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Dr. rer. nat. Michal Or-Guil
HU-FIS-Profil ↗Ziel der Forschungsgruppe ist es, ein genaueres Bild von den Mechanismen zu gewinnen, die die Zellen des adaptiven Immunsystems veranlassen, Antigene zu erkennen. Das Hauptaugenmerk gilt dabei einerseits den Mechanismen der B-Zell-Selektion, die anhand von Experimenten in Keimzentren und von mathematischen Modellen zu untersuchen sind, und andererseits der Bestimmung von Merkmalen der Gesamtheit der Antikörper, die mit Hilfe von Simulationen und Datenanalyse von Protein-Antikörper-Bindungen durchgeführt werden.
SYSTHER is a virtual institute consisting of partners from Germany and Slovenia. It was established to investigate and improve solid tumor treatment based on systems biology approaches. Identification of target genes and proteins, and subsequent mathematical modelling of candidate pathways, intracellular networks and interaction dynamics of immunocompetence, tumor and stem cells aim at elucidating mechanisms of tumor immunomodulation and the role of tumor stem cells in solid, non-metastasizing tumors, improving thus therapeutic strategies. Serum antibody profiling and gene expression analysis of tumor and stem cells shall further lead to the understanding of immunomodulation mechanisms.
Mammals possess immune systems to resist disease. In order to identify a foreign organism, such systems exploit the protein binding activity of antibodies. In dependence on the invader, highly specialized antibodies are secreted by suitable B-cells which are selected out of a large cell pool and induced to expand their population. The project aims at answering questions like: What are the mechanisms governing the selection of most suitable cells, and how are the selected cells improved during the course of an acute immune response? Furthermore, what recognition properties must a set of antibodies possess to be able to locate any invading pathogen? The project will pursue an interdisciplinary ansatz combining three subprojects in order to approach those questions. On the one hand, the dynamic behavior of B-cells in the tissues where selection occurs will be investigated experimentally and by means of computer simulations and data analysis. On the other hand, features of immune cell population dynamics shall be determined on the basis of mathematical models, utilizing the concept of an abstract space representing recognition capabilities of antibodies, the so called shape space. Finally, a shape space shall be designed from experimental data with the aim of characterizing and predicting protein binding properties.
EBioMedicine · DOI
Background: BK virus (BKV), Cytomegalovirus (CMV) and Epstein-Barr virus (EBV) reactivations are common after kidney transplantation and associated with increased morbidity and mortality. Although CMV might be a risk factor for BKV and EBV, the effects of combined reactivations remain unknown. The purpose of this study is to ascertain the interaction and effects on graft function of these reactivations. Methods: 3715 serum samples from 540 kidney transplant recipients were analysed for viral load by qPCR. Measurements were performed throughout eight visits during the first post-transplantation year. Clinical characteristics, including graft function (GFR), were collected in parallel. Findings: BKV had the highest prevalence and viral loads. BKV or CMV viral loads over 10,000 copiesmL -1 led to significant GFR impairment. 57 patients had BKV-CMV combined reactivation, both reactivations were significantly associated (p = 0.005). Combined reactivation was associated with a significant GFR reduction one year post-transplantation of 11.7 mLmin -1 1.73 m -2 (p = 0.02) at relatively low thresholds (BKV > 1000 and CMV > 4000 copiesmL -1 ). For EBV, a significant association was found with CMV reactivation (p = 0.02), but no GFR reduction was found. Long cold ischaemia times were a further risk factor for high CMV load. Interpretation: BKV-CMV combined reactivation has a deep impact on renal function one year posttransplantation and therefore most likely on long-term allograft function, even at low viral loads. Frequent viral monitoring and subsequent interventions for low BKV and/or CMV viraemia levels and/or long cold ischaemia time are recommended.
PLoS Computational Biology · DOI
A fundamental property of cell populations is their growth rate as well as the time needed for cell division and its variance. The eukaryotic cell cycle progresses in an ordered sequence through the phases G1, S, G2, and M, and is regulated by environmental cues and by intracellular checkpoints. Reflecting this regulatory complexity, the length of each phase varies considerably in different kinds of cells but also among genetically and morphologically indistinguishable cells. This article addresses the question of how to describe and quantify the mean and variance of the cell cycle phase lengths. A phase-resolved cell cycle model is introduced assuming that phase completion times are distributed as delayed exponential functions, capturing the observations that each realization of a cycle phase is variable in length and requires a minimal time. In this model, the total cell cycle length is distributed as a delayed hypoexponential function that closely reproduces empirical distributions. Analytic solutions are derived for the proportions of cells in each cycle phase in a population growing under balanced growth and under specific non-stationary conditions. These solutions are then adapted to describe conventional cell cycle kinetic assays based on pulse labelling with nucleoside analogs. The model fits well to data obtained with two distinct proliferating cell lines labelled with a single bromodeoxiuridine pulse. However, whereas mean lengths are precisely estimated for all phases, the respective variances remain uncertain. To overcome this limitation, a redesigned experimental protocol is derived and validated in silico. The novelty is the timing of two consecutive pulses with distinct nucleosides that enables accurate and precise estimation of both the mean and the variance of the length of all phases. The proposed methodology to quantify the phase length distributions gives results potentially equivalent to those obtained with modern phase-specific biosensor-based fluorescent imaging.
Analytical Biochemistry · DOI