Investigation of protein expression dynamics in human tumor cells following pharmacological treatment [Elektronische Ressource] = Untersuchungen zur Proteinexpressionsdynamik in humanen Tumorzellen nach pharmakologischer Behandlung / Sven Nahnsen

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• Naturwissenschaften

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• Publié par: eberhard_karls_universitat_tubingen
• Publié le: 01 janvier 2010
• Nombre de lectures: 20
• Langue: English
• Poids de l'ouvrage: 35 Mo

Extrait

Investigation of protein expression dynamics in
human tumor cells following pharmacological
treatment
Insights from wet and dry lab approaches
Dissertation
der Mathematisch-Naturwissenschaftlichen Fakult at
der Eberhard Karls Universit at Tubingen
zur Erlangung des Grades eines
Doktors der Naturwissenschaften
(Dr. rer. nat.)
vorgelegt von
Dipl.-Biotech. Sven Nahnsen
aus Heilbronn-Neckargartach
Tubingen
2010Tag der mundlic hen Quali kation: 20/12/2010
Dekan: Prof. Dr. Wolfgang Rosenstiel
1. Berichterstatter: Prof. Dr.-Ing. Oliver Kohlbacher
2. Berich Prof. Dr. Alfred NordheimThis thesis is dedicated to the memory of Andreas Bertsch, my dear
friend, whom I thank for a great time together. Andreas
taught me C++ and instilled endless enthusiasm for
the eld of computational proteomics.Acknowledgements
I would like to express my deepest gratitude to my advisors Oliver
Kohlbacher and Alfred Nordheim. They gave me the opportunity to
undertake this interdisciplinary PhD project. They encouraged and
supported me at any time during my graduate studies; their open-
mindedness gave me the con dence to pursue this direction.
I am also very grateful to Boris Macek for sharing his enormous knowl-
edge in mass spectrometry and the great scienti c support.
Furthermore, I would like to thank all my colleagues at the Proteome
Center, namely Alejandro Carpy, Irina Droste-Borel, Ulrike Gram-
mig, Mirita Franz, Karsten Krug, Johannes Madlung, Raphael Otto,
Wolfgang Schutz, Nicole Sessler and Silke Wahl, as well as the for-
mer PCT members Michael Beller, Inga Buchen, Claudia Fladerer,
Stephan Jung and Stuart Penguelly, for the inspiring atmosphere at
the PCT.
Likewise I am very thankful for a great time to all my colleagues at
the Center for Bioinformatics, namely Sebastian Briesemeister, Mag-
dalena Feldhahn, Nina Fischer, Sandra Gesing, Erhan Kenar, Andreas
K amper, Peter Niermann, Lars Nilse, Marc R ottig, Timo Sachsen-
berg, Marcel Schumann, Nora Toussaint and Mathias Walzer, as well
as former members Thorsten Blum, Nico Pfeifer and Marc Sturm.Many thanks go Birgit Schittek and the whole Division of Derma-
tooncology for a fruitful collaboration and for providing the melanoma
cells.
The scienti c discussions and close interactions alike with all my col-
leagues made my time in Tubingen very interesting.
I am thankful to David Wojnar for his work on OpenMS and especially
for his contributions to the posterior error probability implementation.
Furthermore I would like to thank Seong-Hwan Rho for the philo-
sophical discussions on systems biology, as well as for his help on the
analysis of the DIGE data.
Finally, I would like to wholeheartedly thank my parents and my
whole family, who patiently followed my scienti c endeavor.
Most importantly I would like to thank Katharina, for her endless
patience and for her support during all the years I worked towards
this thesis. Thank you for making every day so colorful.In accordance with the standard scienti c protocol, I will use the
personal pronoun we to indicate the reader and the writer, or my
scienti c collaborators and myself.Abstract
Cancer, the multifactorial disease, resulting in uncontrolled growth of malignant
cells, is the second most frequent cause of death worldwide. Despite enor-
mous growth in knowledge on cancer pathology, e cient medication still re-
mains elusive. In recent years, global pro ling approaches are increasingly impor-
tant tools to study complex biological problems, such as cancer. One emerging
pro ling technology is proteomics, the continuously growing research branch of
(bio)analytical chemistry that studies the entire set of proteins in a biological sys-
tem, their modi cations and interactions. However, a variety of computational
and technological challenges in proteomics are still limiting the broad application
of the technology in cancer research.
This thesis contributes in three major topics to new methodological approaches
for the analysis of proteomics data and to novel insights of the e ects of thera-
peutical treatment in cancer cells. In the rst research part, a new method to
analyze 2D-Polyacrylamid Gel Electrophoresis (PAGE) proteomics data is intro-
duced. Although the DIGE (Di erence Gel Eletrophoresis) technology greatly
in uenced the quality of 2D-PAGE experiments through the uorescent labeling
