2

phosphorylated by PKC and GSK3, phosphorylation controls activity, involved in migration through Golgi trafficking and

microtubule dynamics, interacts with actin 1349 -1.31 -1.82

CLIP1 CAP-GLY domain containing linker

protein 1

essential for normal cell cycle progression; holds centrosomes together; microtubule interaction

247, 248, 249

1.60 -1.01

ADD3 Gamma-adducin mediates association of spectrin and actin, is phosporylated by

PKC, increased phosphorylation leads to tumor progression, regulated by Calmodulin

56, 57, 58 -2.03 ----

EML3 Echinoderm

microtubule-associated protein-like 3

required for correct alignment of chromosomes in metaphase 650 -2.16 -5.09

FNBP1L Formin-binding protein

1-like involved in actin organization and migration; interaction with N-

WASP-WIP complex, regulate cell polarity, metastasis 291 1.54 -5.09

SSFA2 Sperm-specific antigen

2 promotes cell adhesion and tumorigenity, cytoskeleton associated

(actin), involved in structural integrity and signal transduction 1071 -2.09 -2.23

AHNAK Desmoyokin cell migration and Ca2+ entry, phosphorylated by PKA 376 -1.43 -1.83

SHROOM2 Protein Shroom2 role in morphogenesis, directly binds to ZO-1, myosin and actin 283, 284 -3.92 -2.64

Microtubule-associated proteins: variants MAP 2, MAP 1B, MAP 4 and MAPT

Microtubules provide a dynamic network, which is essential for chromosome segregation during

mitosis and for the establishment of cell polarity (Zhai et al. 1996). These processes are regulated

and mediated by motorproteins and microtubule- (MT-) associated proteins (MAPs) (Bhat and

Setaluri 2007). All MAPs bind to MTs and promote MT stabilization.

Discussion

120

Microtubule-associated protein 1B (MAP 1B)

MAP 1B is a phosphorylated microtubule-binding protein, which is required, amongst others, for cell

shape determination and axonal transport. It is a phosphoprotein that has extensively been studied

in neuronal cells, where it is down-regulated during devolopment with its expression remaining high

only in dynamic areas of the brain. In neuronal cells, phosphorylated MAP 1B is of highest abundance

in growth cones. Its subcellular localization varies. It has been detected at the plasma membrane or

as soluble protein (Riederer 2007). Its involvement in migration is represented by its participation in

netrin signaling. Netrin structurally resembles laminin and functions as growth factor as well as

attractive or repulsive chemotactic cue (Del Rio et al. 2004). In addition, Map 1B light chain binds to

serotonin type 3 receptors, accelerates receptor desensitization, and reduces steady-state receptor

density at the cell membrane (Sun et al. 2008). Furthermore, MAP 1B is involved in several processes

in the context of cell proliferation and growth. It has been shown to negatively regulate the tumor

suppressor protein TP53 (Lee, S. Y. et al. 2008). Furthermore, the interaction of Map 1B light chain

and Pes1 induces a cytoplasmic sequestration of otherwise nuclear Pes1, which results in a reduction

of cell proliferation (Lerch-Gaggl et al. 2007). Map 1B phosphorylation is involved in modulating its

binding to microfilaments and MT dynamics. MAP1B is phosphorylated in two phases: in phase 1, it is

phosphorlyated by GSK3, PKD and CDK5 and in phase 2 by CKII. Phase 1 phosphorlyation is

development-related and is reduced in adulthood, whereas mode 2 phosphorylation can occur

throughout life time.

After β-ionone stimulation, numerous phosphorylation sites have been identified to be considerably

downreguated. However, the corresponding non-phosphorylated peptides, of MAP 1B appeared to

be regulated with approximately the same factor, indicating that the actual phosphorylation level of

the respcective peptides have not been downregulated, but the abundance of the entire protein in

the cytoplasmic fraction.

Microtubule-associated protein 2 (MAP 2)

The microtubule-associated protein 2 is another member of the MAP family. MAP 2 initiates tubulin

bundling in neuronal and non-neuronal cells (Kalcheva et al. 1998). Differential expression of MAP 2

has been described in several cancer types. It was shown to stabilize MTs, leading to cell cycle arrest

and growth inhibition. Therefore, induction of MAP 2 expression in metastatic cancer is thought to

be a promising approach for treatment of such cancers (Bhat and Setaluri 2007). Furthermore, MAP 2

binds to myosine VIIA, a motor protein involved in the transport of cargo along cytoskeletal

structures. This suggests an additional role for MAP 2 in intracellular transport (Todorov et al. 2001).

Discussion

121

Apart from its functions in cytosceletal stability and cargo transport, it has also been shown to act as

MT anchor for regulatory proteins such as PKA to microtubuli (Leterrier et al. 2009).

MAP 2 is highly phoshorylated (up to 46 times; (Sanchez et al. 2000)) and its phosphorlyation is

developmentally regulated. Among others, MAP 2 phosphorylation regulates its binding to

cytoskeletal components. The phosphorylation level of MAP2 is regulated by a variety of different

kinases such as MAPK, CDC2 (Goldenring et al. 1985), CaMKII, PDKs, GSK3 and PKA (Sanchez et al.

2000). The cAMP-dependent phosphorylation of MAP 2 by PKA reduces the ability of MAP 2 to form

MT networks (Leterrier et al. 2009).

In this work, seven phosphorylation sites were identified in MAP 2 and four of these could be

mapped to distinct amino acid residues. Following β-ionone treatment, the average phosphorylation

level of MAP 2 appeared to be up-regulated approximately 3-fold after 2 min. However, as already

shown for the protein MAP 1B, the elevated level of phosphorylation rather reflected increased

abundance of MAP 2 in the cytoplasmic fraction, since both phosphorylated and non-phosphorylated

peptides generally showed similar regulation factors. The peptide AGKSGTSTPTTPGSTAITPGTPPSYSSR

was identified to be phosphorylated at three amino acid residues. This peptide was significantly

down-regulated after 10 min of β-ionone treatment, especially when considered in relation to the

overall protein abundance. However, the MS/MS data did not allow for assigning these

phosphorylations to a specific amino acid residue. This may be due to the fact that seven

phosphorylation sites are predicted in this region (http://www.uniprot.org/uniprot/P11137),

potentially resulting in coeluting isobaric phospho-isoforms and “mixed” MS/MS spectra that are

difficult to interprete.

Microtubule-associated protein tau (MAPT)

Of all MAPs, MAPT is the most extensively studied protein. It was described as a MT binding

phosphoprotein in 1984 (Lindwall and Cole 1984) and is found predominantly in neurons.

Hyperphosphorylated tau is associated with neurodegenerative diseases such as Alzheimer

(reviewed in Buee et al. 2000). The phosphorylation at the identified sites in tau in this work (T548,

S552 both unaltered; S713, S717, S721 up-regulated) is mediated by GS3K and have also been found

paired helical filaments of Alzheimer patients (Avila 2009). However, in hyperphosphorylated tau up

to 40 sites were reported with high stoechiometry (Avila 2009) and therefore, if tau was

hyperphophosphorylated in LNCaP cells, more phosphopeptide should have been observed (Cripps

et al. 2006). Hyperphosphorylation disrupts the binding of tau to MT, but tau in its physiological

condition is phosphorlyated as well and its phosphorlyation level is developmentally regulated

(Johnson and Stoothoff 2004). There is evidence that in vivo phosphorylation in neuronal cells is

regulated by GSK3β, a kinase also binding to MTs, Cdk5, PKA and MARK. In addition, other

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overexpressed kinases are also able to phosphorylate tau in non-neuronal cell models (Johnson and

Stoothoff 2004). Physiological phosphorylation of the KXGS motifs in the MT-binding repeats of tau

decreases its binding to MTs. None of the sites found to be regulated by β-ionone treatment,

however, are located in those repeats.

In neurons tau and MAP 1B are essential for normal migration, axonogenesis and axonal transport. It

has been reproted, that modulation of tau phosphorylation is required for these processes (Jamal et

al. 2009). However, none of the phosphorylation sites known to be involved in these processes have

been indentified with significant regulation factors in LNCaP cells treated with β-ionone.

In neuronal and ovarian cells, tau phoshorylation was shown to be cell cycle-dependent and

predominatly located at SP and TP motifs (Illenberger et al. 1998). Four of the five phosphorylation

sites detected in the β-ionone experiment are as well serine and threonine residues immediately

followed by a proline residue. To the best of my knowledge, the phosphorylation sites found for tau

in the β-ionone treatment experiments of LNCaP cells have in fact also been identified in large scale

phosphoproteomics studies (Beausoleil et al. 2004; DeGiorgis et al. 2005; Molina et al. 2007; Cantin

et al. 2008; Gauci et al. 2009; Mayya et al. 2009) but have not been functionally characterized yet.

