Standard Promotion Practices versus Up-or-Out Contracts (CRI 2009-001)

In most firms a worker in any period is either promoted, left in the same job, or fired (demotions are typically rare), and there is no specific date by which a promotion needs to occur. In other employment situations, however, up-or-out contracts are common, i.e., if a worker is not promoted by a certain date the worker must leave the firm. This paper develops a theory that explains why and when each of these practices is employed. Our theory is based on asymmetric learning in labor markets and incentives associated with the prospect of future promotion. Our main result is that firms employ up-or-out contracts when firm-specific human capital is low while they employ standard promotion practices when it is high. We also find that, if firms can commit to a wage floor for promoted workers and effort provision is important, then up-or-out contracts are employed when low-level and high-level jobs are similar. We believe these results are of interest because they are consistent with many of the settings in which up-or-out is typically observed such as law firms and academic institutions

Crosstalk and the Dynamical Modularity of Feed-Forward Loops in Transcriptional Regulatory Networks

Network motifs, such as the feed-forward loop (FFL), introduce a range of complex behaviors to transcriptional regulatory networks, yet such properties are typically determined from their isolated study. We characterize the effects of crosstalk on FFL dynamics by modeling the cross regulation between two different FFLs and evaluate the extent to which these patterns occur in vivo. Analytical modeling suggests that crosstalk should overwhelmingly affect individual protein-expression dynamics. Counter to this expectation we find that entire FFLs are more likely than expected to resist the effects of crosstalk (approximate to 20% for one crosstalk interaction) and remain dynamically modular. The likelihood that cross-linked FFLs are dynamically correlated increases monotonically with additional crosstalk, but is independent of the specific regulation type or connectivity of the interactions. Just one additional regulatory interaction is sufficient to drive the FFL dynamics to a statistically different state. Despite the potential for modularity between sparsely connected network motifs, Escherichia coli (E. coli) appears to favor crosstalk wherein at least one of the cross-linked FFLs remains modular. A gene ontology analysis reveals that stress response processes are significantly overrepresented in the cross-linked motifs found within E. coli. Although the daunting complexity of biological networks affects the dynamical properties of individual network motifs, some resist and remain modular, seemingly insulated from extrinsic perturbations-an intriguing possibility for nature to consistently and reliably provide certain network functionalities wherever the need arise

Advancing immunopeptidomics: validation of the method, improved epitope prediction, peptide-based HLA typing and discrimination of healthy and malignant tissue