of di erent samples and their common separation in the same 2D gel, the tech-
nology is still accompanied with major challenges. In this thesis we provide a
solution to one of the major problems, the accurate and automated mapping of
protein spots from di erent DIGE gels. We implemented a novel scoring method
and applied a graph-theoretical approach to solve the assignment problem and
to ultimately nd the protein spots with reproducible regulation on di erent gels.
v0. ABSTRACT
The second research section presents a new method for the integration of sev-
eral database search engines for improved peptide identi cation. Database search
for peptide identi cation belongs to the cornerstones in the processing of shotgun
proteomics data. The underlying algorithms from di erent search engines pro-
duce results that overlap in parts and disagree in others. Here we present a new
computational framework that combines results from several search algorithms
and thereby shows signi cant gain in peptide identi cation rates. Our method
relies on the normalization of single engine scores and on a weighted, average-like
method to combine the identi cation results from di erent engines to a common
consensus score. This new approach to peptide identi cation yields up to 63%
more identi cations as the single engines alone.
In the last research section we present the application of quantitative shotgun
proteomics to an important aspect of cancer research, the study of the in uence
of kinase inhibitors to the global protein expression. Dynamic quantitation of
protein expression after kinase inhibitor treatment using SILAC (Stable Isotope
Labeling by Amino Acids in Cell culture) opened new insights to the quantitative
and dynamic e ects of the two multi-kinase inhibitors, sorafenib and LY294002,
on the whole proteome. In these experiments, we were able to identify and quan-
tify more than 5,400 proteins and to investigate the protein expression levels at
ve di erent time points, revealing unprecedented insights to the kinetic behavior
of the proteome as a function of length of treatment. We could show that for
both inhibitors several clusters of proteins show similar regulation following in-
hibitor treatment. We con rm the known regulation of the mTor pathway by the
LY294003 inhibitor and we speculate about the in uence of LY294002 to DNA
replication. Furthermore, the investigations on the kinetic e ects of sorafenib
treatment revealed known mechanisms, such as the in uence to the Rho and Ras
mediated cell cycle progression, but opened also new and interesting hypothesis,
such as sorafenib’s contribution to autophagy induction. Large scale proteomics
datasets provide a wealth of information and new ways to study biological systems
on a system-wide level.
viZusammenfassung
Krebs, die multifaktorielle Krankheit bei der sich pathologisch ver anderte Zellen
unkontrolliert teilen, ist weltweit die zweith au gste Todesursache. Trotz des enor-
men Zuwachses an Wissen ub er die Entstehung von Krebs, bleiben e ziente
Therapiemethoden bislang aus. Globale Pro lierungsmethoden haben sich als
sehr vielversprechende Ans atze fur die Untersuchung von komplexen biologis-
chen Problemen, wie Krebs, erwiesen. Eine dieser neuen Methoden ist die Pro-
teomik, der stetig wachsenden Zweig der (bio)analytischen Chemie, welcher die
Gesamtheit der Proteine eines biologischen Systems, sowie ihre Modi kationen
und Interaktionen erforscht. Eine Vielzahl von bioinformatischen und technolo-
gischen Herausforderungen in der Proteomik verhindern jedoch immer noch den
breiten Einsatz dieser Technologie in der Krebsforschung. Im Rahmen dieser
Dissertation tragen wir zu drei wichtigen Themengebiete der Proteomik und
ihrer Anwendung in der Krebsforschung bei. Wir entwickelten neue methodische
Ans atze fur die Analyse von proteomischen Daten und wendeten proteomische
Methoden an, um ein besseres Verst andnis zum Mechanismus von therapeutis-
chen Substanzen in Tumorzellen zu gewinnen.
In dem ersten Teil der Forschungsarbeiten stellen wir eine neue Methode fur die
Analyse von 2D Gel basierten Daten vor. Obwohl die DIGE Technologie durch die
Floureszenzmarkierung von verschiedenen Proben und deren gemeinsame Tren-
nung auf einem Gel, einen erheblichen Beitrag zur Verbesserung der Qualit at
von 2D Gel Experimenten gemacht hat, gibt es nach wie vor noch erhebliche
Herausforderungen in der DIGE basierten Proteomanalytik. Diese Dissertation
pr asentiert eine neue L osung fur eines der gr o ten Probleme der DIGE basierten
vii0. ZUSAMMENFASSUNG
Proteomik, der akkurate und automatisierte Abgleich von Proteinspots auf ver-
schiedenen DIGE Gelen. Die Implementierung einer neuen Scoring-Methode
und die Anwendung von graph-theoretischen Ans atzen zur L osung des Zuord-
nungsproblems erlauben das schnelle Finden von Proteinspots, welche auf ver-
schiedenen Gelen reproduzierbar reguliert sind.
Das zweite Kapitel der Forschungsarbei