Microtubule-associated protein 4 (MAP 4)

MAP 4 is another protein of the MAP family. The rate of MT binding of MAP 4 can be regulated by

binding of other proteins like septins to MAP 4 (Kremer et al. 2005). Different MT-dependent

processes are regulated by MAP 4. On the one hand, MT-bound MAP 4 was shown to slow down

GPCR recycling and reapearence of GPCRs at the cell surface after internalization (Cheng et al. 2002).

2002). On the other hand, MAP 4 was implicated in cell cycle progression. It binds to cyclin B, thereby

targeting CDC2 to MTs and regulating MT dynamics (Ookata et al. 1995).

This process is mediated by the MAP 4 phosphorylation level which is regulated during cell cycle with

increased phosphorylation at the G2/M transition (Vandre et al. 1991). Phosphorylation at S696 and

S787 is mediated by cyclin B/cdc2 and was shown to lead to cell arrest in G2 phase or slow down

G2/M progression (Chang et al. 2001). Of the seven phosphorylation sites detected for MAP 4 in

LNCaP cells, one was found to be downregulated (Table 17). This phosphorylation site, again, is not

functionally characterized.

Stathmin (STMN1)

Stathmin, also referred to as oncoprotein 18, is an important protein in mitosis and was reported to

have tubulin-depolymerizing activity and, therefore, opposing functions to MAP 4 (Holmfeldt et al.

2007). Stathmin was shown to be critically involved in proliferation and cell cycle progression. It is

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upregulated in proliferating cells like cancer cells or cells induced to proliferate. Overexpression or

inhibition of stathmin both results in growth suppression and accumulation of cells in G2/M phase

(Rubin and Atweh 2004).

By phosphorlyation of stathmin at the onset of mitosis, it is “switched off” enabling the assembly of

the mitotic spindle. In order to exit mitosis, stathmin has to be dephosphorylated again (Rubin and

Atweh 2004). In the β-ionone study, stathmin has been found to be phosphorylated at S16, S25 and

S38, but only phosphorylation of S16 was differentially regulated by β-ionone treatment. This

phosphorylation site was first dramatically downregulated nearly four-fold after 2 min and slightly

upregulated after 10 min compared to control cells. Phosphorylation at the other two sites was

slightly but not significantly higher than in control cells. These three phosphorylation sites are known

to be involved in the regulation of cell cycle progression (Brattsand et al. 1993). Single

phosphorylation of S16 or S63 were shown to decrease or abolish the MT-destabilizing activity of

stathmin. In contrast, double phosphorylation at S25 and S38 did not influence MT-binding capabilities

(Manna et al. 2009). Therefore, it is tempting to speculate that downregulation of S16

phosphorylation leads to increased stathmin activity shortly after starting β-ionone treatment start,

which is again decreased after 10 min by reestablished and slightly increase S16 phosphorylation.

Cytoplasmic linker-associated protein 2 (CLASP2)

MT not only function as mechanic stabilizers, scaffolding matrix for proteins or transport matrix for

proteins and chromosomes, entire organelles are organized and transported along MTs. Mediated by

the MT plus-end-binding CLASP proteins (Cytoplasmic linker-associated proteins), the Golgi network

is organized through MT structures and the polarity of post-Golgi vesicular trafficking is enabled. This

is especially important in migrating cells (Liu et al. 2007; Miller et al. 2009). Phosphorylation by

GSK3β at serine residues 533 and 537 was shown to destabilize the binding of CLASP2 to MT and to

the protein IQGAP1, which in turn is accociated with actin. Downregulation of CLASP2 inhibited

serum-stimulated migration of COS7 cells in a Boyden chamber assay. The authors concluded that

phosphorylation of CLASP2 at S533 and S537 hinders migration by disrupting MT and actin interaction

via CLASP2 and IQGAP1 (Kumar et al. 2009a; Watanabe et al. 2009).

For CLASP2, S604 and S129 could be identified as novel phosphorylation sites of CLASP2 in LNCaP cells.

While the phosphorylation level of S604 remained unaltered, S129 was found to be downregulated

upon β-ionone treatment. These phosphorylation sites, however, are far away from the functionally

described phosphorylation sites S533 and S537, their relevance remains elusive.

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CAP-GLY domain containing linker protein 1 (CLIP1)

The CAP-GLY domain-containing linker protein 1 (CLIP1, CLIP-170 or Restin) has been reported to pull

centrosomes together in association with dynein and LIS1, and thereby antagonizing EG5, which pulls

centrosomes in opposite directions (Tanenbaum et al. 2008). CLIP1 is furthermore reported to be

localized at tips of MTs, where it also interacts with IQGAP1. The interaction of CLIP1 with IQGAP1

results in the dissociation of CLIP1 from MT (Fukata et al. 2002). The downregulation of CLIP1 in

cancer cells resulted in growth inhibition (Ho et al. 2003). The phosphorylation sites at S200 and S204

were found to be slightly upregulated after 2 min of β-ionone treatment. After 10 min, the previous

basal phosphorylation level was restored. The functional relevance of these phosphorylations,

however, is not know to date.

Gamma Adducin (ADD3)

ADD3 is a membrane-skeletal protein, which caps the fast growing ends of actin filaments and

recruits spectrin to the fast growing actin ends. It is phosphorylated by PKC and Rho-associated

kinases suggesting a role for ADD3 in calcium and Rho-dependent cell motility (Matsuoka et al.

2000).

Phosphorylation of ADD3 at S660 by PKC was shown to be increased during cancer cell development.

This phosphorylation results in a loss of differential targeting of ADD3, which is usually located to the

basal membrane, leading to localization to the lateral membrane (Fowler et al. 1998). However, this

phosphorylation site cannot be assigned to a site in the sequence of ADD3 in the latest version of the

UniProt database, as no sequence stretch is given in the paper (Fowler et al. 1998) and residue 660 in

ADD3 isoform 1 is a proline, whereas it is an isoleucine in isoform 2 (http://www.uniprot.org/

uniprot/Q9UEY8).

Following β-ionone treatment, a doubly phosphorylated peptide (663-684 in isoform 2 of ADD3) was

found to be downregulated in LNCaP cells. MS/MS spectra of this peptide contain ambigous

information regarding the exact localization of the phosphorylation sites. In some spectra S679 and

S681 are clearly identfied as being phosphorylated. In other spectra S683 and another not amino acid

seems to be phosphorylated. According to the UniProt database, predictions of phosphorylation at

S683 is labeled as predicted to be a PKC phosphorylation site. This may be the phosphorlyation site

reported previously with increased levels of phosphorylation during cancer development.

Echinoderm microtubule-associated protein-like 3 (EML3)

Only little information is available about the function of echinoderm microtubule-associated protein-

like 3 (EML3). It has been described to be a nuclear MT-binding protein, which localizes to the spindle

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125

and is required for the correct alignment of chromosomes during metaphase (Tegha-Dunghu et al.

2008). The phosphoserine S176, which was found to be regulated following β-ionone stimulation, has

been reported previously in large-scale phosphoproteomics experiments (Beausoleil et al. 2006;

Dephoure et al. 2008; Mayya et al. 2009) but has not been functionally characterized yet.

Formin-binding protein 1-like (FNBP1L)

Formin-binding protein 1-like (FNBP1L or Toca1) forms a complex with the N-WASP protein. This

complex is an essential cofactor for Cdc42-mediated actin remodeling. Both proteins are necessary

for filopodia formation and endocytosis (Bu et al. 2009). Within the complex, FNBP1L is required for

WASP activation via Cdc42 (Ho et al. 2004).

FNBP1L is phosphorylated at several serine and threonine residues (Dephoure et al. 2008; Gauci et al.

2009; Mayya et al. 2009). Amongst these, S488, which was found to be slightly upregulated by β-

ionone treatment in this work, has been reported, but its functional relevance has not been

investigated (Dephoure et al. 2008).

Sperm-specific antigen 2 (SSFA2)

The sperm-specific antigen 2 (SSFA2, CS1 or KRAP) is a cell surface glycoprotein, highly expressed in

myeloma. It has been suggested to mediate homophilic interaction, cell adhesion, and tumorigenity

in myeloma via c-maf transactivation (Tai et al. 2010). In addition, high expression of SSFA2 was

observed in colon cancer cells and was found here to be associated to actin filaments potentially

mediating signals from outside the cell directly to the cytoskeleton (Inokuchi et al. 2004).

In SSFA2, one phosphorylation site was found to be downregulated upon β-ionone treatment,

namely S737. This site has also been described in large-scale studies performed by Dephoure et al.

(2008) and Oppermann et al. (2009) but lacks functional characterization.

Neuroblast differentiation-associated protein AHNAK

The neuroblast differentiation-associated protein AHNAK (also referred to as Desmoyokin) is a giant

phosphoprotein and signaling molecule of 629 kDa, which binds and bundles actin and provides a

scaffold for other enzymes such as PKC and PLCγ. It activates PKC, which in turn leads to c-Raf, MEK

and Erk phosphorylation (Lee, I. H. et al. 2008). It also binds to Ca2+ L-type channel subunit β2 and

regulates Ca2+ entry in the cell. AHNAK inhibits subunit β2 by sequestration. This inhibitory effect is

released by PKA phosphorylation of T5236 (Haase 2007). Phosphorylation of S5335 leads to membrane

targeting of AHNAK.