Seit fast 30 Jahren wird das Immunpeptidom durch Elution von Peptiden aus HLA-Molekülen analysiert. Weltweit nutzen mittlerweile mehrere Institute und Unternehmen diese Methode für ein breites Spektrum an Untersuchungen, die von der simplen Identifizierung von HLA-Peptidmotiven für verschiedene Organismen bis hin zum Nachweis kryptischer krankheitsspezifischer Peptide reichen. Die Immunpeptidomik ist populärer denn je, seit sich die Medikamentenentwicklung in den letzten Jahren auf die positive Modulation des Immunsystems fokussiert hat. Die Zulassung der ersten Checkpoint-Antikörper leitete die Ära der Immuntherapie ein und spezifische Immuntherapien mit weniger Nebenwirkungen stehen nun im Blickpunkt. Das Anwendungsspektrum der Immunpeptidomik ist mittlerweile breit gefächert, dennoch enthält das Immunpeptidom immer noch eine große Fülle von Informationen, die darauf warten, entschlüsselt zu werden. Aktuell ist die Immunpeptidomik darin eingeschränkt, dass die große Anzahl von Peptiden, mit unterschiedlichen Affinitäten und Stabilitäten der Peptid-HLA-Komplexe, nicht optimal erfasst werden kann und daher unter anderem nur begrenzte Wiederfindungsraten möglich sind. Zu Beginn dieser Doktorarbeit gab es ungelöste Fragestellungen auf dem Gebiet der Immunpeptidomik, die in dieser Arbeit untersucht werden sollten: Ist es möglich, die Immunpeptidomik zu validieren und diese zuverlässig für klinische Studien und die Medikamentenentwicklung einzusetzen? Gibt es heute eine zuverlässige Methode zur Identifizierung von Peptidmotiven für Peptid-präsentierende MHC-Klasse-I-Allotypen, dem Grundstein für Epitopvorhersagen und Wirkstoffidentifizierungen? Ist es möglich, Peptide zur Klassifizierung von HLA-Allotypen oder zur Unterscheidung zwischen gesundem und bösartigem Gewebe zu verwenden? Können tumorspezifische Peptide mit dieser Omik-Technologie zuverlässig charakterisiert werden? In dieser Doktorarbeit wurde die immunpeptidomische Methode validiert, um die Zuverlässigkeit der LC-MS/MS-Peptid-Identifizierung zu gewährleisten, und es wurden alle erforderlichen Parameter der Europäischen Arzneimittel-Agentur und U. S. Food and Drug Administration untersucht. Darüber hinaus wurde ein aktualisiertes Protokoll für die Identifizierung von MHC-Liganden, die Entschlüsselung von Peptidmotiven und die Generierung von Matrizen für die Epitopvorhersage erstellt, das sowohl für monoallele Zellen als auch für multiallele Gewebe verwendet werden kann. Schließlich wurde eine Methode entwickelt, um allotypische Peptide zu identifizieren, die eine HLA-Typisierung ermöglichen. Diese Peptide können auch als interner Standard für die semi-quantitative Untersuchung der Tumorspezifität von Peptiden verwendet werden. Diese Methode wurde erfolgreich implementiert, um gewebe- und dignitätsspezifische Muster im Immunpeptidom zu identifizieren und die Dignität von immunpeptidomischen Proben zu bestimmen.For almost 30 years now, the immunopeptidome has been analyzed by eluting peptides from HLA molecules. This method has already been established in several institutes and companies worldwide and is now used for a wide range of investigations from the simple identification of HLA peptide motifs for different organisms to the detection of cryptic disease-specific peptides. The field of immunopeptidomics is more popular than ever as drug development has focused on the positive modulation of the immune system in recent years. Since the approval of the first checkpoint antibodies, the era of immunotherapy has been running and specific immunotherapies with fewer side effects are in the focus. There is a wide range of applications, yet, the immunopeptidome still contains a great wealth of information waiting to be deciphered. Currently, immunopeptidomics is limited in the identification of the large number of peptides with different affinities and stabilities of the peptide-HLA complexes. Therefore, amongst many other factors, only limited recovery rates are possible. When this doctoral thesis started, there were several unresolved questions in the field of immunopeptidomics that should be approached in this thesis: Is it possible to validate immunopeptidomics and use it reliably for clinical studies and drug development? Is there nowadays a reliable method to identify the peptide motif for peptide presenting MHC class I allotypes, the cornerstone for epitope predictions or active substance identification? Is it possible to use peptides to classify HLA allotypes or differentiate between healthy and malignant tissue? Can tumor-specific peptides be reliably characterized with this omic technology? In this doctoral thesis the immunopeptidomic method was validated to ensure the reliability of LC-MS/MS peptide identification and all required parameters of the European Medicines Agency (EMA) and Food and Drug Administration (FDA) were investigated. In addition, an updated protocol for the identification of MHC ligands, deconvolution of peptide motifs and generation of matrices for epitope prediction was established, which can be used for monoallelic cells as well as multiallelic tissue. Finally, a method was developed to identify allotypic peptides that allow HLA typing. These peptides can also be used as an internal standard for semi-quantitative investigation of the tumor specificity of peptides. The developed method was also successfully implemented to identify tissue and dignity specific patterns in the immunopeptidome and to determine the dignity of immunopeptidomic samples

A predictive computational model reveals that GIV/girdin serves as a tunable valve for EGFR-stimulated cyclic AMP signals.

Cellular levels of the versatile second messenger cyclic (c)AMP are regulated by the antagonistic actions of the canonical G protein → adenylyl cyclase pathway that is initiated by G-protein-coupled receptors (GPCRs) and attenuated by phosphodiesterases (PDEs). Dysregulated cAMP signaling drives many diseases; for example, its low levels facilitate numerous sinister properties of cancer cells. Recently, an alternative paradigm for cAMP signaling has emerged in which growth factor-receptor tyrosine kinases (RTKs; e.g., EGFR) access and modulate G proteins via a cytosolic guanine-nucleotide exchange modulator (GEM), GIV/girdin; dysregulation of this pathway is frequently encountered in cancers. In this study, we present a network-based compartmental model for the paradigm of GEM-facilitated cross-talk between RTKs and G proteins and how that impacts cellular cAMP. Our model predicts that cross-talk between GIV, Gαs, and Gαi proteins dampens ligand-stimulated cAMP dynamics. This prediction was experimentally verified by measuring cAMP levels in cells under different conditions. We further predict that the direct proportionality of cAMP concentration as a function of receptor number and the inverse proportionality of cAMP concentration as a function of PDE concentration are both altered by GIV levels. Taking these results together, our model reveals that GIV acts as a tunable control valve that regulates cAMP flux after growth factor stimulation. For a given stimulus, when GIV levels are high, cAMP levels are low, and vice versa. In doing so, GIV modulates cAMP via mechanisms distinct from the two most often targeted classes of cAMP modulators, GPCRs and PDEs