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Although there are 54 phosphorylation sites known for AHNAK to date, the two described above are

the only ones which have thoroughly been characterized. The AHNAK phosphopeptides identified in

this work were all slightly downregulated. Two of the identified phosphorylation sites have not been

described before, namely S212 and S5746. In total, six phosphorylation sites in five different peptides

have been identified in this work. Interestingly, only the doubly phosphorylated peptide 376

(MaxQuant ID) was significantly downregulated after 10 min, whereas its singly phosphorylated

counterpart was slightly but not significantly upregulated (1.24-fold).

Shroom2

The protein formerly known as APXL (apical protein Xenophobus-like) belongs to the Shroom protein

family (Hagens et al. 2006). Unlike Shroom3, which is localized to the apical junctional complex (AJC)

of epithelial cells, Shroom2 is found in association with cortical actin where it colocalizes with non-

muscle myosin II and functions in the regulation of dynamic actin organization (Dietz et al. 2006). In

addition, it binds to myosin VIIa, F-actin and to ZO-1 at mature tight junctions and may have a role in

linking the junctional membrane to the cytoskeleton, thereby stabilizing the junctions (Etournay et al.

2007).

Of the six phosphorylation sites found after β-ionone treatment (S921, S922, S413, S425, S1171, S1173) none

has been identified before. The peptide containing the serine residues 413 and 425 was significantly

downregulated after stimulation. Neither these sites, nor the other sites identified, lie within a

known protein-protein interaction region. Therefore no conclusions about the potential function of

these sites can be drawn.

8.3.2.9 Functional category: Nuclear factors, DNA repair and cell cycle progression

A distinct feature of cancer cells is their uncontrolled proliferation. In order to grow and devide, cells

need to progress through the cell cycle. The cell cycle is a highly ordered and tightly controlled

process beginning with DNA replication in S phase followed by G2 phase, in which the cell synthesizes

proteins and other biomolecules and reassembles the cytoskleton in preparation for cell division in

mitosis. In mitosis (M phase), daughter chromosomes are segregated followed by another G phase

(G1) of preparation for another S phase. Along the cell cycle, multiple checkpoints are implemented,

where the cell cycle can be stopped or delayed to allow for response to DNA damage, incorrect

replication or segregation as well as to environmental cues such as growth factors or nutrient

disposability. Defects in proteins, which are involved in the complex signaling cascades controling

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these check points, can lead to uncontrolled cell cycle progression and cancer (for review see:

Molecular biology of the cell, chapter 17, fourth edition; Singhal et al. 2005).

13 proteins, which were identified in this work with β-ionone-induced changes in phosphorylation

level were sorted in the category “Nuclear factors, DNA repair and cell cycle progression”. These

proteins are listed in Table 18.

Table 18: Proteins grouped into the functional category “Nuclear factors, DNA repair, cell cycle progression”.

Gene name, protein name, additional protein informaion and the ID of the regulated phosphopeptide are given

as well as the respective regulation factors. For further details, please refer to Supplementary Table SP 23.

Nuclear factors, DNA repair, cell cycle progression

gene name




RRM2 Ribonucleoside-

diphosphate reductase subunit M2

cell-cycle gene;pS20 relieves inhibitory effect on Wnt signaling; role in tumor growth and metastasis/angiogenesis, overexpressed in

prostate cancer 193 1.72 1.83

LAP2 Lamina-associated

polypeptide 2 upregulated in cancer, involved in nuclear envelop organization

and cell cycle control, phosphorylation by PKA

1524 1.54 1.57

490 1.57 1.78

XPC DNA repair protein

complementing XP-C cells

nucleotide excision pathway, polymorphisms rise risk of cancer 606 -1.63 -1.04

ARID4B AT-rich interactive domain-containing

protein 4B

subunit of histone deacetylated-dependent SIN3A transcriptional corepressor complex; functions in proliferation; oncogenesis,

955, 1651 -1.12 -1.75

HUWE1 E3 ubiquitin-protein

ligase HUWE1 involved in cancer development, ubiquinates histones, Mcl1 and

p53, synchronization of differentiation, proliferation 683 1.57 1.43

NFATC2IP

nuclear factor of activated T-cells,

cytoplasmic, calcineurin-dependent 2 interacting

protein

calcineurin-dependent, belongs to family of transcription factors; NFAT-complex

663 -1.65 -1.77

TPR Nucleoprotein TPR interacts with components of nuclear core complex, invovled in

mitotic spindle checkpoint signaling 1258 1.16 1.78

MDC1 Nuclear factor with BRCT

domains 1 required to activate S phase and G2/M phase checkpoints; recruits

proteins to DNA damage sites 1698 1.60 1.57

PPP1R10 Serine/threonine-protein phosphatase 1 regulatory

subunit 10

transcriptional regulator; down regulation induces apoptosis in cancer cells

894 -1.60 -1.06

RNF20 E3 ubiquitin-protein

ligase BRE1A ubiquitinates H2B, has tumor supressor activity 825 -4.03 -1.76

CUX2 Homeobox protein cut-

like 2 regulates cell cycle length, transcription factor, proliferation 397 -13.71 10.94

RANBP2 E3 SUMO-protein ligase

RanBP2 complex with RanGAP1 recruited to kinetochores; invovled in

tumorigenesis 811 -1.74 -1.83

SMARCAD1 ATP-dependent helicase

1 actin dependent regulator of chromatin; helicase; ATP dependent

chromatin remodeling 122 -1.12 -1.82

Ribonucleoside-diphosphate reductase subunit M2 (RRM2)

Ribonucleoside-diphosphate reductase subunit M2 (RRM2) is a component of the ribonucleotide

reductase (RNR) enzyme complex. RNR consists of two RRM1 large subunits and two RRM2 small

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128

subunits and catalyzes the reduction of ribonucleoside diphosphates to deoxyribonucleoside

diphosphates (dNTPs) for DNA synthesis. A deregulation of the dNTP pool leads to cell death or

genetic abnormalities (Nordlund and Reichard 2006). While the expression level of RRM1 remains

relatively constant throughout the cell cycle, RRM2 is expressed during late G1 and early S phase and

downregulated in late S phase, thereby regulating the activity of the RNR complex (Engstrom et al.

1985). Upregulation of RRM2 is observed in many cancer types and contributes to cancer progression

(Furuta et al. 2010). Besides its effects on DNA synthesis and repair, RRM2 overexpression reduces

expression of TSP-1, which is an anti-angiogenic factor. It simultaneously induces expression of the

pro-angiogenic factor VEGF leading to a tumor type with higher angiogenic potential (Zhang et al.

2009).

RRM2 was shown to function downstream of beta-catenin as an inhibitor of Wnt signaling.

Phosphorylation at S20 of RRM2 counteracted its inhibitory effect (Tang et al. 2007). Whether the

phosphorylation of S80, which was found to be elevated after β-ionone treatment, is of significant

importance for the function of RRM2, remains to be elucidated.

Lamina-associated polypeptide 2 (LAP2)

Lamina-associated polypeptide 2 (formerly known as thymopoietin (TMPO)) is expressed in mammals

in six different splice variants. While LAP2β is an integral protein of the inner nuclear membrane,

where it binds to lamin B and chromatin, LAP2α is a non-membrane protein associated with the

nucleoskeleton. LAP2 proteins are involved in the disassembly and reassembly of the nucleus during

cell cycle (Dechat et al. 2000).

Phosphorylation of LAP2 leads to disruption of its binding to lamin and chromosomes (Foisner and

Gerace 1993; Dechat et al. 1998). In addition, phosphorylation influences the binding of LAP2 to

other interaction partners. The LAP2-HA95 complex, for example, which is implicated in DNA

replication initiation, dissociates via cAMP/PKA signaling mediated phosphorylation (Martins et al.

2003). The actual sites of phosphorylation, which are responsible for these effects, have not been

identified.

For LAP2, nine phosphorylation sites were identified in the β-ionone study, three of which are newly

identified, namely S158, S180 and Y183. Two peptides were found to be upregulated upon treatment.

One containing two phosphorylation sites, of which only one could be confidently localized (S156), was

upregulated 1.7 fold. Interestingly, the other regulated phosphopeptide was present in two isoforms.

The peptide was not regulated when S66 and S67 were phosphorylated. If this peptide was

phosphorylated at T74, however, it was upregulated 1.5 fold. These observations suggest highly

specific functions of the respective phosphorylation sites.

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DNA repair protein complementing XP-C cells (XPC)

DNA repair protein complementing XP-C cells (XPC) is a DNA damage recognition factor that is

required for global genomic nucleotide excision repair. It detects disrupted DNA base pairs in a

complex with RAD23B by directly binding to the damaged sites (Sengupta and Harris 2005). The gene

encoding the XPC protein belongs to the genes frequently mutated in xeroderma pigmentosum, an

autosomal recessive genetic disorder, in which the DNA excision repair is impaired. Patients develop

skin cancer and lesions at early age and later on, if still alive, internal malignancies (exemplary case

study described by Halpern et al. (Halpern et al. 2008)). Functional XPC however, leads to enhanced

cell survival in the bone marrow of mice upon DNA damage by carboplatin (Fischer et al. 2009).

After β-ionone stimulation a phosphopeptide (amino acid sequence 869-898) was identified

containing two phosphorylation sites, which could not be confidently assigned to a specific residue.

This phosphopeptide was slighty downregulated after 2 min of treatment. Four phospho-serine

modifications have been described for this stretch in the amino acid sequence in other large-scale

studies (Beausoleil et al. 2004; Giorgianni et al. 2007; Matsuoka et al. 2007; Han et al. 2008; Mayya et

al. 2009). However, the functions of the phosphorylation at these sites still remain unknown.

AT-rich interactive domain-containing protein 4B (ARID4B)

AT-rich interactive domain-containing protein 4B (ARID4B, BRCAA1, RBBP1L1, RBP1L1 or SAP180)

was found to be a molecular marker associated with many cancers (Cao et al. 2001). It is a member

of the AT-rich interaction domain gene family (Wilsker et al. 2005). It acts as transcriptional repressor

via its role in the assembly and/or enzymatic activity of the Sin3A corepressor complex (Fleischer et

al. 2003). Mice with defects in ARID4A and B as well as an additional myelodysplastic

/myeloproliferative disorder develop multiple malignancies, suggesting a role of ARID4 A and B as

tumor suppressor.

For this protein, too, many phosphorylation sites have been identified in large-scale

phosphoproteomics studies without characterizing their function. Phosphorylation at S790 and T793,

which are known phosphorylation sites of ARID4B (Dephoure et al. 2009; Gauci et al. 2009; Mayya et

al. 2009), were slightly downregulated after β-ionone stimulation of LNCaP cells.

E3 ubiquitin-protein ligase HUWE1 (HUWE1)

E3 ubiquitin-protein ligase HUWE1 (also referred to as UREB1) is a ubiquitin ligase of the HECT family,

which was shown to be involved in the ubiquitination of histones (Liu et al. 2005). In general, the

function of HUWE1 is controversially discussed. It was reported to be involved in ubiquitin-mediated

degradation of the anti-apoptotic protein Mcl1 and the pro-apoptotic protein p53, resulting in

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130

completely different phenotypes in the same cell line (U2OS cells). In some studies, it was reported

to support gene activation by c-Myc, whereas in others, it was reported to target c-Myc for

degradation by ubiquitination. In spite of this controversy, HUWE1 is of relevance in the context of

cancer, as it was reported to be upregulated in many different cancer types (Bernassola et al. 2008).

In neuronal cells, HUWE1 was reported to destabilize N-Myc, thereby inhibiting proliferation and

promoting differentiation (Zhao et al. 2008; Zhao et al. 2009). In addition, HUWE1 was reported to be

involved in the synchronization of neuron and glia development (D'Arca et al. 2010).

Following β-ionone treatment, the phosphorylation at S3555 was found to be upregulated, whereas

phosphorylation at S1907 remained unaffected. Interestingly, S1907 has already been reported to be

phosphorylated in large-scale studies, while the regulated site at S3555 is hitherto unknown.

Accordingly, no functional information is available of this site as well as of any other known site for

this protein.

Nuclear factor of activated T-cell, cytoplasmic, calcineurin-dependent 2 interacting

protein (NFATC2IP)

The nuclear factor of activated T-cells (NFAT), cytoplasmic, calcineurin-dependent 2 interacting

protein (NFATC2IP or Nip45) is a transcription cofactor, which recruits the PRMT1 protein (Mowen et

al. 2004) to the NFAT complex. NFATC2IP is methylated by PRMT1, which potentially leads to

enhanced activity of the NFAT complex. It contains a ubiquitin- like domain and was shown to bind to

a sumoylation enzyme (Ubc9) instead of SUMO in mice, thereby preventing further sumoylation

(Sekiyama et al. 2010).

In the β-ionone study, S198 and S204 were identified as being phosphorylated. Two different phospho-

isoforms of one peptides were detected, a monophosphorylated peptide (S204) and one doubly

phosphorylated peptide (S198 and S204). Interestingly, only the doubly phosphorylated peptide was

found to be downregulated, whereas the monophosphorylated peptide remained constant. Both

sites have been identified previously (Olsen et al. 2006; Dephoure et al. 2008; Mayya et al. 2009), but

again no functional information are available for the identified phosphorylation sites.

Nucleoprotein TPR (TPR)

Nucleoprotein (TPR) binds to the Mad1/Mad2 complex, which is responsible for the cell cycle arrest

upon perturbation of mitotic spindle essembly. Interaction of TPR with the Mad - complex seems to

be important for controling of mitotic spindle checkpoint (Lee, S. H. et al. 2008). The Drosophila

orthologue of TPR was shown to co-localize with spindle matrix proteins and to be involved in the

nucleation of the spindle complex by providing the structural basis for spindle matrix formation (Qi et

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131

al. 2004). After mitosis, TPR is localized at the cytoplasmic surface of the reassembled nuclear pore

complex (Byrd et al. 1994).

The phosphoserine residue (S2155), which was found to be upregulated after 10 min of β-ionone

treatment, had been identified previously in large-scale studies (Olsen et al. 2006; Molina et al. 2007;

Yu et al. 2007; Cantin et al. 2008; Dephoure et al. 2008; Mayya et al. 2009). Functional data are not

yet available.

Nuclear factor with BRCT domains (MDC1)

Nuclear factor with BRCT domains (MDC1 or NFBD1) is a protein involved in DNA damage response

(DDR). Upon DNA damage, the histone H2AX is phosphorylated. MDC1 binds to this phosphorylated

H2AX and induces the amplification of the DNA damage signal. MDC1 serves as scaffold for recruiting

different DDR complexes, including the breast cancer-associated suppressor BRA1, to sites of double

strand breaks to mediate DNA repair (Yan and Jetten 2008). MDC1 also mediates intra-S phase

checkpoint control via its interaction with NBS1 (Wu et al. 2008).

MDC1 is phosphorylated upon DNA damage and during mitosis. Downregulation of MDC1 leads to

defects in cell cylce checkpoint function and eventually apoptosis (Stucki and Jackson 2004; Stucki

and Jackson 2006). Aprataxin is another protein, to which MDC1 binds in a phosphorylation-

dependent manner. The interaction is mediated by SDTD-motifs doubly phosphorylated by CKII

(Becherel et al. 2010). A phosphopeptide with such a doubly phosphorylated motif has been found to

be slightly upregulated after 2 min of β-ionone exposure (regulation factor 1.52). However, with a

regulation factor of 1.52, it was just below the cut-off at 1.54. Another phosphopeptide of MDC1 was

found to be significantly upregulated. This peptide (amino acid residues 455-467) contains two

phosphorylation sites, which could not be confidently assigned to a distinct site. For this peptide,

three phosphorylation sites have been reported previously in large scale studies (Dephoure et al.

2008; Mayya et al. 2009), but no functional properties are known.

Serine/threonine-protein phosphatase 1 regulatory subunit 10 (PPP1R10)

The serine/threonine-protein phosphatase 1 regulatory subunit 10 (PPP1R10, synonymes: CAT53,

FB19, PNUTS) is an inhibitor of the phosphatases PPP1CA and PPP1CC (Kreivi et al. 1997). When

bound to PPP1R10, PPP1CA is targeted to the nucleus (Grana 2008). Inhibition of PPP1CA by binding

to PPP1R10 is reduced by PKA-mediated phosphorylation of PPP1R10 in the PPP1CA binding domain

(Kim et al. 2003). Knock-down of PPP1R10 leads to apoptosis (De Leon et al. 2008), and association of

PPP1R10 with, for example, LCP1 is potentially involved in transcriptional regulation which was

determined in a heterologous transcription system (Lee et al. 2009).

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The phosphorylation at S313 was downregulated after 2 min of β-ionone treatment and restored to

base level after 10 min. This residue lies N-terminal to the domains which are involved in PPP1CA and

PPP1CC binding and inhibition and was identified in large-scale studies before (Olsen et al. 2006; Yu

et al. 2007; Dephoure et al. 2008). The functional relevance of this site has not been characterized

yet.

E3 ubiquitin-protein ligase BRE1A (RNF20)

E3 ubiquitin-protein ligase BRE1A (RNF20) is a histone 2B specific ubiquitin ligase that mediates

monoubiquitination of Lys120 of histone 2B (Kim et al. 2005), thereby regulating the expression rates

of certain growth-related genes like the p53 tumor suppressor protein and acting as tumor

suppressor (Shema et al. 2008).

RNF20 was found to be phosphorylated at S136 and S138 in two different monophosphorylated

peptides, which have been identified previously (Dephoure et al. 2008, Gauci et al. 2009). Only S136

phosphorylation was downregulated upon β-ionone treatment.

Homeobox protein cut-like 2 (CUX2)

Homeobox protein cut-like 2 (CUX2 or CUTL2) is a transcription factor, which regulates cell cycle

progression and development of neuronal progenitor cells (Iulianella et al. 2008). The transcription of

CUX2 has been shown to be regulated down-stream of Notch signaling in the formation of

interneurons during spinal cord formation (Iulianella et al. 2009). CUX2 is also implicated in a variety

of morphogenic changes in the brain such as dendritic branching (Cubelos et al. 2010). Only little is

known about CUX2 function in non-neuronal cell. It is higher expressed in the liver of female rats

than in the liver of male rats, where it is involved in the suppression of male specific genes (Laz et al.

2007).

The phosphorylation site identified in CUX2 upon β-ionone treatment (S90) has been indentified for

the first time in this work. It showed dramatic down-regulation after 2 min but was up-regulated

after 10 min of β-ionone exposure. is dramatically downregulated after 2 min and likewise

dramatically upreguated after 10 min.

E3 SUMO-protein ligase RanBP2 (RanBP2)

E3 SUMO-protein ligase RanBP2 is a nuclear pore protein (Reverter and Lima 2005). The

RanGAP1/RanBP2 complex, which resides at the cytosolic surface of nuclear pore complexes in the

interphase, was found to localize to kinetochores during mitosis and has been shown to be involved

in connecting kinetochores to spindle poles (Arnaoutov and Dasso 2005). During mitosis, RanBP2

Discussion

133

binds and sumoylates topoisomerase IIα and contributes to the correct resolution of sister

centromers. Mice with low levels of RanBP2 are prone to chromosome instability and cancerogene-

induced tumorigenesis, whereas knock-out of RanBP2 is lethal (Dawlaty et al. 2008).

In RanBP2, four phosphorlyation sites were identified after β-ionone stimulation, namely S781, S955,

T1396 and S1400. Of these, only S955 was regulated (downregulated after 2 and 10 min). S955 has been

identified before (Dephoure et al. 2008, Mayya et al. 2009), however, functional characterization is

still missing.

SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily A

containing DEAD/H box 1 (SMARCAD1)

SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily A containing

DEAD/H box 1 (SMARCAD1) is a potential ATP-dependent helicase (Adra et al. 2000). The gene coding

for SMARCAD1 lies in a chromosomal region, frequently mutated in head and neck cancer (Cetin et

al. 2008)(Cetin et al. 2008).

Two doubly phosphorylated peptides with four phosphorylation sites were found, namely S211 and

either S212, S213 or S214 and, in another peptide, S124 and S127. Only the peptide containing S212 was

downregulated upon β-ionone treatment. These sites have also been identified in large scale studies

(Olsen et al. 2006; Matsuoka et al. 2007; Dephoure et al. 2008; Gauci et al. 2009; Mayya et al. 2009).

8.3.2.10 Functional category: Protein and Biomolecule synthesis and RNA

processing

During cell cylcle, not only the DNA has to be replicated, but also new proteins and organelles have

to be produced. These processes mainly occur during G1 and G2 phase and require increased

synthesis of proteins as well as other biomolecules such as lipids (Alberts 2002; Singhal et al. 2005).

The molecular processes involved in these synthesis events, influence cell cylce progression and

proliferation just as much as processes involved in DNA processing (Caraglia et al. 2000). In total, 10

proteins involved in protein and biomolecule synthesis as well as in RNA processing were found to be

regulated with respect to their phosphorylation level upon stimulation of LNCaP cells by β-ionone.

Discussion

134

Table 19: 10 proteins are listed in this table, which are involved in protein or biomolecule synthesis and RNA

processing. In addition, some further information are given as well as the ID of regulated phosphopeptides and

their respective regulation factors. For furhter details, please refer to Supplementary Table SP 23.

Protein/Biomolecule Synthesis, RNA processing

gene name


regulation factor 2

min

regulation factor 10

min

EEF2K Elongation factor 2 kinase Control of protein synthesis; is upregulated in cancers;

downregulation lead to stop of protein synthesis and induction of apoptosis

198, 199, 200

-1.17 -5.68

EIF4G2 Eukaryotic translation

initiation factor 4 gamma, 2 Control of protein synthesis; member of eIF4F complex,

essential for translation initiation 1485 -1.04 -1.88

MFAP1 Microfibrillar-associated

protein 1 mutations in gene rise breast cancer risk, involved in pre-mRNA

processing, reduced level lead to slow proliferation 405, 406 -1.16 -1.83

PCYT1A Choline-phosphate

cytidylyltransferase A Plays role in every process which requires membrane

biosynthesis; binds upon calcium elevation to 14-3-3 zeta 1018, 1019,

1021 -3.00 -2.37

LARP1 La ribonucleoprotein domain

family, member 1 RNA binding protein, regulates cell division and migration,

eIF4E interaction 714 -1.22 -1.87

DCP1A mRNA-decapping enzyme 1A

forms complex with DCP2 to decap mRNA prior to decomposition; TGF-beta signaling; hyperphosphorylated

during development,differentiation and cell stress; transcriptional coactivator

623, 1559 -1.65 -1.21

SRRM2 Serine/arginine repetitive

matrix protein suggested to be involved in spliceosomes/splicing factor, RNA

binding 1720 -5.39 -1.06

AGFG1 Nucleoporin-like protein RIP mediate nucleocytoplasmic transport, involved in clathrin

dependent endocytosis, partner of EPS15, Rev Cofactor, RNA export from nucleus to cytoplasm

973, 1653 -1.30 -3.01

CPSF2 Cleavage and polyadenylation

specificity factor subunit 2 involved in mRNA maturation; endocytosis

1135, 1136, 1137

1.01 -1.84

SF3A1 Splicing factor 3 subunit 1 subunit of spliceosome 3a complex, essential for pre-mRNA

splicing 322 1.03 -3.28

Elongation factor 2 kinase (eEF2K)

Elongation factor 2 kinase (eEF2K) is a Ca2+/calmodulin-dependent kinase, which controls the rate of

peptide chain elongation. This effect is mediated by elongation factor 2 (eEF2), which is the specific

substrate of eEF2K and promotes the translocation step of peptide chain elongation. When eEF2 is

phosphorylated by eEF2K, its interaction with the ribosome is disrupted and its activity is impaired

(Hait et al. 2006; Wang and Proud 2006).

eEF2K activity is regulated in a cell cycle-dependent manner by Cdc2 kinase through phoshorylation

at S359. Phosphorylation at this site inhibits EEF2K activity, thereby preventing phosphorylation of

eEF2, which in turn leads to increased and accelerated elongation of polypeptide chains (Figure 39)

(Smith and Proud 2008). Furthermore, phosphorylation of S78 has also been shown to have an

inhibitory effect on EEF2 activity. This phosphorylation site lies within the calmodulin-binding domain

of EEF2K and potentially inhibits the binding of Ca2+-Calmodulin binding to eEF2K (Wang and Proud

2006).

Discussion

135

Following β-ionone stimulation, a phosphopeptide with two phosphorylation sites has been found to

be strongly downregulated after 10 min of treatment. These two phosphorylation sites were

assigned to S72 and S74, which have not been reported to be phosphorylated before. However, the

localization could not be confidently confirmed by the obtained data. Since both sites lie in close

proximity to S78 it is conceivable that they also exhibit an inhibitory effect on Ca2+-Calmodulin binding

to eEF2K. This would activate eEF2K, thereby enhancing the phosphorylation of eEF2, which in turn

would result in the dissociation of eEF2 from the ribosome and decreased mRNA translation.

Fig. 40: Hypothetic model for the regulation of protein translation by eEF2K based on the mechanism

reviewed in Wang and Proud 2006. Decrease in eEF2K phosphorylation within the Ca2+

-Calmodulin (CaM)-

binding domain as determined in this work results in binding of CaM to eEF2K and its subsequent activation (1).

eEF2K phosphorylates its specific substrate eEF2, leading to dissociation of eEF2 from the ribosome (2).

Thereby polypeptide chain elongation is stopped (3).

Eukaryotic translation initiation factor 4 gamma, 2 (eIF4G2)

Eukaryotic translation initiation factor 4 gamma, 2 (eIF4G2; synonymes DAP5, p97, NAT1) is part of

the eIF4F complex, which consists of eIF4E, eIF4A and eIF4G. This complex bridges the cap structure

at the 5´end of mRNAs to ribosomes. There are two homologues of eIF4G in humans, eIF4G1 and

eIF4G2. eIF4G2 is hyperphosphorylated during mitosis. This leads to the disassembly of the eIF4F

complex (Pyronnet et al. 2001). It also binds to the eIF4E-specific kinases MNK1 and 2, serving as a

scaffold to bring kinase and substrate together (Fukunaga and Hunter 1997). Knock-down of eIF4G2

induces M phase-specific caspase-dependent apoptosis. Furthermore, eIF4G2 is involved in Bcl2 and

CDK1 translation during mitosis, potentially by a cap-independent process (Marash et al. 2008).

The phosphopeptide found to be downregulated after 10 min of β-ionone treatment contained one

phosphorylation site. Most likely, T468 was phosphorylated, although the localization score was below

= phosphorylation site

1

2

3

Discussion

136

the score cut off (0.63). No phosphorylation site has been reported in this peptide so far and,

accordingly, no functional information is available.

Microfibrillar-associated protein 1 (MFAP1)

So far, little is known about the Microfibrillar-associated protein 1 (MFAP1). The drosophila

homologue of MFAP1 has been shown to be required for pre-mRNA processing and G2 to M phase

progression. Drosophila cells with reduced levels of Drosophila MFAP proliferate slowly and are

prone to apoptosis (Andersen and Tapon 2008). The human gene encoding MFAP1 is frequently

found to be mutated in breast tumors (Olson et al. 2010).

The phosphorylation sites S132 and S133 were downregulated after 10 min of β-ionone treatment.

These sites have been reported previously in large-scale studies (Beausoleil et al. 2004; Olsen et al.

2006; Dephoure et al. 2008; Mayya et al. 2009) but have not been functionally characterized yet.

Choline-phosphate cytidylyltransferase A (PCYT1A)

Choline-phosphate cytidylyltransferase A (PCYT1A or CTPCT, PCYT1) is involved in the synthesis of the

major membrane component phosphatidylcholine. PCYT1A is differentially expressed (Jackowski and

Fagone 2005) and phosphorylated (Jackowski 1994; Fagone and Jackowski 2009) during cell cycle

progression. An increase in phosphorylation has been shown to be associated with a decrease in

activity during the exit from G0/G1 phase. Active membrane-bound PCYT1A is released from the

membrane and dephosphorylated. However, whether the phosphorylation is the cause for

membrane release or a consequence could not be answered in the study (Fagone and Jackowski

2009). Recently, an interaction of PCYT1A with 14-3-3 zeta was reported, which is required for

nuclear import of PCYT1A for nuclear membrane growth. This calcium mediated interaction occurs

via a domain of PCYT1A with multiple phosphorylatable serines from positions 328-342 in the amino

acid sequence. The phosphopeptide, which was found to be downregulated upon β-ionone

treatment, lays just N-terminal of this position (S315, S319, S323) and might be implicated in nuclear

transport.

La ribonucleoprotein domain family, member 1 (LARP1)

La ribonucleoprotein domain family, member 1 (LARP1) is an RNA-binding protein, which was first

described in drosophila, where it is required for spermatogenesis, embryogenesis and cell cycle

progression (Ichihara et al. 2007; Blagden et al. 2009). Burrows et al. demonstrated in 2010, that, in

HeLa cells, LARP1 exists in complex with EIF4E, where it is required for ordered mitosis, cell survival

and migration (Burrows et al. 2010).

Discussion

137

Many phosphorylation sites have been found for LARP1 in large-scale phosphoproteomics studies.

However, none was functionally characterized yet. In this work, S766 was found to be downregulated

after β-ionone treatment of LNCaP cells.

mRNA-decapping enzyme 1A (DCP1A)

mRNA-decapping enzyme 1A (DCP1A or SMIF) is a member of the human mRNA-decapping complex,

which catalyzes the removal of the 5´mRNA structure prior to mRNA degradation (van Dijk et al.

2002; Fenger-Gron et al. 2005). Blumenthal et al. showed that DCP1A is phosphorylated at S315, S319

and T321 during neuronal development and arsenate-induced cellular stress (Blumenthal et al. 2009).

In addition, DCP1A is a Smad-interacting transcriptional co-activator, which can be activated by TGFβ-

signaling.

The phosphopeptide, which was found to be downregulated after 10 min of β-ionone stimulation,

contained two phosphorylation sites. It was fragmented multiple times and in the different spectra

three confirmed phosphorylation sites were identified (S523, S525, T531) suggesting different

phosphoisoforms of this doubly phosphorylated peptide. These phosphorylation sites, too, have

been reported previously in large-scale studies without functional characterization (Beausoleil et al.

2004; Cantin et al. 2008; Dephoure et al. 2008; Imami et al. 2008; Gauci et al. 2009; Mayya et al.

2009).

Serine/arginine repetitive matrix protein (SRRM2) Serine/arginine repetitive matrix protein (SRRM2) is a 300 kDa protein, which is a non-essential part

of the active spliceosome (Blencowe et al. 2000) with a potential contribution to the catalytic center

of the spliceosome (Grainger et al. 2009). Interestingly, SRRM2 depletion in ErbB2-overexpressing

ovarian cancer cells reduces the migration rate.

SRRM2 is a highly phosphorylated protein and a large number of phosphorylation sites have been

identified in large-scale phosphoproteomics studies. None of these sites have been functionally

characterized yet. Results of the β-ionone study also revealed the occurrence of many

phosphorylation sites, but only one of them, S288, was found to be regulated. It was down-regulated

approximately 5.4-fold after 2 min of β-ionone exposure.

Arf-GAP domain and FG repeats-containing protein 1 (AGFG1)

Arf-GAP domain and FG repeats-containing protein 1 (AGFG1 or Nucleoporin-like protein RIP) binds

in the nucleus to the Rev RNA export factor and significantly enhances its activity when

Discussion

138

overexpressed (Bogerd et al. 1995). Cytoplasmic AGFG1 associates with Eps15 and VAMP7 and is

involved in clathrin-dependent endocytosis (Doria et al. 1999; Chaineau et al. 2008).

After 10 min of β-ionone stimulation, the phosphorylation at T177 and S179 was down-regulated

threefold. The phosphorylation at T177 has been reported previously (Olsen et al. 2006; Dephoure et

al. 2008; Zahedi et al. 2008; Mayya et al. 2009), but the S179 phosphorylation was hitherto unknown.

For these sites, too, no functional information are available.

Cleavage and polyadenylation specificity factor subunit 2 (CPSF2)

Cleavage and polyadenylation specificity factor subunit 2 (CPSF2 or CPSF100), together with CPSF73,

forms the cleavage and polyadenylation specificity factor (CPSF), required for 3´cleavage of RNA

transcripts and its subsequent polyadenylation (Jenny et al. 1994; Kolev et al. 2008).

A phosphopeptide of CPSF2 was found to be two-fold downregulated after 10 min of β-ionone

treatment. This peptide was phosphorylated at serine residues 419, 420 and 423. These sites have

also been found in large-scale studies (Olsen et al. 2006; Dephoure et al. 2008; Gauci et al. 2009;

Mayya et al. 2009), but were not functionally characterized up to now.

Splicing factor 3 subunit 1 (SF3A1)

Splicing factor 3 subunit 1 (SF3A1) is one of three components of the splicing factor 3A. This complex

is essential for functional 17S U2 snRNP and pre-spliceosome assembly (Das et al. 2000; Tanackovic

and Kramer 2005).

Phosphorylation of S329 was observed to be downregulated after 10 min of β-ionone treatment. This

phosphorylation site has already been described in a number of phosphoproteomics studies (Olsen

et al. 2006; Molina et al. 2007; Dephoure et al. 2008; Han et al. 2008; Gauci et al. 2009; Mayya et al.

2009). As for many other phosphorylation sites detected in this work, functional information are not

available.

Discussion

139

8.3.2.11 Functional category: Others

Remaining proteins, which could not be classified in one of the first five categories because they

fulfill other cellular functions than the proteins described in categories a) - e) or for which no

literature information was available, were grouped into category f) “Others” (Table 20).

Table 20: List of proteins, which exhibited phosphopeptides significantly regulated upon β-ionone treatment

and could not be classified to one of the first five categories. For each peptide, the dedicated protein is listed

in the table as well as the MaxQaunt regulation factors and the MaxQaunt phosphopeptide ID. For further

details, please refer to Supplementary Table SP 23 (N/A, no literature available).

others

gene name protein name protein information phospho-peptide ID



IPI00328306 Zinc finger CCCH domain-containing

protein 11A N/A 1011 -1.47 1.02

IPI00878236 Fibrous sheath-interacting protein 2 sperm tail protein 1103 7.83 ---

C3ORF20 chromosome 3 open reading frame

20 N/A 950 4.50 -1.09

CCDC150 coiled-coil domain containing 150 N/A 1379 -3.89 5.18

COBLL1 COBL-like 1 used for prediction of cancer therapy outcome;

exact function unknown 108 1.63 1.47

LOC100290370 hypothetical protein LOC100290370 N/A 1751 -5.37 -15.94

TMLHE trimethyllysine hydroxylase, epsilon First step in carnitin biosynthesis; carnitin is

important for the transport of activated fatty acids across the inner mitochondiral membrane

913 234.48 ---

TTC7B tetratricopeptide repeat domain 7B no literature information 1037, 1038 -1.68 -1.77

PDHA1 (includes EG:5160)

pyruvate dehydrogenase (lipoamide) alpha 1

mitochondrial complex; links glycolysis and TCA cycle

993 -1.25 -2.22

994, 1786 average

-1.22 -4.28

SLC4A7 solute carrier family 4, sodium

bicarbonate cotransporter, member 7

associated with tumor development/breast cancer development, involved in chronic

metabolic acidosis, pH regulation

351 1.30 3.69

352, 353 1.96 7.49

OSBPL11 oxysterol binding protein-like 11 intracellular lipid receptor, potentially involved

in cholesterol and glucose metabolsim 523 2.41 3.72

KIAA0415 KIAA0415 putative helicase 1172 -1.11 2.96

TBC1D10B TBC1 domain family, member 10B other family members are invovled in cancerogenesis and insulin signaling

1734, 1735, 1736

-1.45 -2.51

OSBPL8 oxysterol binding protein-like 8

regulated by runx2 transcription factor; intracellular lipid receptor, other members of

the family of ORPs involved in lipid metabolism and cell signaling

619 -1.37 -1.69

KIAA1522 KIAA1522 N/A 14, 15 -1.38 -2.71

C9ORF25 chromosome 9 open reading frame

25 N/A 812, 1609 -1.12 -1.81

Candidate proteins in this category are involved in diverse cellular and metabolic processes or have

unknow functions. The potential link of these proteins to processes triggered by β-ionone treatment

did not become clear by what is know so far about these proteins. However, they may play important

roles in the entire process from receptor binding to cellular processes since they predominantly

exhibit very high regulation factors. Despite their unknown role - or indeed because of it - these

proteins might provide eminently interesting candidate proteins for follow-up studies.

Discussion

140

8.3.3 Recapitulation of phospho-protein regulation in β-ionone-

stimulated LNCaP cells

The aim of this work was to unravel the signaling network triggered by the PSGR receptor upon

stimulation with β-ionone. Numerous phosphorylation sites in a total of 73 proteins were found to be

up- or down-regulated after β-ionone treatment. However, a complete PSGR signaling network could

not be constructed from the respective phospho-proteins. On the one hand, it is very likely that more

than 73 proteins are involved in the transduction of PSGR-mediated signaling. Therefore, probably

only a fraction of the entire signaling network was monitored in this work. On the other hand,

interpretation of the phospho-proteins and, in particular, interpretation of the functional relevance

of regulated phosphorylation sites is extremely difficult, since, in many cases, only little is known or

no information are available in the literature and databases. In addition, several new

phosphorylation sites have been identified in this work, which present new candidate sites, which

have to be further investigated in follow-up studies.

With the information available however, a working hypothesis for the PSGR mediated signaling could

be proposed as depicted in Figure 40. Firstly, the PSGR receptor might potentially be associated with

the GPR126 and might be internalized upon β-ionone treatment, as the abundance of the GPR126 in

the cytosolic fractions more than doubles upon 2 min of β-ionone treatment. Secondly, ignaling of

the activated PSGR or PSGR/GPR126 complex triggeres signaling processes at the plasma membrane,

which lead to reduced Akt and PI3K activity and subsequently, by so far unknown intermediate

proteins, to the inhibition of translation and to a decrease in proliferation and migration in general.

Discussion

141

Fig. 41: Proposed β-ionone triggered PSGR signaling events. Upon β-ionone stimulation, the cytosolic

abundance of the GPR126 increases by 2.6-fold leading to the suggestion, that this receptor may be

internalized, probably in a complex with the PSGR. Signaling of the activated PSGR subsequently leads to the

decreased GRB10 phosphorylation, which is perhaps due to a reduced Akt activity. In addition, phosphorylation

of MME was found to be significantly upregulated after 2 min of stimulation. This might lead to a prolonged

activation of PTEN and/or the binding of Lyn and both subunits of PI3K. Thereby, the activity of PI3K is

decreased as well. These signaling events subsequently are transduced via phosphorylation or

dephosphorylation events to proteins, which are in volved in migration and proliferation including protein

biosynthesis and potentially inhibiting these processes.

Only few phosphorylation sites detected in this work have been functionally characterized, but the

majority has been identified in large-scale studies previously. Although the mere identification of

phosphorylation sites in these studies does not contribute to the assembly of a functional PSGR

network. A hint regarding the cellular processes these proteins or phosphorylation sites are involved

in can be deduced from the foci of the cited large-scale studies. Most frequently cited studies

describe the analysis mitotic phosphorylation (Dephoure et al. 2008), ATM- and ATR-mediated

phosphorylation upon DNA damage (Matsuoka et al. 2007) and signaling (Olsen et al. 2006).

Nevertheless, the findings of this work provide some evidence that stimulation of the PSGR results in

processes associated with proliferation and growth. In addition results obtained in this work indicate,

that activation of the PSGR has an impact on migratory processes. Some findings indicate that

proliferation as well as migration might be inhibited through β-ionone treatment. Neuhaus et al.

recently showed that activation of the PSGR inhibits proliferation of prostate cancer cells (Neuhaus et

X

X

Discussion

142

al. 2009). The β-ionone study conducted in this work provides a list of candidate proteins, possibly

mediating this anti-proliferative effect of PSGR activation.

As to address the effect of PSGR stimulation with β-ionone on the migratory behavior of LNCaP cells,

a Boyden chamber assay was performed. This assay revealed, that β-ionone neither acts as attractive

nor a repellent chemotactic cue. However, β-ionone stimulation of LNCaP cells significantly inhibited

serum-directed migration.

In summary, this study provides a range of candidate proteins, by which these effects on migratory

and proliferative processes may be mediated. Furthermore, the inhibition of proliferation and

migration of prostate cancer cells is of high interest, since the PSGR receptor and proteins, found to

be regulated in this study, may be promising targets for anti-cancer drug development.

Conclusion

143

9 Conclusion

Initial approaches to study phosphorylation mediated signaling of olfactory receptors in the murine

OE by a gel-based phosphoproteomics approach, could not be established due to irreproducible

staining results using the ProQ®Diamond stain, which is the only available phospho-specific stain to

date. In addition, problems with the reproducible phosphoprotein sample preparation from treated

olfactory epithelia (OE) of mice emerged. However, this sample preparation could not be optimized,

as no suitable method was available to control the level of phosphorylation in preparted samples.

Therefore, a phosphoproteomics methodology should be established, which allows for the

reproducible and differential analysis of a great number of phosphorylated proteins. For that

purpose a gel-free phosphoproteomics approach was established, refined and tested in a global

phosphoproteomics study of lysates of orthovanadate treated LNCaP cells. This study demonstrated,

that the established workflow is indeed efficient and, in respect to the number of identified

phosphorylation sites, comparable to recently published studies. Overall, 2095 phosphopeptides

from 726 different proteins have been identified in two replicate analyses of 200 µg of peptides. In

this study, two different phosphopeptide analysis platforms were compared, using the LTQ-Orbitrap

XL instrument with MSA fragmentation and subsequent analysis via the MaxQaunt algorithm and the

HCTUltra PTM with CID and NL-triggered ETD and the bioinformatic analysis via ProteinScape and the

SLoMo algorithm. The approach using the LTQ-Orbitrap with subsequent MaxQuant analysis

provided better results both in the number of identified phosphopeptides and in the number of

confidently assigned phosphorylation sites. Still, both approaches identified a highly complementary

set of phosphopeptides, thereby contributing equally to the identification of new phosphorylation

sites. In addition, the analysis resulted in the identification of 164 hitherto unknown phosphorylation

sites. This global phosphoproteomics analysis resembles the most comprehensive

phosphoproteomics study in LNCaP cells so far.

For the analysis of PSGR mediated signaling, LNCaP cells expressing the PSGR were treated with the

known PSGR ligand β-ionone. The previously established phosphoproteomics workflow using the

LTQ-Orbitrap analysis platform was combined with SILAC labeling for the differential and time

resolved phosphoproteomics analysis of β-ionone stimulated LNCaP cells. This resulted in a total of

1154 identified phosphopeptides of which 99 phosphopeptides from 73 proteins were differentially

up- or downregulated upon β-ionone treatment. Almost nothing is known about the actual function

of the phosphorylation sites, which were identified to be regulated by β-ionone treatment. Many of

the respective proteins however, are involved in proliferation and migration processes. Whether the

observed changes in the phosphorylation level lead to the activation or deactivation of the particular

proteins however, remains elusive. However, the little information available on the proteins suggest

Conclusion

144

that proliferation as well as migration might be decreases. Indeed it was previously demonstrated,

that β-ionone has an antiproliferatory effect, which is mediated by the PSGR (Neuhaus et al. 2009). In

addition, a migration assay was performed, in which β-ionone appeard to inhibit serum directed

migration. In summary, the 73 phosphoproteins, which were identified to be differentially regulated

by β-ionone treatment provide new candidate proteins which may mediate PSGR signaling. In

addition, the results suggest that β-ionone stimulation of the PSGR receptor may have

antiproliferatory and antimigratory effects, which naturally is highly relevant in in the context of

prostate cancer. To confirm the actual role of PSGR signaling and the functions of the involved

proteins as well as the relevance of the identified phosphorylation sites, extensive further studies are

needed.

Outlook

145

10 Outlook

In this work, the virtually unknown PSGR signaling network was analyzed. The results present the

basis for the future elucidation of PSGR function in the prostate and its relevance for prostate cancer.

However, to intimately characterize PSGR mediated signaling, many more of its aspects have to be

investigated. For instance, almost all of the proteins found to be differentially phosphorylated upon

β-ionone stimulation, are known to be phosphorylated on tyrosine residues. Nevertheless, these

sites have not been identified via the workflow employed in this work. This was due to the

methodological approach, which is inherently biased towards serine and threonine phosphorylation.

Therefore, it a phosphotyrosine specific proteomics approach would provide subsidiary insights into

PSGR mediated signaling. For that purpose, immunoprecipitation with phosphotyrosine specific

antibodys could be performed prior to MS-analysis. In addition, the continuative analysis of

differentially regulated serine and threonine phosphoryation sites already identified in the study,

would add to the knowledge about the PSGR mediated signaling. For instance, the phosphorylation

level of phosphopeptides from candidate proteins could be monitored for more time points including

very short and longer treatment. For that purpose, a targeted and sensitive MS-based screening

approach termed multiple reaction monitoring (MRM) could be employed. By this method, selected

peptides can be quantified in multiple samples, thereby providing an opportunity for the rapid

quantification of peptides from candidate proteins in samples treated with β-ionone for multiple

time points. Furthermore, the candidate protein list contained proportionally many membrane

proteins, indicating strong participation of processes located at the plasma membrane. Thus, it might

contribute to the elucidation of PSGR mediated signaling to enrich for plasma membranes and to

specifically analyze phosphorylation events occurring in close proximity to the PSGR. In addition to

the verification of β-ionone triggered signaling, androstenone derivatives, which were also

demonstrated to be PSGR ligands (Neuhaus et al. 2009), could be used for stimulation experiments.

The resulting effects on the phosphorylation level could be analyzed and compared to the effects of

β-ionone treatment. As androstenone derivatives are more likely to be the physiological ligands of

the PSGR in prostate, it would be interesting, whether the effects observed by β-ionone and

androstenone derivative stimulation would be the same.

Beyond the additional proteomics approaches, complementary biological experiments have to be

performed to confirm the relevance of the identified candidate proteins and the respective

phosphorylation sites in mediating β-ionone induced signaling. For example, the three candidate

proteins, which change in protein abundance rather than in the actual phosphorylation level, could

be analyzed by immunofluorescence staining to analyze, whether the observed effect is due to a

change in subcellular location. One of these three proteins is the only G-protein coupled receptor

Outlook

146

identified in this work. For this protein it might be additionally of interest, to test for potential

colocalization with the PSGR. The relevance for other candidate proteins concerning the discussed

processes migration and proliferation could be further studied by knock-down experiments or

overexpression for the respective proteins. Beyond that, the actual function of the involved

phosphorylation sites has to be determined. For that purpose, the respective amino acid could be

mutated to analyze the effects of hindered phosphorylation at that point. In summary, the study

presented here provides a number of candidate proteins, which are likely to be involved in the

transduction of the observed antimigratory and antiproliferatorive effects and which now have to be

further examined.

Finally, an efficient gel-free phosphoproteomics approach was established successfully in this work.

Therefore, an application of this workflow to the analysis of phosphorylation events triggered by

odorant binding to olfactory receptors in the murine olfactory epithelium might be possible in the

future.

Literature

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Supplementary Tables

167

12 Supplementary Tables

Due to their length and limited use in a printed format, all supplementary tables can be found on the

accompanying CD

Publications

168

13 Publications

Characterization of a Peptide that Specifically Blocks the Ras Binding Domain of p75

Egert S, Piechura H, Hambruch N, Feigel M, Blöchl A.

International Journal of Peptide Research and Therapeutics. 2007; 13 (3):413-421.

Towards multidimensional liquid chromatography separation of proteins using fluorescence and

isotope-coded protein labelling for quantitative proteomics.

Tribl F, Lohaus C, Dombert T, Langenfeld E, Piechura H, Warscheid B, Meyer HE, Marcus K.

Proteomics. 2008 Mar;8(6):1204-11.

New insight into stimulus-induced plasticity of the olfactory epithelium in Mus musculus by

quantitative proteomics.

Barbour J, Neuhaus EM, Piechura H, Stoepel N, Mashukova A, Brunert D, Sitek B, Stühler K, Meyer

HE, Hatt H, Warscheid B.

Journal of Proteome Research. 2008 Apr;7(4):1594-605.

Members of the E2D (UbcH5) family mediate the ubiquitination of the conserved cysteine of

Pex5p, the peroxisomal import receptor.

Grou CP, Carvalho AF, Pinto MP, Wiese S, Piechura H, Meyer HE, Warscheid B, Sá-Miranda C,

Azevedo JE.

Journal of Biological Chemistry. 2008 May 23;283(21):14190-7.

Coa3 and Cox14 are essential for negative feedback regulation of COX1 translation in

mitochondria.

Mick DU, Vukotic M, Piechura H, Meyer HE, Warscheid B, Deckers M, Rehling P.

Journal of Cell Biology. 2010 Oct 4;191(1):141-54.

A salivary serine protease of the haematophagous reduviid Panstrongylus megistus: sequence

characterization, expression pattern and characterization of proteolytic activity.

Meiser CK, Piechura H, Meyer HE, Warscheid B, Schaub GA, Balczun C.

Insect Molecular Biology. 2010 Mar 24.

Kazal-type inhibitors in the stomach of Panstrongylus megistus (Triatominae, Reduviidae).

Meiser CK, Piechura H, Werner T, Dittmeyer-Schäfer S, Meyer HE, Warscheid B, Schaub GA, Balczun

C.

Insect Biochemistry and Molecular Biology. 2010 Apr;40(4):345-53.

Curriculum Vitae

169

Curriculum Vitae

Persönliches

Name: Heike Piechura

Anschrift: Schulstraße 34

79235 Vogtsburg-Oberbergen

[email protected]

Familienstand: Ledig

Promotion

Oktober 2010 Abgabe der Dissertationsschrift

seit 1. Juni 2005 Promotion am Medizinischen Proteom-Center unter Betreuung durch

Frau Prof. Dr. Bettina Warscheid und Prof. Dr. Dr. Hanns Hatt zum

Thema „Differential phosphoproteomics of olfactory receptor

mediated signaling“

Studium

Mai 2005 Erlangung des Akademischen Grades „Diplom-Chemiker“

November 2004 -

Mai 2005 Anfertigung der Diplomarbeit am Lehrstuhl für molekulare

Neurobiochemie unter Betreuung von PD Dr. Andrea Blöchl mit dem

Thema: „Intrazelluläre Inhibition der p75-Signaltransduktion“

Oktober 2003 -

März 2004 Auslandssemester an der University of Sussex, Brighton, UK und

Praktikum in der Arbeitsgruppe von Mark Paget am Lehrstuhl

Biochemistry and Biomedical Science

Oktober 2000 -

Mai 2005 Studium der Chemie an der Ruhr-Universität Bochum

Schullaufbahn

Mai 2000 Erlangung der Allgemeinen Hochschulreife

August 1990-

21. Mai 1999 Besuch der Schiller-Schule in Bochum

Curriculum Vitae

170

September 1989

Jui 1991 Besuch der Gemeinschaftsgrundschule Brenschede in Bochum

September 1987

Jui 1989 Besuch der Grundschule Osterwald

Erklärung

171

Erklärung

Hiermit erkläre ich, dass ich die Arbeit selbstständig verfasst und bei keiner anderen Fakultät

eingereicht und dass ich keine anderen als die angegebenen Hilfsmittel verwendet habe. Es handelt

sich bei der heute von mir eingereichten Dissertation um sechs in Wort und Bild völlig

übereinstimmende Exemplare.

Weiterhin erkläre ich, dass digitale Abbildungen nur die originalen Daten enthalten und in keinem

Fall inhaltsverändernde Bildbearbeitung vorgenommen wurde.

Bochum, den 15. Oktober 2010

________________________________

Dipl.-Chem. Heike Piechura

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